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TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Convert International System of Units (SI) and Binary prefixes
"""
from enum import Enum
from typing import Union
class SI_Unit(Enum):
yotta = 24
zetta = 21
exa = 18
peta = 15
tera = 12
giga = 9
mega = 6
kilo = 3
hecto = 2
deca = 1
deci = -1
centi = -2
milli = -3
micro = -6
nano = -9
pico = -12
femto = -15
atto = -18
zepto = -21
yocto = -24
class Binary_Unit(Enum):
yotta = 8
zetta = 7
exa = 6
peta = 5
tera = 4
giga = 3
mega = 2
kilo = 1
def convert_si_prefix(
known_amount: float,
known_prefix: Union[str, SI_Unit],
unknown_prefix: Union[str, SI_Unit],
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Binary_prefix
Wikipedia reference: https://en.wikipedia.org/wiki/International_System_of_Units
>>> convert_si_prefix(1, SI_Unit.giga, SI_Unit.mega)
1000
>>> convert_si_prefix(1, SI_Unit.mega, SI_Unit.giga)
0.001
>>> convert_si_prefix(1, SI_Unit.kilo, SI_Unit.kilo)
1
>>> convert_si_prefix(1, 'giga', 'mega')
1000
>>> convert_si_prefix(1, 'gIGa', 'mEGa')
1000
"""
if isinstance(known_prefix, str):
known_prefix = SI_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = SI_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
10 ** (known_prefix.value - unknown_prefix.value)
)
return unknown_amount
def convert_binary_prefix(
known_amount: float,
known_prefix: Union[str, Binary_Unit],
unknown_prefix: Union[str, Binary_Unit],
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Metric_prefix
>>> convert_binary_prefix(1, Binary_Unit.giga, Binary_Unit.mega)
1024
>>> convert_binary_prefix(1, Binary_Unit.mega, Binary_Unit.giga)
0.0009765625
>>> convert_binary_prefix(1, Binary_Unit.kilo, Binary_Unit.kilo)
1
>>> convert_binary_prefix(1, 'giga', 'mega')
1024
>>> convert_binary_prefix(1, 'gIGa', 'mEGa')
1024
"""
if isinstance(known_prefix, str):
known_prefix = Binary_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = Binary_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
2 ** ((known_prefix.value - unknown_prefix.value) * 10)
)
return unknown_amount
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Convert International System of Units (SI) and Binary prefixes
"""
from enum import Enum
from typing import Union
class SI_Unit(Enum):
yotta = 24
zetta = 21
exa = 18
peta = 15
tera = 12
giga = 9
mega = 6
kilo = 3
hecto = 2
deca = 1
deci = -1
centi = -2
milli = -3
micro = -6
nano = -9
pico = -12
femto = -15
atto = -18
zepto = -21
yocto = -24
class Binary_Unit(Enum):
yotta = 8
zetta = 7
exa = 6
peta = 5
tera = 4
giga = 3
mega = 2
kilo = 1
def convert_si_prefix(
known_amount: float,
known_prefix: Union[str, SI_Unit],
unknown_prefix: Union[str, SI_Unit],
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Binary_prefix
Wikipedia reference: https://en.wikipedia.org/wiki/International_System_of_Units
>>> convert_si_prefix(1, SI_Unit.giga, SI_Unit.mega)
1000
>>> convert_si_prefix(1, SI_Unit.mega, SI_Unit.giga)
0.001
>>> convert_si_prefix(1, SI_Unit.kilo, SI_Unit.kilo)
1
>>> convert_si_prefix(1, 'giga', 'mega')
1000
>>> convert_si_prefix(1, 'gIGa', 'mEGa')
1000
"""
if isinstance(known_prefix, str):
known_prefix = SI_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = SI_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
10 ** (known_prefix.value - unknown_prefix.value)
)
return unknown_amount
def convert_binary_prefix(
known_amount: float,
known_prefix: Union[str, Binary_Unit],
unknown_prefix: Union[str, Binary_Unit],
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Metric_prefix
>>> convert_binary_prefix(1, Binary_Unit.giga, Binary_Unit.mega)
1024
>>> convert_binary_prefix(1, Binary_Unit.mega, Binary_Unit.giga)
0.0009765625
>>> convert_binary_prefix(1, Binary_Unit.kilo, Binary_Unit.kilo)
1
>>> convert_binary_prefix(1, 'giga', 'mega')
1024
>>> convert_binary_prefix(1, 'gIGa', 'mEGa')
1024
"""
if isinstance(known_prefix, str):
known_prefix = Binary_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = Binary_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
2 ** ((known_prefix.value - unknown_prefix.value) * 10)
)
return unknown_amount
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Python code to demonstrate working of
# extend(), extendleft(), rotate(), reverse()
# importing "collections" for deque operations
import collections
# initializing deque
de = collections.deque([1, 2, 3])
# using extend() to add numbers to right end
# adds 4,5,6 to right end
de.extend([4, 5, 6])
# printing modified deque
print("The deque after extending deque at end is : ")
print(de)
# using extendleft() to add numbers to left end
# adds 7,8,9 to right end
de.extendleft([7, 8, 9])
# printing modified deque
print("The deque after extending deque at beginning is : ")
print(de)
# using rotate() to rotate the deque
# rotates by 3 to left
de.rotate(-3)
# printing modified deque
print("The deque after rotating deque is : ")
print(de)
# using reverse() to reverse the deque
de.reverse()
# printing modified deque
print("The deque after reversing deque is : ")
print(de)
# get right-end value and eliminate
startValue = de.pop()
print("The deque after popping value at end is : ")
print(de)
# get left-end value and eliminate
endValue = de.popleft()
print("The deque after popping value at start is : ")
print(de)
# eliminate element searched by value
de.remove(5)
print("The deque after eliminating element searched by value : ")
print(de)
| # Python code to demonstrate working of
# extend(), extendleft(), rotate(), reverse()
# importing "collections" for deque operations
import collections
# initializing deque
de = collections.deque([1, 2, 3])
# using extend() to add numbers to right end
# adds 4,5,6 to right end
de.extend([4, 5, 6])
# printing modified deque
print("The deque after extending deque at end is : ")
print(de)
# using extendleft() to add numbers to left end
# adds 7,8,9 to right end
de.extendleft([7, 8, 9])
# printing modified deque
print("The deque after extending deque at beginning is : ")
print(de)
# using rotate() to rotate the deque
# rotates by 3 to left
de.rotate(-3)
# printing modified deque
print("The deque after rotating deque is : ")
print(de)
# using reverse() to reverse the deque
de.reverse()
# printing modified deque
print("The deque after reversing deque is : ")
print(de)
# get right-end value and eliminate
startValue = de.pop()
print("The deque after popping value at end is : ")
print(de)
# get left-end value and eliminate
endValue = de.popleft()
print("The deque after popping value at start is : ")
print(de)
# eliminate element searched by value
de.remove(5)
print("The deque after eliminating element searched by value : ")
print(de)
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Build a simple bare-minimum quantum circuit that starts with a single
qubit (by default, in state 0) and inverts it. Run the experiment 1000
times and print the total count of the states finally observed.
Qiskit Docs: https://qiskit.org/documentation/getting_started.html
"""
import qiskit as q
def single_qubit_measure(qubits: int, classical_bits: int) -> q.result.counts.Counts:
"""
>>> single_qubit_measure(2, 2)
{'11': 1000}
>>> single_qubit_measure(4, 4)
{'0011': 1000}
"""
# Use Aer's qasm_simulator
simulator = q.Aer.get_backend("qasm_simulator")
# Create a Quantum Circuit acting on the q register
circuit = q.QuantumCircuit(qubits, classical_bits)
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0)
circuit.x(1)
# Map the quantum measurement to the classical bits
circuit.measure([0, 1], [0, 1])
# Execute the circuit on the qasm simulator
job = q.execute(circuit, simulator, shots=1000)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(circuit)
if __name__ == "__main__":
counts = single_qubit_measure(2, 2)
print(f"Total count for various states are: {counts}")
| #!/usr/bin/env python3
"""
Build a simple bare-minimum quantum circuit that starts with a single
qubit (by default, in state 0) and inverts it. Run the experiment 1000
times and print the total count of the states finally observed.
Qiskit Docs: https://qiskit.org/documentation/getting_started.html
"""
import qiskit as q
def single_qubit_measure(qubits: int, classical_bits: int) -> q.result.counts.Counts:
"""
>>> single_qubit_measure(2, 2)
{'11': 1000}
>>> single_qubit_measure(4, 4)
{'0011': 1000}
"""
# Use Aer's qasm_simulator
simulator = q.Aer.get_backend("qasm_simulator")
# Create a Quantum Circuit acting on the q register
circuit = q.QuantumCircuit(qubits, classical_bits)
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0)
circuit.x(1)
# Map the quantum measurement to the classical bits
circuit.measure([0, 1], [0, 1])
# Execute the circuit on the qasm simulator
job = q.execute(circuit, simulator, shots=1000)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(circuit)
if __name__ == "__main__":
counts = single_qubit_measure(2, 2)
print(f"Total count for various states are: {counts}")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Sum of digits sequence
Problem 551
Let a(0), a(1),... be an integer sequence defined by:
a(0) = 1
for n >= 1, a(n) is the sum of the digits of all preceding terms
The sequence starts with 1, 1, 2, 4, 8, ...
You are given a(10^6) = 31054319.
Find a(10^15)
"""
ks = [k for k in range(2, 20 + 1)]
base = [10 ** k for k in range(ks[-1] + 1)]
memo = {}
def next_term(a_i, k, i, n):
"""
Calculates and updates a_i in-place to either the n-th term or the
smallest term for which c > 10^k when the terms are written in the form:
a(i) = b * 10^k + c
For any a(i), if digitsum(b) and c have the same value, the difference
between subsequent terms will be the same until c >= 10^k. This difference
is cached to greatly speed up the computation.
Arguments:
a_i -- array of digits starting from the one's place that represent
the i-th term in the sequence
k -- k when terms are written in the from a(i) = b*10^k + c.
Term are calulcated until c > 10^k or the n-th term is reached.
i -- position along the sequence
n -- term to calculate up to if k is large enough
Return: a tuple of difference between ending term and starting term, and
the number of terms calculated. ex. if starting term is a_0=1, and
ending term is a_10=62, then (61, 9) is returned.
"""
# ds_b - digitsum(b)
ds_b = sum(a_i[j] for j in range(k, len(a_i)))
c = sum(a_i[j] * base[j] for j in range(min(len(a_i), k)))
diff, dn = 0, 0
max_dn = n - i
sub_memo = memo.get(ds_b)
if sub_memo is not None:
jumps = sub_memo.get(c)
if jumps is not None and len(jumps) > 0:
# find and make the largest jump without going over
max_jump = -1
for _k in range(len(jumps) - 1, -1, -1):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
max_jump = _k
break
if max_jump >= 0:
diff, dn, _kk = jumps[max_jump]
# since the difference between jumps is cached, add c
new_c = diff + c
for j in range(min(k, len(a_i))):
new_c, a_i[j] = divmod(new_c, 10)
if new_c > 0:
add(a_i, k, new_c)
else:
sub_memo[c] = []
else:
sub_memo = {c: []}
memo[ds_b] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_diff, terms_jumped = next_term(a_i, k - 1, i + dn, n)
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_diff, terms_jumped = compute(a_i, k, i + dn, n)
diff += _diff
dn += terms_jumped
jumps = sub_memo[c]
# keep jumps sorted by # of terms skipped
j = 0
while j < len(jumps):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(j, (diff, dn, k))
return (diff, dn)
def compute(a_i, k, i, n):
"""
same as next_term(a_i, k, i, n) but computes terms without memoizing results.
"""
if i >= n:
return 0, i
if k > len(a_i):
a_i.extend([0 for _ in range(k - len(a_i))])
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
start_i = i
ds_b, ds_c, diff = 0, 0, 0
for j in range(len(a_i)):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
addend = ds_c + ds_b
diff += addend
ds_c = 0
for j in range(k):
s = a_i[j] + addend
addend, a_i[j] = divmod(s, 10)
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(a_i, k, addend)
return diff, i - start_i
def add(digits, k, addend):
"""
adds addend to digit array given in digits
starting at index k
"""
for j in range(k, len(digits)):
s = digits[j] + addend
if s >= 10:
quotient, digits[j] = divmod(s, 10)
addend = addend // 10 + quotient
else:
digits[j] = s
addend = addend // 10
if addend == 0:
break
while addend > 0:
addend, digit = divmod(addend, 10)
digits.append(digit)
def solution(n: int = 10 ** 15) -> int:
"""
returns n-th term of sequence
>>> solution(10)
62
>>> solution(10**6)
31054319
>>> solution(10**15)
73597483551591773
"""
digits = [1]
i = 1
dn = 0
while True:
diff, terms_jumped = next_term(digits, 20, i + dn, n)
dn += terms_jumped
if dn == n - i:
break
a_n = 0
for j in range(len(digits)):
a_n += digits[j] * 10 ** j
return a_n
if __name__ == "__main__":
print(f"{solution() = }")
| """
Sum of digits sequence
Problem 551
Let a(0), a(1),... be an integer sequence defined by:
a(0) = 1
for n >= 1, a(n) is the sum of the digits of all preceding terms
The sequence starts with 1, 1, 2, 4, 8, ...
You are given a(10^6) = 31054319.
Find a(10^15)
"""
ks = [k for k in range(2, 20 + 1)]
base = [10 ** k for k in range(ks[-1] + 1)]
memo = {}
def next_term(a_i, k, i, n):
"""
Calculates and updates a_i in-place to either the n-th term or the
smallest term for which c > 10^k when the terms are written in the form:
a(i) = b * 10^k + c
For any a(i), if digitsum(b) and c have the same value, the difference
between subsequent terms will be the same until c >= 10^k. This difference
is cached to greatly speed up the computation.
Arguments:
a_i -- array of digits starting from the one's place that represent
the i-th term in the sequence
k -- k when terms are written in the from a(i) = b*10^k + c.
Term are calulcated until c > 10^k or the n-th term is reached.
i -- position along the sequence
n -- term to calculate up to if k is large enough
Return: a tuple of difference between ending term and starting term, and
the number of terms calculated. ex. if starting term is a_0=1, and
ending term is a_10=62, then (61, 9) is returned.
"""
# ds_b - digitsum(b)
ds_b = sum(a_i[j] for j in range(k, len(a_i)))
c = sum(a_i[j] * base[j] for j in range(min(len(a_i), k)))
diff, dn = 0, 0
max_dn = n - i
sub_memo = memo.get(ds_b)
if sub_memo is not None:
jumps = sub_memo.get(c)
if jumps is not None and len(jumps) > 0:
# find and make the largest jump without going over
max_jump = -1
for _k in range(len(jumps) - 1, -1, -1):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
max_jump = _k
break
if max_jump >= 0:
diff, dn, _kk = jumps[max_jump]
# since the difference between jumps is cached, add c
new_c = diff + c
for j in range(min(k, len(a_i))):
new_c, a_i[j] = divmod(new_c, 10)
if new_c > 0:
add(a_i, k, new_c)
else:
sub_memo[c] = []
else:
sub_memo = {c: []}
memo[ds_b] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_diff, terms_jumped = next_term(a_i, k - 1, i + dn, n)
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_diff, terms_jumped = compute(a_i, k, i + dn, n)
diff += _diff
dn += terms_jumped
jumps = sub_memo[c]
# keep jumps sorted by # of terms skipped
j = 0
while j < len(jumps):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(j, (diff, dn, k))
return (diff, dn)
def compute(a_i, k, i, n):
"""
same as next_term(a_i, k, i, n) but computes terms without memoizing results.
"""
if i >= n:
return 0, i
if k > len(a_i):
a_i.extend([0 for _ in range(k - len(a_i))])
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
start_i = i
ds_b, ds_c, diff = 0, 0, 0
for j in range(len(a_i)):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
addend = ds_c + ds_b
diff += addend
ds_c = 0
for j in range(k):
s = a_i[j] + addend
addend, a_i[j] = divmod(s, 10)
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(a_i, k, addend)
return diff, i - start_i
def add(digits, k, addend):
"""
adds addend to digit array given in digits
starting at index k
"""
for j in range(k, len(digits)):
s = digits[j] + addend
if s >= 10:
quotient, digits[j] = divmod(s, 10)
addend = addend // 10 + quotient
else:
digits[j] = s
addend = addend // 10
if addend == 0:
break
while addend > 0:
addend, digit = divmod(addend, 10)
digits.append(digit)
def solution(n: int = 10 ** 15) -> int:
"""
returns n-th term of sequence
>>> solution(10)
62
>>> solution(10**6)
31054319
>>> solution(10**15)
73597483551591773
"""
digits = [1]
i = 1
dn = 0
while True:
diff, terms_jumped = next_term(digits, 20, i + dn, n)
dn += terms_jumped
if dn == n - i:
break
a_n = 0
for j in range(len(digits)):
a_n += digits[j] * 10 ** j
return a_n
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Tree_traversal
class Node:
"""
A Node has data variable and pointers to its left and right nodes.
"""
def __init__(self, data):
self.left = None
self.right = None
self.data = data
def make_tree() -> Node:
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
return root
def preorder(root: Node):
"""
Pre-order traversal visits root node, left subtree, right subtree.
>>> preorder(make_tree())
[1, 2, 4, 5, 3]
"""
return [root.data] + preorder(root.left) + preorder(root.right) if root else []
def postorder(root: Node):
"""
Post-order traversal visits left subtree, right subtree, root node.
>>> postorder(make_tree())
[4, 5, 2, 3, 1]
"""
return postorder(root.left) + postorder(root.right) + [root.data] if root else []
def inorder(root: Node):
"""
In-order traversal visits left subtree, root node, right subtree.
>>> inorder(make_tree())
[4, 2, 5, 1, 3]
"""
return inorder(root.left) + [root.data] + inorder(root.right) if root else []
def height(root: Node):
"""
Recursive function for calculating the height of the binary tree.
>>> height(None)
0
>>> height(make_tree())
3
"""
return (max(height(root.left), height(root.right)) + 1) if root else 0
def level_order_1(root: Node):
"""
Print whole binary tree in Level Order Traverse.
Level Order traverse: Visit nodes of the tree level-by-level.
"""
if not root:
return
temp = root
que = [temp]
while len(que) > 0:
print(que[0].data, end=" ")
temp = que.pop(0)
if temp.left:
que.append(temp.left)
if temp.right:
que.append(temp.right)
return que
def level_order_2(root: Node, level: int):
"""
Level-wise traversal: Print all nodes present at the given level of the binary tree
"""
if not root:
return root
if level == 1:
print(root.data, end=" ")
elif level > 1:
level_order_2(root.left, level - 1)
level_order_2(root.right, level - 1)
def print_left_to_right(root: Node, level: int):
"""
Print elements on particular level from left to right direction of the binary tree.
"""
if not root:
return
if level == 1:
print(root.data, end=" ")
elif level > 1:
print_left_to_right(root.left, level - 1)
print_left_to_right(root.right, level - 1)
def print_right_to_left(root: Node, level: int):
"""
Print elements on particular level from right to left direction of the binary tree.
"""
if not root:
return
if level == 1:
print(root.data, end=" ")
elif level > 1:
print_right_to_left(root.right, level - 1)
print_right_to_left(root.left, level - 1)
def zigzag(root: Node):
"""
ZigZag traverse: Print node left to right and right to left, alternatively.
"""
flag = 0
height_tree = height(root)
for h in range(1, height_tree + 1):
if flag == 0:
print_left_to_right(root, h)
flag = 1
else:
print_right_to_left(root, h)
flag = 0
def main(): # Main function for testing.
"""
Create binary tree.
"""
root = make_tree()
"""
All Traversals of the binary are as follows:
"""
print(f" In-order Traversal is {inorder(root)}")
print(f" Pre-order Traversal is {preorder(root)}")
print(f"Post-order Traversal is {postorder(root)}")
print(f"Height of Tree is {height(root)}")
print("Complete Level Order Traversal is : ")
level_order_1(root)
print("\nLevel-wise order Traversal is : ")
for h in range(1, height(root) + 1):
level_order_2(root, h)
print("\nZigZag order Traversal is : ")
zigzag(root)
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| # https://en.wikipedia.org/wiki/Tree_traversal
class Node:
"""
A Node has data variable and pointers to its left and right nodes.
"""
def __init__(self, data):
self.left = None
self.right = None
self.data = data
def make_tree() -> Node:
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
return root
def preorder(root: Node):
"""
Pre-order traversal visits root node, left subtree, right subtree.
>>> preorder(make_tree())
[1, 2, 4, 5, 3]
"""
return [root.data] + preorder(root.left) + preorder(root.right) if root else []
def postorder(root: Node):
"""
Post-order traversal visits left subtree, right subtree, root node.
>>> postorder(make_tree())
[4, 5, 2, 3, 1]
"""
return postorder(root.left) + postorder(root.right) + [root.data] if root else []
def inorder(root: Node):
"""
In-order traversal visits left subtree, root node, right subtree.
>>> inorder(make_tree())
[4, 2, 5, 1, 3]
"""
return inorder(root.left) + [root.data] + inorder(root.right) if root else []
def height(root: Node):
"""
Recursive function for calculating the height of the binary tree.
>>> height(None)
0
>>> height(make_tree())
3
"""
return (max(height(root.left), height(root.right)) + 1) if root else 0
def level_order_1(root: Node):
"""
Print whole binary tree in Level Order Traverse.
Level Order traverse: Visit nodes of the tree level-by-level.
"""
if not root:
return
temp = root
que = [temp]
while len(que) > 0:
print(que[0].data, end=" ")
temp = que.pop(0)
if temp.left:
que.append(temp.left)
if temp.right:
que.append(temp.right)
return que
def level_order_2(root: Node, level: int):
"""
Level-wise traversal: Print all nodes present at the given level of the binary tree
"""
if not root:
return root
if level == 1:
print(root.data, end=" ")
elif level > 1:
level_order_2(root.left, level - 1)
level_order_2(root.right, level - 1)
def print_left_to_right(root: Node, level: int):
"""
Print elements on particular level from left to right direction of the binary tree.
"""
if not root:
return
if level == 1:
print(root.data, end=" ")
elif level > 1:
print_left_to_right(root.left, level - 1)
print_left_to_right(root.right, level - 1)
def print_right_to_left(root: Node, level: int):
"""
Print elements on particular level from right to left direction of the binary tree.
"""
if not root:
return
if level == 1:
print(root.data, end=" ")
elif level > 1:
print_right_to_left(root.right, level - 1)
print_right_to_left(root.left, level - 1)
def zigzag(root: Node):
"""
ZigZag traverse: Print node left to right and right to left, alternatively.
"""
flag = 0
height_tree = height(root)
for h in range(1, height_tree + 1):
if flag == 0:
print_left_to_right(root, h)
flag = 1
else:
print_right_to_left(root, h)
flag = 0
def main(): # Main function for testing.
"""
Create binary tree.
"""
root = make_tree()
"""
All Traversals of the binary are as follows:
"""
print(f" In-order Traversal is {inorder(root)}")
print(f" Pre-order Traversal is {preorder(root)}")
print(f"Post-order Traversal is {postorder(root)}")
print(f"Height of Tree is {height(root)}")
print("Complete Level Order Traversal is : ")
level_order_1(root)
print("\nLevel-wise order Traversal is : ")
for h in range(1, height(root) + 1):
level_order_2(root, h)
print("\nZigZag order Traversal is : ")
zigzag(root)
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ A Stack using a linked list like structure """
from typing import Any
class Node:
def __init__(self, data):
self.data = data
self.next = None
def __str__(self):
return f"{self.data}"
class LinkedStack:
"""
Linked List Stack implementing push (to top),
pop (from top) and is_empty
>>> stack = LinkedStack()
>>> stack.is_empty()
True
>>> stack.push(5)
>>> stack.push(9)
>>> stack.push('python')
>>> stack.is_empty()
False
>>> stack.pop()
'python'
>>> stack.push('algorithms')
>>> stack.pop()
'algorithms'
>>> stack.pop()
9
>>> stack.pop()
5
>>> stack.is_empty()
True
>>> stack.pop()
Traceback (most recent call last):
...
IndexError: pop from empty stack
"""
def __init__(self) -> None:
self.top = None
def __iter__(self):
node = self.top
while node:
yield node.data
node = node.next
def __str__(self):
"""
>>> stack = LinkedStack()
>>> stack.push("c")
>>> stack.push("b")
>>> stack.push("a")
>>> str(stack)
'a->b->c'
"""
return "->".join([str(item) for item in self])
def __len__(self):
"""
>>> stack = LinkedStack()
>>> len(stack) == 0
True
>>> stack.push("c")
>>> stack.push("b")
>>> stack.push("a")
>>> len(stack) == 3
True
"""
return len(tuple(iter(self)))
def is_empty(self) -> bool:
"""
>>> stack = LinkedStack()
>>> stack.is_empty()
True
>>> stack.push(1)
>>> stack.is_empty()
False
"""
return self.top is None
def push(self, item: Any) -> None:
"""
>>> stack = LinkedStack()
>>> stack.push("Python")
>>> stack.push("Java")
>>> stack.push("C")
>>> str(stack)
'C->Java->Python'
"""
node = Node(item)
if not self.is_empty():
node.next = self.top
self.top = node
def pop(self) -> Any:
"""
>>> stack = LinkedStack()
>>> stack.pop()
Traceback (most recent call last):
...
IndexError: pop from empty stack
>>> stack.push("c")
>>> stack.push("b")
>>> stack.push("a")
>>> stack.pop() == 'a'
True
>>> stack.pop() == 'b'
True
>>> stack.pop() == 'c'
True
"""
if self.is_empty():
raise IndexError("pop from empty stack")
assert isinstance(self.top, Node)
pop_node = self.top
self.top = self.top.next
return pop_node.data
def peek(self) -> Any:
"""
>>> stack = LinkedStack()
>>> stack.push("Java")
>>> stack.push("C")
>>> stack.push("Python")
>>> stack.peek()
'Python'
"""
if self.is_empty():
raise IndexError("peek from empty stack")
return self.top.data
def clear(self) -> None:
"""
>>> stack = LinkedStack()
>>> stack.push("Java")
>>> stack.push("C")
>>> stack.push("Python")
>>> str(stack)
'Python->C->Java'
>>> stack.clear()
>>> len(stack) == 0
True
"""
self.top = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| """ A Stack using a linked list like structure """
from typing import Any
class Node:
def __init__(self, data):
self.data = data
self.next = None
def __str__(self):
return f"{self.data}"
class LinkedStack:
"""
Linked List Stack implementing push (to top),
pop (from top) and is_empty
>>> stack = LinkedStack()
>>> stack.is_empty()
True
>>> stack.push(5)
>>> stack.push(9)
>>> stack.push('python')
>>> stack.is_empty()
False
>>> stack.pop()
'python'
>>> stack.push('algorithms')
>>> stack.pop()
'algorithms'
>>> stack.pop()
9
>>> stack.pop()
5
>>> stack.is_empty()
True
>>> stack.pop()
Traceback (most recent call last):
...
IndexError: pop from empty stack
"""
def __init__(self) -> None:
self.top = None
def __iter__(self):
node = self.top
while node:
yield node.data
node = node.next
def __str__(self):
"""
>>> stack = LinkedStack()
>>> stack.push("c")
>>> stack.push("b")
>>> stack.push("a")
>>> str(stack)
'a->b->c'
"""
return "->".join([str(item) for item in self])
def __len__(self):
"""
>>> stack = LinkedStack()
>>> len(stack) == 0
True
>>> stack.push("c")
>>> stack.push("b")
>>> stack.push("a")
>>> len(stack) == 3
True
"""
return len(tuple(iter(self)))
def is_empty(self) -> bool:
"""
>>> stack = LinkedStack()
>>> stack.is_empty()
True
>>> stack.push(1)
>>> stack.is_empty()
False
"""
return self.top is None
def push(self, item: Any) -> None:
"""
>>> stack = LinkedStack()
>>> stack.push("Python")
>>> stack.push("Java")
>>> stack.push("C")
>>> str(stack)
'C->Java->Python'
"""
node = Node(item)
if not self.is_empty():
node.next = self.top
self.top = node
def pop(self) -> Any:
"""
>>> stack = LinkedStack()
>>> stack.pop()
Traceback (most recent call last):
...
IndexError: pop from empty stack
>>> stack.push("c")
>>> stack.push("b")
>>> stack.push("a")
>>> stack.pop() == 'a'
True
>>> stack.pop() == 'b'
True
>>> stack.pop() == 'c'
True
"""
if self.is_empty():
raise IndexError("pop from empty stack")
assert isinstance(self.top, Node)
pop_node = self.top
self.top = self.top.next
return pop_node.data
def peek(self) -> Any:
"""
>>> stack = LinkedStack()
>>> stack.push("Java")
>>> stack.push("C")
>>> stack.push("Python")
>>> stack.peek()
'Python'
"""
if self.is_empty():
raise IndexError("peek from empty stack")
return self.top.data
def clear(self) -> None:
"""
>>> stack = LinkedStack()
>>> stack.push("Java")
>>> stack.push("C")
>>> stack.push("Python")
>>> str(stack)
'Python->C->Java'
>>> stack.clear()
>>> len(stack) == 0
True
"""
self.top = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 4: https://projecteuler.net/problem=4
Largest palindrome product
A palindromic number reads the same both ways. The largest palindrome made
from the product of two 2-digit numbers is 9009 = 91 × 99.
Find the largest palindrome made from the product of two 3-digit numbers.
References:
- https://en.wikipedia.org/wiki/Palindromic_number
"""
def solution(n: int = 998001) -> int:
"""
Returns the largest palindrome made from the product of two 3-digit
numbers which is less than n.
>>> solution(20000)
19591
>>> solution(30000)
29992
>>> solution(40000)
39893
>>> solution(10000)
Traceback (most recent call last):
...
ValueError: That number is larger than our acceptable range.
"""
# fetches the next number
for number in range(n - 1, 9999, -1):
str_number = str(number)
# checks whether 'str_number' is a palindrome.
if str_number == str_number[::-1]:
divisor = 999
# if 'number' is a product of two 3-digit numbers
# then number is the answer otherwise fetch next number.
while divisor != 99:
if (number % divisor == 0) and (len(str(number // divisor)) == 3.0):
return number
divisor -= 1
raise ValueError("That number is larger than our acceptable range.")
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 4: https://projecteuler.net/problem=4
Largest palindrome product
A palindromic number reads the same both ways. The largest palindrome made
from the product of two 2-digit numbers is 9009 = 91 × 99.
Find the largest palindrome made from the product of two 3-digit numbers.
References:
- https://en.wikipedia.org/wiki/Palindromic_number
"""
def solution(n: int = 998001) -> int:
"""
Returns the largest palindrome made from the product of two 3-digit
numbers which is less than n.
>>> solution(20000)
19591
>>> solution(30000)
29992
>>> solution(40000)
39893
>>> solution(10000)
Traceback (most recent call last):
...
ValueError: That number is larger than our acceptable range.
"""
# fetches the next number
for number in range(n - 1, 9999, -1):
str_number = str(number)
# checks whether 'str_number' is a palindrome.
if str_number == str_number[::-1]:
divisor = 999
# if 'number' is a product of two 3-digit numbers
# then number is the answer otherwise fetch next number.
while divisor != 99:
if (number % divisor == 0) and (len(str(number // divisor)) == 3.0):
return number
divisor -= 1
raise ValueError("That number is larger than our acceptable range.")
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Uses Pythagoras theorem to calculate the distance between two points in space."""
import math
class Point:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def __repr__(self) -> str:
return f"Point({self.x}, {self.y}, {self.z})"
def distance(a: Point, b: Point) -> float:
return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))
def test_distance() -> None:
"""
>>> point1 = Point(2, -1, 7)
>>> point2 = Point(1, -3, 5)
>>> print(f"Distance from {point1} to {point2} is {distance(point1, point2)}")
Distance from Point(2, -1, 7) to Point(1, -3, 5) is 3.0
"""
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| """Uses Pythagoras theorem to calculate the distance between two points in space."""
import math
class Point:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def __repr__(self) -> str:
return f"Point({self.x}, {self.y}, {self.z})"
def distance(a: Point, b: Point) -> float:
return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))
def test_distance() -> None:
"""
>>> point1 = Point(2, -1, 7)
>>> point2 = Point(1, -3, 5)
>>> print(f"Distance from {point1} to {point2} is {distance(point1, point2)}")
Distance from Point(2, -1, 7) to Point(1, -3, 5) is 3.0
"""
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Wiggle Sort.
Given an unsorted array nums, reorder it such
that nums[0] < nums[1] > nums[2] < nums[3]....
For example:
if input numbers = [3, 5, 2, 1, 6, 4]
one possible Wiggle Sorted answer is [3, 5, 1, 6, 2, 4].
"""
def wiggle_sort(nums: list) -> list:
"""
Python implementation of wiggle.
Example:
>>> wiggle_sort([0, 5, 3, 2, 2])
[0, 5, 2, 3, 2]
>>> wiggle_sort([])
[]
>>> wiggle_sort([-2, -5, -45])
[-45, -2, -5]
>>> wiggle_sort([-2.1, -5.68, -45.11])
[-45.11, -2.1, -5.68]
"""
for i, _ in enumerate(nums):
if (i % 2 == 1) == (nums[i - 1] > nums[i]):
nums[i - 1], nums[i] = nums[i], nums[i - 1]
return nums
if __name__ == "__main__":
print("Enter the array elements:")
array = list(map(int, input().split()))
print("The unsorted array is:")
print(array)
print("Array after Wiggle sort:")
print(wiggle_sort(array))
| """
Wiggle Sort.
Given an unsorted array nums, reorder it such
that nums[0] < nums[1] > nums[2] < nums[3]....
For example:
if input numbers = [3, 5, 2, 1, 6, 4]
one possible Wiggle Sorted answer is [3, 5, 1, 6, 2, 4].
"""
def wiggle_sort(nums: list) -> list:
"""
Python implementation of wiggle.
Example:
>>> wiggle_sort([0, 5, 3, 2, 2])
[0, 5, 2, 3, 2]
>>> wiggle_sort([])
[]
>>> wiggle_sort([-2, -5, -45])
[-45, -2, -5]
>>> wiggle_sort([-2.1, -5.68, -45.11])
[-45.11, -2.1, -5.68]
"""
for i, _ in enumerate(nums):
if (i % 2 == 1) == (nums[i - 1] > nums[i]):
nums[i - 1], nums[i] = nums[i], nums[i - 1]
return nums
if __name__ == "__main__":
print("Enter the array elements:")
array = list(map(int, input().split()))
print("The unsorted array is:")
print(array)
print("Array after Wiggle sort:")
print(wiggle_sort(array))
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import math
from typing import List, Union
class SegmentTree:
def __init__(self, size: int) -> None:
self.size = size
# approximate the overall size of segment tree with given value
self.segment_tree = [0 for i in range(0, 4 * size)]
# create array to store lazy update
self.lazy = [0 for i in range(0, 4 * size)]
self.flag = [0 for i in range(0, 4 * size)] # flag for lazy update
def left(self, idx: int) -> int:
"""
>>> segment_tree = SegmentTree(15)
>>> segment_tree.left(1)
2
>>> segment_tree.left(2)
4
>>> segment_tree.left(12)
24
"""
return idx * 2
def right(self, idx: int) -> int:
"""
>>> segment_tree = SegmentTree(15)
>>> segment_tree.right(1)
3
>>> segment_tree.right(2)
5
>>> segment_tree.right(12)
25
"""
return idx * 2 + 1
def build(
self, idx: int, left_element: int, right_element: int, A: List[int]
) -> None:
if left_element == right_element:
self.segment_tree[idx] = A[left_element - 1]
else:
mid = (left_element + right_element) // 2
self.build(self.left(idx), left_element, mid, A)
self.build(self.right(idx), mid + 1, right_element, A)
self.segment_tree[idx] = max(
self.segment_tree[self.left(idx)], self.segment_tree[self.right(idx)]
)
def update(
self, idx: int, left_element: int, right_element: int, a: int, b: int, val: int
) -> bool:
"""
update with O(lg n) (Normal segment tree without lazy update will take O(nlg n)
for each update)
update(1, 1, size, a, b, v) for update val v to [a,b]
"""
if self.flag[idx] is True:
self.segment_tree[idx] = self.lazy[idx]
self.flag[idx] = False
if left_element != right_element:
self.lazy[self.left(idx)] = self.lazy[idx]
self.lazy[self.right(idx)] = self.lazy[idx]
self.flag[self.left(idx)] = True
self.flag[self.right(idx)] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
self.segment_tree[idx] = val
if left_element != right_element:
self.lazy[self.left(idx)] = val
self.lazy[self.right(idx)] = val
self.flag[self.left(idx)] = True
self.flag[self.right(idx)] = True
return True
mid = (left_element + right_element) // 2
self.update(self.left(idx), left_element, mid, a, b, val)
self.update(self.right(idx), mid + 1, right_element, a, b, val)
self.segment_tree[idx] = max(
self.segment_tree[self.left(idx)], self.segment_tree[self.right(idx)]
)
return True
# query with O(lg n)
def query(
self, idx: int, left_element: int, right_element: int, a: int, b: int
) -> Union[int, float]:
"""
query(1, 1, size, a, b) for query max of [a,b]
>>> A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
>>> segment_tree = SegmentTree(15)
>>> segment_tree.build(1, 1, 15, A)
>>> segment_tree.query(1, 1, 15, 4, 6)
7
>>> segment_tree.query(1, 1, 15, 7, 11)
14
>>> segment_tree.query(1, 1, 15, 7, 12)
15
"""
if self.flag[idx] is True:
self.segment_tree[idx] = self.lazy[idx]
self.flag[idx] = False
if left_element != right_element:
self.lazy[self.left(idx)] = self.lazy[idx]
self.lazy[self.right(idx)] = self.lazy[idx]
self.flag[self.left(idx)] = True
self.flag[self.right(idx)] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
mid = (left_element + right_element) // 2
q1 = self.query(self.left(idx), left_element, mid, a, b)
q2 = self.query(self.right(idx), mid + 1, right_element, a, b)
return max(q1, q2)
def __str__(self) -> str:
return str([self.query(1, 1, self.size, i, i) for i in range(1, self.size + 1)])
if __name__ == "__main__":
A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
size = 15
segt = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt)
| from __future__ import annotations
import math
from typing import List, Union
class SegmentTree:
def __init__(self, size: int) -> None:
self.size = size
# approximate the overall size of segment tree with given value
self.segment_tree = [0 for i in range(0, 4 * size)]
# create array to store lazy update
self.lazy = [0 for i in range(0, 4 * size)]
self.flag = [0 for i in range(0, 4 * size)] # flag for lazy update
def left(self, idx: int) -> int:
"""
>>> segment_tree = SegmentTree(15)
>>> segment_tree.left(1)
2
>>> segment_tree.left(2)
4
>>> segment_tree.left(12)
24
"""
return idx * 2
def right(self, idx: int) -> int:
"""
>>> segment_tree = SegmentTree(15)
>>> segment_tree.right(1)
3
>>> segment_tree.right(2)
5
>>> segment_tree.right(12)
25
"""
return idx * 2 + 1
def build(
self, idx: int, left_element: int, right_element: int, A: List[int]
) -> None:
if left_element == right_element:
self.segment_tree[idx] = A[left_element - 1]
else:
mid = (left_element + right_element) // 2
self.build(self.left(idx), left_element, mid, A)
self.build(self.right(idx), mid + 1, right_element, A)
self.segment_tree[idx] = max(
self.segment_tree[self.left(idx)], self.segment_tree[self.right(idx)]
)
def update(
self, idx: int, left_element: int, right_element: int, a: int, b: int, val: int
) -> bool:
"""
update with O(lg n) (Normal segment tree without lazy update will take O(nlg n)
for each update)
update(1, 1, size, a, b, v) for update val v to [a,b]
"""
if self.flag[idx] is True:
self.segment_tree[idx] = self.lazy[idx]
self.flag[idx] = False
if left_element != right_element:
self.lazy[self.left(idx)] = self.lazy[idx]
self.lazy[self.right(idx)] = self.lazy[idx]
self.flag[self.left(idx)] = True
self.flag[self.right(idx)] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
self.segment_tree[idx] = val
if left_element != right_element:
self.lazy[self.left(idx)] = val
self.lazy[self.right(idx)] = val
self.flag[self.left(idx)] = True
self.flag[self.right(idx)] = True
return True
mid = (left_element + right_element) // 2
self.update(self.left(idx), left_element, mid, a, b, val)
self.update(self.right(idx), mid + 1, right_element, a, b, val)
self.segment_tree[idx] = max(
self.segment_tree[self.left(idx)], self.segment_tree[self.right(idx)]
)
return True
# query with O(lg n)
def query(
self, idx: int, left_element: int, right_element: int, a: int, b: int
) -> Union[int, float]:
"""
query(1, 1, size, a, b) for query max of [a,b]
>>> A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
>>> segment_tree = SegmentTree(15)
>>> segment_tree.build(1, 1, 15, A)
>>> segment_tree.query(1, 1, 15, 4, 6)
7
>>> segment_tree.query(1, 1, 15, 7, 11)
14
>>> segment_tree.query(1, 1, 15, 7, 12)
15
"""
if self.flag[idx] is True:
self.segment_tree[idx] = self.lazy[idx]
self.flag[idx] = False
if left_element != right_element:
self.lazy[self.left(idx)] = self.lazy[idx]
self.lazy[self.right(idx)] = self.lazy[idx]
self.flag[self.left(idx)] = True
self.flag[self.right(idx)] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
mid = (left_element + right_element) // 2
q1 = self.query(self.left(idx), left_element, mid, a, b)
q2 = self.query(self.right(idx), mid + 1, right_element, a, b)
return max(q1, q2)
def __str__(self) -> str:
return str([self.query(1, 1, self.size, i, i) for i in range(1, self.size + 1)])
if __name__ == "__main__":
A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
size = 15
segt = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt)
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # An OOP approach to representing and manipulating matrices
class Matrix:
"""
Matrix object generated from a 2D array where each element is an array representing
a row.
Rows can contain type int or float.
Common operations and information available.
>>> rows = [
... [1, 2, 3],
... [4, 5, 6],
... [7, 8, 9]
... ]
>>> matrix = Matrix(rows)
>>> print(matrix)
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]]
Matrix rows and columns are available as 2D arrays
>>> print(matrix.rows)
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> print(matrix.columns())
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]
Order is returned as a tuple
>>> matrix.order
(3, 3)
Squareness and invertability are represented as bool
>>> matrix.is_square
True
>>> matrix.is_invertable()
False
Identity, Minors, Cofactors and Adjugate are returned as Matrices. Inverse can be
a Matrix or Nonetype
>>> print(matrix.identity())
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
>>> print(matrix.minors())
[[-3. -6. -3.]
[-6. -12. -6.]
[-3. -6. -3.]]
>>> print(matrix.cofactors())
[[-3. 6. -3.]
[6. -12. 6.]
[-3. 6. -3.]]
>>> # won't be apparent due to the nature of the cofactor matrix
>>> print(matrix.adjugate())
[[-3. 6. -3.]
[6. -12. 6.]
[-3. 6. -3.]]
>>> print(matrix.inverse())
None
Determinant is an int, float, or Nonetype
>>> matrix.determinant()
0
Negation, scalar multiplication, addition, subtraction, multiplication and
exponentiation are available and all return a Matrix
>>> print(-matrix)
[[-1. -2. -3.]
[-4. -5. -6.]
[-7. -8. -9.]]
>>> matrix2 = matrix * 3
>>> print(matrix2)
[[3. 6. 9.]
[12. 15. 18.]
[21. 24. 27.]]
>>> print(matrix + matrix2)
[[4. 8. 12.]
[16. 20. 24.]
[28. 32. 36.]]
>>> print(matrix - matrix2)
[[-2. -4. -6.]
[-8. -10. -12.]
[-14. -16. -18.]]
>>> print(matrix ** 3)
[[468. 576. 684.]
[1062. 1305. 1548.]
[1656. 2034. 2412.]]
Matrices can also be modified
>>> matrix.add_row([10, 11, 12])
>>> print(matrix)
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]
[10. 11. 12.]]
>>> matrix2.add_column([8, 16, 32])
>>> print(matrix2)
[[3. 6. 9. 8.]
[12. 15. 18. 16.]
[21. 24. 27. 32.]]
>>> print(matrix * matrix2)
[[90. 108. 126. 136.]
[198. 243. 288. 304.]
[306. 378. 450. 472.]
[414. 513. 612. 640.]]
"""
def __init__(self, rows):
error = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same number of values, each of which must be of type "
"int or float."
)
if len(rows) != 0:
cols = len(rows[0])
if cols == 0:
raise error
for row in rows:
if len(row) != cols:
raise error
for value in row:
if not isinstance(value, (int, float)):
raise error
self.rows = rows
else:
self.rows = []
# MATRIX INFORMATION
def columns(self):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]
@property
def num_rows(self):
return len(self.rows)
@property
def num_columns(self):
return len(self.rows[0])
@property
def order(self):
return (self.num_rows, self.num_columns)
@property
def is_square(self):
return self.order[0] == self.order[1]
def identity(self):
values = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
for row_num in range(self.num_rows)
]
return Matrix(values)
def determinant(self):
if not self.is_square:
return None
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return self.rows[0][0]
if self.order == (2, 2):
return (self.rows[0][0] * self.rows[1][1]) - (
self.rows[0][1] * self.rows[1][0]
)
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns)
)
def is_invertable(self):
return bool(self.determinant())
def get_minor(self, row, column):
values = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns)
if other_column != column
]
for other_row in range(self.num_rows)
if other_row != row
]
return Matrix(values).determinant()
def get_cofactor(self, row, column):
if (row + column) % 2 == 0:
return self.get_minor(row, column)
return -1 * self.get_minor(row, column)
def minors(self):
return Matrix(
[
[self.get_minor(row, column) for column in range(self.num_columns)]
for row in range(self.num_rows)
]
)
def cofactors(self):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns)
]
for row in range(self.minors().num_rows)
]
)
def adjugate(self):
values = [
[self.cofactors().rows[column][row] for column in range(self.num_columns)]
for row in range(self.num_rows)
]
return Matrix(values)
def inverse(self):
determinant = self.determinant()
return None if not determinant else self.adjugate() * (1 / determinant)
def __repr__(self):
return str(self.rows)
def __str__(self):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(self.rows[0]) + "]]"
return (
"["
+ "\n ".join(
[
"[" + ". ".join([str(value) for value in row]) + ".]"
for row in self.rows
]
)
+ "]"
)
# MATRIX MANIPULATION
def add_row(self, row, position=None):
type_error = TypeError("Row must be a list containing all ints and/or floats")
if not isinstance(row, list):
raise type_error
for value in row:
if not isinstance(value, (int, float)):
raise type_error
if len(row) != self.num_columns:
raise ValueError(
"Row must be equal in length to the other rows in the matrix"
)
if position is None:
self.rows.append(row)
else:
self.rows = self.rows[0:position] + [row] + self.rows[position:]
def add_column(self, column, position=None):
type_error = TypeError(
"Column must be a list containing all ints and/or floats"
)
if not isinstance(column, list):
raise type_error
for value in column:
if not isinstance(value, (int, float)):
raise type_error
if len(column) != self.num_rows:
raise ValueError(
"Column must be equal in length to the other columns in the matrix"
)
if position is None:
self.rows = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
else:
self.rows = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows)
]
# MATRIX OPERATIONS
def __eq__(self, other):
if not isinstance(other, Matrix):
raise TypeError("A Matrix can only be compared with another Matrix")
return self.rows == other.rows
def __ne__(self, other):
return not self == other
def __neg__(self):
return self * -1
def __add__(self, other):
if self.order != other.order:
raise ValueError("Addition requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
]
)
def __sub__(self, other):
if self.order != other.order:
raise ValueError("Subtraction requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
]
)
def __mul__(self, other):
if isinstance(other, (int, float)):
return Matrix([[element * other for element in row] for row in self.rows])
elif isinstance(other, Matrix):
if self.num_columns != other.num_rows:
raise ValueError(
"The number of columns in the first matrix must "
"be equal to the number of rows in the second"
)
return Matrix(
[
[Matrix.dot_product(row, column) for column in other.columns()]
for row in self.rows
]
)
else:
raise TypeError(
"A Matrix can only be multiplied by an int, float, or another matrix"
)
def __pow__(self, other):
if not isinstance(other, int):
raise TypeError("A Matrix can only be raised to the power of an int")
if not self.is_square:
raise ValueError("Only square matrices can be raised to a power")
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable:
return self.inverse() ** (-other)
raise ValueError(
"Only invertable matrices can be raised to a negative power"
)
result = self
for i in range(other - 1):
result *= self
return result
@classmethod
def dot_product(cls, row, column):
return sum(row[i] * column[i] for i in range(len(row)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| # An OOP approach to representing and manipulating matrices
class Matrix:
"""
Matrix object generated from a 2D array where each element is an array representing
a row.
Rows can contain type int or float.
Common operations and information available.
>>> rows = [
... [1, 2, 3],
... [4, 5, 6],
... [7, 8, 9]
... ]
>>> matrix = Matrix(rows)
>>> print(matrix)
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]]
Matrix rows and columns are available as 2D arrays
>>> print(matrix.rows)
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> print(matrix.columns())
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]
Order is returned as a tuple
>>> matrix.order
(3, 3)
Squareness and invertability are represented as bool
>>> matrix.is_square
True
>>> matrix.is_invertable()
False
Identity, Minors, Cofactors and Adjugate are returned as Matrices. Inverse can be
a Matrix or Nonetype
>>> print(matrix.identity())
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
>>> print(matrix.minors())
[[-3. -6. -3.]
[-6. -12. -6.]
[-3. -6. -3.]]
>>> print(matrix.cofactors())
[[-3. 6. -3.]
[6. -12. 6.]
[-3. 6. -3.]]
>>> # won't be apparent due to the nature of the cofactor matrix
>>> print(matrix.adjugate())
[[-3. 6. -3.]
[6. -12. 6.]
[-3. 6. -3.]]
>>> print(matrix.inverse())
None
Determinant is an int, float, or Nonetype
>>> matrix.determinant()
0
Negation, scalar multiplication, addition, subtraction, multiplication and
exponentiation are available and all return a Matrix
>>> print(-matrix)
[[-1. -2. -3.]
[-4. -5. -6.]
[-7. -8. -9.]]
>>> matrix2 = matrix * 3
>>> print(matrix2)
[[3. 6. 9.]
[12. 15. 18.]
[21. 24. 27.]]
>>> print(matrix + matrix2)
[[4. 8. 12.]
[16. 20. 24.]
[28. 32. 36.]]
>>> print(matrix - matrix2)
[[-2. -4. -6.]
[-8. -10. -12.]
[-14. -16. -18.]]
>>> print(matrix ** 3)
[[468. 576. 684.]
[1062. 1305. 1548.]
[1656. 2034. 2412.]]
Matrices can also be modified
>>> matrix.add_row([10, 11, 12])
>>> print(matrix)
[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]
[10. 11. 12.]]
>>> matrix2.add_column([8, 16, 32])
>>> print(matrix2)
[[3. 6. 9. 8.]
[12. 15. 18. 16.]
[21. 24. 27. 32.]]
>>> print(matrix * matrix2)
[[90. 108. 126. 136.]
[198. 243. 288. 304.]
[306. 378. 450. 472.]
[414. 513. 612. 640.]]
"""
def __init__(self, rows):
error = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same number of values, each of which must be of type "
"int or float."
)
if len(rows) != 0:
cols = len(rows[0])
if cols == 0:
raise error
for row in rows:
if len(row) != cols:
raise error
for value in row:
if not isinstance(value, (int, float)):
raise error
self.rows = rows
else:
self.rows = []
# MATRIX INFORMATION
def columns(self):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]
@property
def num_rows(self):
return len(self.rows)
@property
def num_columns(self):
return len(self.rows[0])
@property
def order(self):
return (self.num_rows, self.num_columns)
@property
def is_square(self):
return self.order[0] == self.order[1]
def identity(self):
values = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
for row_num in range(self.num_rows)
]
return Matrix(values)
def determinant(self):
if not self.is_square:
return None
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return self.rows[0][0]
if self.order == (2, 2):
return (self.rows[0][0] * self.rows[1][1]) - (
self.rows[0][1] * self.rows[1][0]
)
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns)
)
def is_invertable(self):
return bool(self.determinant())
def get_minor(self, row, column):
values = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns)
if other_column != column
]
for other_row in range(self.num_rows)
if other_row != row
]
return Matrix(values).determinant()
def get_cofactor(self, row, column):
if (row + column) % 2 == 0:
return self.get_minor(row, column)
return -1 * self.get_minor(row, column)
def minors(self):
return Matrix(
[
[self.get_minor(row, column) for column in range(self.num_columns)]
for row in range(self.num_rows)
]
)
def cofactors(self):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns)
]
for row in range(self.minors().num_rows)
]
)
def adjugate(self):
values = [
[self.cofactors().rows[column][row] for column in range(self.num_columns)]
for row in range(self.num_rows)
]
return Matrix(values)
def inverse(self):
determinant = self.determinant()
return None if not determinant else self.adjugate() * (1 / determinant)
def __repr__(self):
return str(self.rows)
def __str__(self):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(self.rows[0]) + "]]"
return (
"["
+ "\n ".join(
[
"[" + ". ".join([str(value) for value in row]) + ".]"
for row in self.rows
]
)
+ "]"
)
# MATRIX MANIPULATION
def add_row(self, row, position=None):
type_error = TypeError("Row must be a list containing all ints and/or floats")
if not isinstance(row, list):
raise type_error
for value in row:
if not isinstance(value, (int, float)):
raise type_error
if len(row) != self.num_columns:
raise ValueError(
"Row must be equal in length to the other rows in the matrix"
)
if position is None:
self.rows.append(row)
else:
self.rows = self.rows[0:position] + [row] + self.rows[position:]
def add_column(self, column, position=None):
type_error = TypeError(
"Column must be a list containing all ints and/or floats"
)
if not isinstance(column, list):
raise type_error
for value in column:
if not isinstance(value, (int, float)):
raise type_error
if len(column) != self.num_rows:
raise ValueError(
"Column must be equal in length to the other columns in the matrix"
)
if position is None:
self.rows = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
else:
self.rows = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows)
]
# MATRIX OPERATIONS
def __eq__(self, other):
if not isinstance(other, Matrix):
raise TypeError("A Matrix can only be compared with another Matrix")
return self.rows == other.rows
def __ne__(self, other):
return not self == other
def __neg__(self):
return self * -1
def __add__(self, other):
if self.order != other.order:
raise ValueError("Addition requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
]
)
def __sub__(self, other):
if self.order != other.order:
raise ValueError("Subtraction requires matrices of the same order")
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
for i in range(self.num_rows)
]
)
def __mul__(self, other):
if isinstance(other, (int, float)):
return Matrix([[element * other for element in row] for row in self.rows])
elif isinstance(other, Matrix):
if self.num_columns != other.num_rows:
raise ValueError(
"The number of columns in the first matrix must "
"be equal to the number of rows in the second"
)
return Matrix(
[
[Matrix.dot_product(row, column) for column in other.columns()]
for row in self.rows
]
)
else:
raise TypeError(
"A Matrix can only be multiplied by an int, float, or another matrix"
)
def __pow__(self, other):
if not isinstance(other, int):
raise TypeError("A Matrix can only be raised to the power of an int")
if not self.is_square:
raise ValueError("Only square matrices can be raised to a power")
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable:
return self.inverse() ** (-other)
raise ValueError(
"Only invertable matrices can be raised to a negative power"
)
result = self
for i in range(other - 1):
result *= self
return result
@classmethod
def dot_product(cls, row, column):
return sum(row[i] * column[i] for i in range(len(row)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
@author: MatteoRaso
"""
from math import pi, sqrt
from random import uniform
from statistics import mean
from typing import Callable
def pi_estimator(iterations: int):
"""
An implementation of the Monte Carlo method used to find pi.
1. Draw a 2x2 square centred at (0,0).
2. Inscribe a circle within the square.
3. For each iteration, place a dot anywhere in the square.
a. Record the number of dots within the circle.
4. After all the dots are placed, divide the dots in the circle by the total.
5. Multiply this value by 4 to get your estimate of pi.
6. Print the estimated and numpy value of pi
"""
# A local function to see if a dot lands in the circle.
def is_in_circle(x: float, y: float) -> bool:
distance_from_centre = sqrt((x ** 2) + (y ** 2))
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
proportion = mean(
int(is_in_circle(uniform(-1.0, 1.0), uniform(-1.0, 1.0)))
for _ in range(iterations)
)
# The ratio of the area for circle to square is pi/4.
pi_estimate = proportion * 4
print(f"The estimated value of pi is {pi_estimate}")
print(f"The numpy value of pi is {pi}")
print(f"The total error is {abs(pi - pi_estimate)}")
def area_under_curve_estimator(
iterations: int,
function_to_integrate: Callable[[float], float],
min_value: float = 0.0,
max_value: float = 1.0,
) -> float:
"""
An implementation of the Monte Carlo method to find area under
a single variable non-negative real-valued continuous function,
say f(x), where x lies within a continuous bounded interval,
say [min_value, max_value], where min_value and max_value are
finite numbers
1. Let x be a uniformly distributed random variable between min_value to
max_value
2. Expected value of f(x) =
(integrate f(x) from min_value to max_value)/(max_value - min_value)
3. Finding expected value of f(x):
a. Repeatedly draw x from uniform distribution
b. Evaluate f(x) at each of the drawn x values
c. Expected value = average of the function evaluations
4. Estimated value of integral = Expected value * (max_value - min_value)
5. Returns estimated value
"""
return mean(
function_to_integrate(uniform(min_value, max_value)) for _ in range(iterations)
) * (max_value - min_value)
def area_under_line_estimator_check(
iterations: int, min_value: float = 0.0, max_value: float = 1.0
) -> None:
"""
Checks estimation error for area_under_curve_estimator function
for f(x) = x where x lies within min_value to max_value
1. Calls "area_under_curve_estimator" function
2. Compares with the expected value
3. Prints estimated, expected and error value
"""
def identity_function(x: float) -> float:
"""
Represents identity function
>>> [function_to_integrate(x) for x in [-2.0, -1.0, 0.0, 1.0, 2.0]]
[-2.0, -1.0, 0.0, 1.0, 2.0]
"""
return x
estimated_value = area_under_curve_estimator(
iterations, identity_function, min_value, max_value
)
expected_value = (max_value * max_value - min_value * min_value) / 2
print("******************")
print(f"Estimating area under y=x where x varies from {min_value} to {max_value}")
print(f"Estimated value is {estimated_value}")
print(f"Expected value is {expected_value}")
print(f"Total error is {abs(estimated_value - expected_value)}")
print("******************")
def pi_estimator_using_area_under_curve(iterations: int) -> None:
"""
Area under curve y = sqrt(4 - x^2) where x lies in 0 to 2 is equal to pi
"""
def function_to_integrate(x: float) -> float:
"""
Represents semi-circle with radius 2
>>> [function_to_integrate(x) for x in [-2.0, 0.0, 2.0]]
[0.0, 2.0, 0.0]
"""
return sqrt(4.0 - x * x)
estimated_value = area_under_curve_estimator(
iterations, function_to_integrate, 0.0, 2.0
)
print("******************")
print("Estimating pi using area_under_curve_estimator")
print(f"Estimated value is {estimated_value}")
print(f"Expected value is {pi}")
print(f"Total error is {abs(estimated_value - pi)}")
print("******************")
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
@author: MatteoRaso
"""
from math import pi, sqrt
from random import uniform
from statistics import mean
from typing import Callable
def pi_estimator(iterations: int):
"""
An implementation of the Monte Carlo method used to find pi.
1. Draw a 2x2 square centred at (0,0).
2. Inscribe a circle within the square.
3. For each iteration, place a dot anywhere in the square.
a. Record the number of dots within the circle.
4. After all the dots are placed, divide the dots in the circle by the total.
5. Multiply this value by 4 to get your estimate of pi.
6. Print the estimated and numpy value of pi
"""
# A local function to see if a dot lands in the circle.
def is_in_circle(x: float, y: float) -> bool:
distance_from_centre = sqrt((x ** 2) + (y ** 2))
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
proportion = mean(
int(is_in_circle(uniform(-1.0, 1.0), uniform(-1.0, 1.0)))
for _ in range(iterations)
)
# The ratio of the area for circle to square is pi/4.
pi_estimate = proportion * 4
print(f"The estimated value of pi is {pi_estimate}")
print(f"The numpy value of pi is {pi}")
print(f"The total error is {abs(pi - pi_estimate)}")
def area_under_curve_estimator(
iterations: int,
function_to_integrate: Callable[[float], float],
min_value: float = 0.0,
max_value: float = 1.0,
) -> float:
"""
An implementation of the Monte Carlo method to find area under
a single variable non-negative real-valued continuous function,
say f(x), where x lies within a continuous bounded interval,
say [min_value, max_value], where min_value and max_value are
finite numbers
1. Let x be a uniformly distributed random variable between min_value to
max_value
2. Expected value of f(x) =
(integrate f(x) from min_value to max_value)/(max_value - min_value)
3. Finding expected value of f(x):
a. Repeatedly draw x from uniform distribution
b. Evaluate f(x) at each of the drawn x values
c. Expected value = average of the function evaluations
4. Estimated value of integral = Expected value * (max_value - min_value)
5. Returns estimated value
"""
return mean(
function_to_integrate(uniform(min_value, max_value)) for _ in range(iterations)
) * (max_value - min_value)
def area_under_line_estimator_check(
iterations: int, min_value: float = 0.0, max_value: float = 1.0
) -> None:
"""
Checks estimation error for area_under_curve_estimator function
for f(x) = x where x lies within min_value to max_value
1. Calls "area_under_curve_estimator" function
2. Compares with the expected value
3. Prints estimated, expected and error value
"""
def identity_function(x: float) -> float:
"""
Represents identity function
>>> [function_to_integrate(x) for x in [-2.0, -1.0, 0.0, 1.0, 2.0]]
[-2.0, -1.0, 0.0, 1.0, 2.0]
"""
return x
estimated_value = area_under_curve_estimator(
iterations, identity_function, min_value, max_value
)
expected_value = (max_value * max_value - min_value * min_value) / 2
print("******************")
print(f"Estimating area under y=x where x varies from {min_value} to {max_value}")
print(f"Estimated value is {estimated_value}")
print(f"Expected value is {expected_value}")
print(f"Total error is {abs(estimated_value - expected_value)}")
print("******************")
def pi_estimator_using_area_under_curve(iterations: int) -> None:
"""
Area under curve y = sqrt(4 - x^2) where x lies in 0 to 2 is equal to pi
"""
def function_to_integrate(x: float) -> float:
"""
Represents semi-circle with radius 2
>>> [function_to_integrate(x) for x in [-2.0, 0.0, 2.0]]
[0.0, 2.0, 0.0]
"""
return sqrt(4.0 - x * x)
estimated_value = area_under_curve_estimator(
iterations, function_to_integrate, 0.0, 2.0
)
print("******************")
print("Estimating pi using area_under_curve_estimator")
print(f"Estimated value is {estimated_value}")
print(f"Expected value is {pi}")
print(f"Total error is {abs(estimated_value - pi)}")
print("******************")
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/python
""" Author: OMKAR PATHAK """
from typing import Set
class Graph:
def __init__(self) -> None:
self.vertices = {}
def print_graph(self) -> None:
"""
prints adjacency list representation of graaph
>>> g = Graph()
>>> g.print_graph()
>>> g.add_edge(0, 1)
>>> g.print_graph()
0 : 1
"""
for i in self.vertices:
print(i, " : ", " -> ".join([str(j) for j in self.vertices[i]]))
def add_edge(self, from_vertex: int, to_vertex: int) -> None:
"""
adding the edge between two vertices
>>> g = Graph()
>>> g.print_graph()
>>> g.add_edge(0, 1)
>>> g.print_graph()
0 : 1
"""
if from_vertex in self.vertices:
self.vertices[from_vertex].append(to_vertex)
else:
self.vertices[from_vertex] = [to_vertex]
def bfs(self, start_vertex: int) -> Set[int]:
"""
>>> g = Graph()
>>> g.add_edge(0, 1)
>>> g.add_edge(0, 1)
>>> g.add_edge(0, 2)
>>> g.add_edge(1, 2)
>>> g.add_edge(2, 0)
>>> g.add_edge(2, 3)
>>> g.add_edge(3, 3)
>>> sorted(g.bfs(2))
[0, 1, 2, 3]
"""
# initialize set for storing already visited vertices
visited = set()
# create a first in first out queue to store all the vertices for BFS
queue = []
# mark the source node as visited and enqueue it
visited.add(start_vertex)
queue.append(start_vertex)
while queue:
vertex = queue.pop(0)
# loop through all adjacent vertex and enqueue it if not yet visited
for adjacent_vertex in self.vertices[vertex]:
if adjacent_vertex not in visited:
queue.append(adjacent_vertex)
visited.add(adjacent_vertex)
return visited
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
g = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
# 0 : 1 -> 2
# 1 : 2
# 2 : 0 -> 3
# 3 : 3
assert sorted(g.bfs(2)) == [0, 1, 2, 3]
| #!/usr/bin/python
""" Author: OMKAR PATHAK """
from typing import Set
class Graph:
def __init__(self) -> None:
self.vertices = {}
def print_graph(self) -> None:
"""
prints adjacency list representation of graaph
>>> g = Graph()
>>> g.print_graph()
>>> g.add_edge(0, 1)
>>> g.print_graph()
0 : 1
"""
for i in self.vertices:
print(i, " : ", " -> ".join([str(j) for j in self.vertices[i]]))
def add_edge(self, from_vertex: int, to_vertex: int) -> None:
"""
adding the edge between two vertices
>>> g = Graph()
>>> g.print_graph()
>>> g.add_edge(0, 1)
>>> g.print_graph()
0 : 1
"""
if from_vertex in self.vertices:
self.vertices[from_vertex].append(to_vertex)
else:
self.vertices[from_vertex] = [to_vertex]
def bfs(self, start_vertex: int) -> Set[int]:
"""
>>> g = Graph()
>>> g.add_edge(0, 1)
>>> g.add_edge(0, 1)
>>> g.add_edge(0, 2)
>>> g.add_edge(1, 2)
>>> g.add_edge(2, 0)
>>> g.add_edge(2, 3)
>>> g.add_edge(3, 3)
>>> sorted(g.bfs(2))
[0, 1, 2, 3]
"""
# initialize set for storing already visited vertices
visited = set()
# create a first in first out queue to store all the vertices for BFS
queue = []
# mark the source node as visited and enqueue it
visited.add(start_vertex)
queue.append(start_vertex)
while queue:
vertex = queue.pop(0)
# loop through all adjacent vertex and enqueue it if not yet visited
for adjacent_vertex in self.vertices[vertex]:
if adjacent_vertex not in visited:
queue.append(adjacent_vertex)
visited.add(adjacent_vertex)
return visited
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
g = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
# 0 : 1 -> 2
# 1 : 2
# 2 : 0 -> 3
# 3 : 3
assert sorted(g.bfs(2)) == [0, 1, 2, 3]
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 43: https://projecteuler.net/problem=43
The number, 1406357289, is a 0 to 9 pandigital number because it is made up of
each of the digits 0 to 9 in some order, but it also has a rather interesting
sub-string divisibility property.
Let d1 be the 1st digit, d2 be the 2nd digit, and so on. In this way, we note
the following:
d2d3d4=406 is divisible by 2
d3d4d5=063 is divisible by 3
d4d5d6=635 is divisible by 5
d5d6d7=357 is divisible by 7
d6d7d8=572 is divisible by 11
d7d8d9=728 is divisible by 13
d8d9d10=289 is divisible by 17
Find the sum of all 0 to 9 pandigital numbers with this property.
"""
from itertools import permutations
def is_substring_divisible(num: tuple) -> bool:
"""
Returns True if the pandigital number passes
all the divisibility tests.
>>> is_substring_divisible((0, 1, 2, 4, 6, 5, 7, 3, 8, 9))
False
>>> is_substring_divisible((5, 1, 2, 4, 6, 0, 7, 8, 3, 9))
False
>>> is_substring_divisible((1, 4, 0, 6, 3, 5, 7, 2, 8, 9))
True
"""
tests = [2, 3, 5, 7, 11, 13, 17]
for i, test in enumerate(tests):
if (num[i + 1] * 100 + num[i + 2] * 10 + num[i + 3]) % test != 0:
return False
return True
def solution(n: int = 10) -> int:
"""
Returns the sum of all pandigital numbers which pass the
divisiility tests.
>>> solution(10)
16695334890
"""
list_nums = [
int("".join(map(str, num)))
for num in permutations(range(n))
if is_substring_divisible(num)
]
return sum(list_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Problem 43: https://projecteuler.net/problem=43
The number, 1406357289, is a 0 to 9 pandigital number because it is made up of
each of the digits 0 to 9 in some order, but it also has a rather interesting
sub-string divisibility property.
Let d1 be the 1st digit, d2 be the 2nd digit, and so on. In this way, we note
the following:
d2d3d4=406 is divisible by 2
d3d4d5=063 is divisible by 3
d4d5d6=635 is divisible by 5
d5d6d7=357 is divisible by 7
d6d7d8=572 is divisible by 11
d7d8d9=728 is divisible by 13
d8d9d10=289 is divisible by 17
Find the sum of all 0 to 9 pandigital numbers with this property.
"""
from itertools import permutations
def is_substring_divisible(num: tuple) -> bool:
"""
Returns True if the pandigital number passes
all the divisibility tests.
>>> is_substring_divisible((0, 1, 2, 4, 6, 5, 7, 3, 8, 9))
False
>>> is_substring_divisible((5, 1, 2, 4, 6, 0, 7, 8, 3, 9))
False
>>> is_substring_divisible((1, 4, 0, 6, 3, 5, 7, 2, 8, 9))
True
"""
tests = [2, 3, 5, 7, 11, 13, 17]
for i, test in enumerate(tests):
if (num[i + 1] * 100 + num[i + 2] * 10 + num[i + 3]) % test != 0:
return False
return True
def solution(n: int = 10) -> int:
"""
Returns the sum of all pandigital numbers which pass the
divisiility tests.
>>> solution(10)
16695334890
"""
list_nums = [
int("".join(map(str, num)))
for num in permutations(range(n))
if is_substring_divisible(num)
]
return sum(list_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from bisect import bisect_left
from functools import total_ordering
from heapq import merge
from typing import List
"""
A pure Python implementation of the patience sort algorithm
For more information: https://en.wikipedia.org/wiki/Patience_sorting
This algorithm is based on the card game patience
For doctests run following command:
python3 -m doctest -v patience_sort.py
For manual testing run:
python3 patience_sort.py
"""
@total_ordering
class Stack(list):
def __lt__(self, other):
return self[-1] < other[-1]
def __eq__(self, other):
return self[-1] == other[-1]
def patience_sort(collection: list) -> list:
"""A pure implementation of quick sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> patience_sort([1, 9, 5, 21, 17, 6])
[1, 5, 6, 9, 17, 21]
>>> patience_sort([])
[]
>>> patience_sort([-3, -17, -48])
[-48, -17, -3]
"""
stacks: List[Stack] = []
# sort into stacks
for element in collection:
new_stacks = Stack([element])
i = bisect_left(stacks, new_stacks)
if i != len(stacks):
stacks[i].append(element)
else:
stacks.append(new_stacks)
# use a heap-based merge to merge stack efficiently
collection[:] = merge(*[reversed(stack) for stack in stacks])
return collection
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(patience_sort(unsorted))
| from bisect import bisect_left
from functools import total_ordering
from heapq import merge
from typing import List
"""
A pure Python implementation of the patience sort algorithm
For more information: https://en.wikipedia.org/wiki/Patience_sorting
This algorithm is based on the card game patience
For doctests run following command:
python3 -m doctest -v patience_sort.py
For manual testing run:
python3 patience_sort.py
"""
@total_ordering
class Stack(list):
def __lt__(self, other):
return self[-1] < other[-1]
def __eq__(self, other):
return self[-1] == other[-1]
def patience_sort(collection: list) -> list:
"""A pure implementation of quick sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> patience_sort([1, 9, 5, 21, 17, 6])
[1, 5, 6, 9, 17, 21]
>>> patience_sort([])
[]
>>> patience_sort([-3, -17, -48])
[-48, -17, -3]
"""
stacks: List[Stack] = []
# sort into stacks
for element in collection:
new_stacks = Stack([element])
i = bisect_left(stacks, new_stacks)
if i != len(stacks):
stacks[i].append(element)
else:
stacks.append(new_stacks)
# use a heap-based merge to merge stack efficiently
collection[:] = merge(*[reversed(stack) for stack in stacks])
return collection
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(patience_sort(unsorted))
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 207: https://projecteuler.net/problem=207
Problem Statement:
For some positive integers k, there exists an integer partition of the form
4**t = 2**t + k, where 4**t, 2**t, and k are all positive integers and t is a real
number. The first two such partitions are 4**1 = 2**1 + 2 and
4**1.5849625... = 2**1.5849625... + 6.
Partitions where t is also an integer are called perfect.
For any m ≥ 1 let P(m) be the proportion of such partitions that are perfect with
k ≤ m.
Thus P(6) = 1/2.
In the following table are listed some values of P(m)
P(5) = 1/1
P(10) = 1/2
P(15) = 2/3
P(20) = 1/2
P(25) = 1/2
P(30) = 2/5
...
P(180) = 1/4
P(185) = 3/13
Find the smallest m for which P(m) < 1/12345
Solution:
Equation 4**t = 2**t + k solved for t gives:
t = log2(sqrt(4*k+1)/2 + 1/2)
For t to be real valued, sqrt(4*k+1) must be an integer which is implemented in
function check_t_real(k). For a perfect partition t must be an integer.
To speed up significantly the search for partitions, instead of incrementing k by one
per iteration, the next valid k is found by k = (i**2 - 1) / 4 with an integer i and
k has to be a positive integer. If this is the case a partition is found. The partition
is perfect if t os an integer. The integer i is increased with increment 1 until the
proportion perfect partitions / total partitions drops under the given value.
"""
import math
def check_partition_perfect(positive_integer: int) -> bool:
"""
Check if t = f(positive_integer) = log2(sqrt(4*positive_integer+1)/2 + 1/2) is a
real number.
>>> check_partition_perfect(2)
True
>>> check_partition_perfect(6)
False
"""
exponent = math.log2(math.sqrt(4 * positive_integer + 1) / 2 + 1 / 2)
return exponent == int(exponent)
def solution(max_proportion: float = 1 / 12345) -> int:
"""
Find m for which the proportion of perfect partitions to total partitions is lower
than max_proportion
>>> solution(1) > 5
True
>>> solution(1/2) > 10
True
>>> solution(3 / 13) > 185
True
"""
total_partitions = 0
perfect_partitions = 0
integer = 3
while True:
partition_candidate = (integer ** 2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(partition_candidate):
partition_candidate = int(partition_candidate)
total_partitions += 1
if check_partition_perfect(partition_candidate):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return partition_candidate
integer += 1
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 207: https://projecteuler.net/problem=207
Problem Statement:
For some positive integers k, there exists an integer partition of the form
4**t = 2**t + k, where 4**t, 2**t, and k are all positive integers and t is a real
number. The first two such partitions are 4**1 = 2**1 + 2 and
4**1.5849625... = 2**1.5849625... + 6.
Partitions where t is also an integer are called perfect.
For any m ≥ 1 let P(m) be the proportion of such partitions that are perfect with
k ≤ m.
Thus P(6) = 1/2.
In the following table are listed some values of P(m)
P(5) = 1/1
P(10) = 1/2
P(15) = 2/3
P(20) = 1/2
P(25) = 1/2
P(30) = 2/5
...
P(180) = 1/4
P(185) = 3/13
Find the smallest m for which P(m) < 1/12345
Solution:
Equation 4**t = 2**t + k solved for t gives:
t = log2(sqrt(4*k+1)/2 + 1/2)
For t to be real valued, sqrt(4*k+1) must be an integer which is implemented in
function check_t_real(k). For a perfect partition t must be an integer.
To speed up significantly the search for partitions, instead of incrementing k by one
per iteration, the next valid k is found by k = (i**2 - 1) / 4 with an integer i and
k has to be a positive integer. If this is the case a partition is found. The partition
is perfect if t os an integer. The integer i is increased with increment 1 until the
proportion perfect partitions / total partitions drops under the given value.
"""
import math
def check_partition_perfect(positive_integer: int) -> bool:
"""
Check if t = f(positive_integer) = log2(sqrt(4*positive_integer+1)/2 + 1/2) is a
real number.
>>> check_partition_perfect(2)
True
>>> check_partition_perfect(6)
False
"""
exponent = math.log2(math.sqrt(4 * positive_integer + 1) / 2 + 1 / 2)
return exponent == int(exponent)
def solution(max_proportion: float = 1 / 12345) -> int:
"""
Find m for which the proportion of perfect partitions to total partitions is lower
than max_proportion
>>> solution(1) > 5
True
>>> solution(1/2) > 10
True
>>> solution(3 / 13) > 185
True
"""
total_partitions = 0
perfect_partitions = 0
integer = 3
while True:
partition_candidate = (integer ** 2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(partition_candidate):
partition_candidate = int(partition_candidate)
total_partitions += 1
if check_partition_perfect(partition_candidate):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return partition_candidate
integer += 1
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
python/black : True
"""
from __future__ import annotations
def prime_factors(n: int) -> list[int]:
"""
Returns prime factors of n as a list.
>>> prime_factors(0)
[]
>>> prime_factors(100)
[2, 2, 5, 5]
>>> prime_factors(2560)
[2, 2, 2, 2, 2, 2, 2, 2, 2, 5]
>>> prime_factors(10**-2)
[]
>>> prime_factors(0.02)
[]
>>> x = prime_factors(10**241) # doctest: +NORMALIZE_WHITESPACE
>>> x == [2]*241 + [5]*241
True
>>> prime_factors(10**-354)
[]
>>> prime_factors('hello')
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> prime_factors([1,2,'hello'])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
i = 2
factors = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(i)
if n > 1:
factors.append(n)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
python/black : True
"""
from __future__ import annotations
def prime_factors(n: int) -> list[int]:
"""
Returns prime factors of n as a list.
>>> prime_factors(0)
[]
>>> prime_factors(100)
[2, 2, 5, 5]
>>> prime_factors(2560)
[2, 2, 2, 2, 2, 2, 2, 2, 2, 5]
>>> prime_factors(10**-2)
[]
>>> prime_factors(0.02)
[]
>>> x = prime_factors(10**241) # doctest: +NORMALIZE_WHITESPACE
>>> x == [2]*241 + [5]*241
True
>>> prime_factors(10**-354)
[]
>>> prime_factors('hello')
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> prime_factors([1,2,'hello'])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
i = 2
factors = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(i)
if n > 1:
factors.append(n)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Created by susmith98
from collections import Counter
from timeit import timeit
# Problem Description:
# Check if characters of the given string can be rearranged to form a palindrome.
# Counter is faster for long strings and non-Counter is faster for short strings.
def can_string_be_rearranged_as_palindrome_counter(
input_str: str = "",
) -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome_counter("Momo")
True
>>> can_string_be_rearranged_as_palindrome_counter("Mother")
False
>>> can_string_be_rearranged_as_palindrome_counter("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
return sum(c % 2 for c in Counter(input_str.replace(" ", "").lower()).values()) < 2
def can_string_be_rearranged_as_palindrome(input_str: str = "") -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome("Momo")
True
>>> can_string_be_rearranged_as_palindrome("Mother")
False
>>> can_string_be_rearranged_as_palindrome("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
if len(input_str) == 0:
return True
lower_case_input_str = input_str.replace(" ", "").lower()
# character_freq_dict: Stores the frequency of every character in the input string
character_freq_dict = {}
for character in lower_case_input_str:
character_freq_dict[character] = character_freq_dict.get(character, 0) + 1
"""
Above line of code is equivalent to:
1) Getting the frequency of current character till previous index
>>> character_freq = character_freq_dict.get(character, 0)
2) Incrementing the frequency of current character by 1
>>> character_freq = character_freq + 1
3) Updating the frequency of current character
>>> character_freq_dict[character] = character_freq
"""
"""
OBSERVATIONS:
Even length palindrome
-> Every character appears even no.of times.
Odd length palindrome
-> Every character appears even no.of times except for one character.
LOGIC:
Step 1: We'll count number of characters that appear odd number of times i.e oddChar
Step 2:If we find more than 1 character that appears odd number of times,
It is not possible to rearrange as a palindrome
"""
oddChar = 0
for character_count in character_freq_dict.values():
if character_count % 2:
oddChar += 1
if oddChar > 1:
return False
return True
def benchmark(input_str: str = "") -> None:
"""
Benchmark code for comparing above 2 functions
"""
print("\nFor string = ", input_str, ":")
print(
"> can_string_be_rearranged_as_palindrome_counter()",
"\tans =",
can_string_be_rearranged_as_palindrome_counter(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
print(
"> can_string_be_rearranged_as_palindrome()",
"\tans =",
can_string_be_rearranged_as_palindrome(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
if __name__ == "__main__":
check_str = input(
"Enter string to determine if it can be rearranged as a palindrome or not: "
).strip()
benchmark(check_str)
status = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")
| # Created by susmith98
from collections import Counter
from timeit import timeit
# Problem Description:
# Check if characters of the given string can be rearranged to form a palindrome.
# Counter is faster for long strings and non-Counter is faster for short strings.
def can_string_be_rearranged_as_palindrome_counter(
input_str: str = "",
) -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome_counter("Momo")
True
>>> can_string_be_rearranged_as_palindrome_counter("Mother")
False
>>> can_string_be_rearranged_as_palindrome_counter("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
return sum(c % 2 for c in Counter(input_str.replace(" ", "").lower()).values()) < 2
def can_string_be_rearranged_as_palindrome(input_str: str = "") -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome("Momo")
True
>>> can_string_be_rearranged_as_palindrome("Mother")
False
>>> can_string_be_rearranged_as_palindrome("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
if len(input_str) == 0:
return True
lower_case_input_str = input_str.replace(" ", "").lower()
# character_freq_dict: Stores the frequency of every character in the input string
character_freq_dict = {}
for character in lower_case_input_str:
character_freq_dict[character] = character_freq_dict.get(character, 0) + 1
"""
Above line of code is equivalent to:
1) Getting the frequency of current character till previous index
>>> character_freq = character_freq_dict.get(character, 0)
2) Incrementing the frequency of current character by 1
>>> character_freq = character_freq + 1
3) Updating the frequency of current character
>>> character_freq_dict[character] = character_freq
"""
"""
OBSERVATIONS:
Even length palindrome
-> Every character appears even no.of times.
Odd length palindrome
-> Every character appears even no.of times except for one character.
LOGIC:
Step 1: We'll count number of characters that appear odd number of times i.e oddChar
Step 2:If we find more than 1 character that appears odd number of times,
It is not possible to rearrange as a palindrome
"""
oddChar = 0
for character_count in character_freq_dict.values():
if character_count % 2:
oddChar += 1
if oddChar > 1:
return False
return True
def benchmark(input_str: str = "") -> None:
"""
Benchmark code for comparing above 2 functions
"""
print("\nFor string = ", input_str, ":")
print(
"> can_string_be_rearranged_as_palindrome_counter()",
"\tans =",
can_string_be_rearranged_as_palindrome_counter(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
print(
"> can_string_be_rearranged_as_palindrome()",
"\tans =",
can_string_be_rearranged_as_palindrome(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
if __name__ == "__main__":
check_str = input(
"Enter string to determine if it can be rearranged as a palindrome or not: "
).strip()
benchmark(check_str)
status = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Password generator allows you to generate a random password of length N."""
from random import choice, shuffle
from string import ascii_letters, digits, punctuation
def password_generator(length=8):
"""
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = tuple(ascii_letters) + tuple(digits) + tuple(punctuation)
return "".join(choice(chars) for x in range(length))
# ALTERNATIVE METHODS
# ctbi= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(ctbi, i):
# Password generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i = i - len(ctbi)
quotient = int(i / 3)
remainder = i % 3
# chars = ctbi + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
ctbi
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
chars = list(chars)
shuffle(chars)
return "".join(chars)
# random is a generalised function for letters, characters and numbers
def random(ctbi, i):
return "".join(choice(ctbi) for x in range(i))
def random_number(ctbi, i):
pass # Put your code here...
def random_letters(ctbi, i):
pass # Put your code here...
def random_characters(ctbi, i):
pass # Put your code here...
def main():
length = int(input("Please indicate the max length of your password: ").strip())
ctbi = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:", alternative_password_generator(ctbi, length)
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| """Password generator allows you to generate a random password of length N."""
from random import choice, shuffle
from string import ascii_letters, digits, punctuation
def password_generator(length=8):
"""
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = tuple(ascii_letters) + tuple(digits) + tuple(punctuation)
return "".join(choice(chars) for x in range(length))
# ALTERNATIVE METHODS
# ctbi= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(ctbi, i):
# Password generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i = i - len(ctbi)
quotient = int(i / 3)
remainder = i % 3
# chars = ctbi + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
ctbi
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
chars = list(chars)
shuffle(chars)
return "".join(chars)
# random is a generalised function for letters, characters and numbers
def random(ctbi, i):
return "".join(choice(ctbi) for x in range(i))
def random_number(ctbi, i):
pass # Put your code here...
def random_letters(ctbi, i):
pass # Put your code here...
def random_characters(ctbi, i):
pass # Put your code here...
def main():
length = int(input("Please indicate the max length of your password: ").strip())
ctbi = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:", alternative_password_generator(ctbi, length)
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Round Robin is a scheduling algorithm.
In Round Robin each process is assigned a fixed time slot in a cyclic way.
https://en.wikipedia.org/wiki/Round-robin_scheduling
"""
from statistics import mean
from typing import List
def calculate_waiting_times(burst_times: List[int]) -> List[int]:
"""
Calculate the waiting times of a list of processes that have a specified duration.
Return: The waiting time for each process.
>>> calculate_waiting_times([10, 5, 8])
[13, 10, 13]
>>> calculate_waiting_times([4, 6, 3, 1])
[5, 8, 9, 6]
>>> calculate_waiting_times([12, 2, 10])
[12, 2, 12]
"""
quantum = 2
rem_burst_times = list(burst_times)
waiting_times = [0] * len(burst_times)
t = 0
while True:
done = True
for i, burst_time in enumerate(burst_times):
if rem_burst_times[i] > 0:
done = False
if rem_burst_times[i] > quantum:
t += quantum
rem_burst_times[i] -= quantum
else:
t += rem_burst_times[i]
waiting_times[i] = t - burst_time
rem_burst_times[i] = 0
if done is True:
return waiting_times
def calculate_turn_around_times(
burst_times: List[int], waiting_times: List[int]
) -> List[int]:
"""
>>> calculate_turn_around_times([1, 2, 3, 4], [0, 1, 3])
[1, 3, 6]
>>> calculate_turn_around_times([10, 3, 7], [10, 6, 11])
[20, 9, 18]
"""
return [burst + waiting for burst, waiting in zip(burst_times, waiting_times)]
if __name__ == "__main__":
burst_times = [3, 5, 7]
waiting_times = calculate_waiting_times(burst_times)
turn_around_times = calculate_turn_around_times(burst_times, waiting_times)
print("Process ID \tBurst Time \tWaiting Time \tTurnaround Time")
for i, burst_time in enumerate(burst_times):
print(
f" {i + 1}\t\t {burst_time}\t\t {waiting_times[i]}\t\t "
f"{turn_around_times[i]}"
)
print(f"\nAverage waiting time = {mean(waiting_times):.5f}")
print(f"Average turn around time = {mean(turn_around_times):.5f}")
| """
Round Robin is a scheduling algorithm.
In Round Robin each process is assigned a fixed time slot in a cyclic way.
https://en.wikipedia.org/wiki/Round-robin_scheduling
"""
from statistics import mean
from typing import List
def calculate_waiting_times(burst_times: List[int]) -> List[int]:
"""
Calculate the waiting times of a list of processes that have a specified duration.
Return: The waiting time for each process.
>>> calculate_waiting_times([10, 5, 8])
[13, 10, 13]
>>> calculate_waiting_times([4, 6, 3, 1])
[5, 8, 9, 6]
>>> calculate_waiting_times([12, 2, 10])
[12, 2, 12]
"""
quantum = 2
rem_burst_times = list(burst_times)
waiting_times = [0] * len(burst_times)
t = 0
while True:
done = True
for i, burst_time in enumerate(burst_times):
if rem_burst_times[i] > 0:
done = False
if rem_burst_times[i] > quantum:
t += quantum
rem_burst_times[i] -= quantum
else:
t += rem_burst_times[i]
waiting_times[i] = t - burst_time
rem_burst_times[i] = 0
if done is True:
return waiting_times
def calculate_turn_around_times(
burst_times: List[int], waiting_times: List[int]
) -> List[int]:
"""
>>> calculate_turn_around_times([1, 2, 3, 4], [0, 1, 3])
[1, 3, 6]
>>> calculate_turn_around_times([10, 3, 7], [10, 6, 11])
[20, 9, 18]
"""
return [burst + waiting for burst, waiting in zip(burst_times, waiting_times)]
if __name__ == "__main__":
burst_times = [3, 5, 7]
waiting_times = calculate_waiting_times(burst_times)
turn_around_times = calculate_turn_around_times(burst_times, waiting_times)
print("Process ID \tBurst Time \tWaiting Time \tTurnaround Time")
for i, burst_time in enumerate(burst_times):
print(
f" {i + 1}\t\t {burst_time}\t\t {waiting_times[i]}\t\t "
f"{turn_around_times[i]}"
)
print(f"\nAverage waiting time = {mean(waiting_times):.5f}")
print(f"Average turn around time = {mean(turn_around_times):.5f}")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Created by sarathkaul on 12/11/19
import requests
_NEWS_API = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def fetch_bbc_news(bbc_news_api_key: str) -> None:
# fetching a list of articles in json format
bbc_news_page = requests.get(_NEWS_API + bbc_news_api_key).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"], 1):
print(f"{i}.) {article['title']}")
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| # Created by sarathkaul on 12/11/19
import requests
_NEWS_API = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def fetch_bbc_news(bbc_news_api_key: str) -> None:
# fetching a list of articles in json format
bbc_news_page = requests.get(_NEWS_API + bbc_news_api_key).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"], 1):
print(f"{i}.) {article['title']}")
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x ** 2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| # https://en.wikipedia.org/wiki/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x ** 2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/python
""" Author: OMKAR PATHAK """
class Graph:
def __init__(self):
self.vertex = {}
# for printing the Graph vertices
def print_graph(self) -> None:
print(self.vertex)
for i in self.vertex:
print(i, " -> ", " -> ".join([str(j) for j in self.vertex[i]]))
# for adding the edge between two vertices
def add_edge(self, from_vertex: int, to_vertex: int) -> None:
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(to_vertex)
else:
# else make a new vertex
self.vertex[from_vertex] = [to_vertex]
def dfs(self) -> None:
# visited array for storing already visited nodes
visited = [False] * len(self.vertex)
# call the recursive helper function
for i in range(len(self.vertex)):
if not visited[i]:
self.dfs_recursive(i, visited)
def dfs_recursive(self, start_vertex: int, visited: list) -> None:
# mark start vertex as visited
visited[start_vertex] = True
print(start_vertex, end=" ")
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(i, visited)
if __name__ == "__main__":
g = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("DFS:")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| #!/usr/bin/python
""" Author: OMKAR PATHAK """
class Graph:
def __init__(self):
self.vertex = {}
# for printing the Graph vertices
def print_graph(self) -> None:
print(self.vertex)
for i in self.vertex:
print(i, " -> ", " -> ".join([str(j) for j in self.vertex[i]]))
# for adding the edge between two vertices
def add_edge(self, from_vertex: int, to_vertex: int) -> None:
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(to_vertex)
else:
# else make a new vertex
self.vertex[from_vertex] = [to_vertex]
def dfs(self) -> None:
# visited array for storing already visited nodes
visited = [False] * len(self.vertex)
# call the recursive helper function
for i in range(len(self.vertex)):
if not visited[i]:
self.dfs_recursive(i, visited)
def dfs_recursive(self, start_vertex: int, visited: list) -> None:
# mark start vertex as visited
visited[start_vertex] = True
print(start_vertex, end=" ")
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(i, visited)
if __name__ == "__main__":
g = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("DFS:")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from sklearn.neural_network import MLPClassifier
X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
y = [0, 1, 0, 0]
clf = MLPClassifier(
solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)
clf.fit(X, y)
test = [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
Y = clf.predict(test)
def wrapper(Y):
"""
>>> wrapper(Y)
[0, 0, 1]
"""
return list(Y)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from sklearn.neural_network import MLPClassifier
X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
y = [0, 1, 0, 0]
clf = MLPClassifier(
solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)
clf.fit(X, y)
test = [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
Y = clf.predict(test)
def wrapper(Y):
"""
>>> wrapper(Y)
[0, 0, 1]
"""
return list(Y)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def topologicalSort(graph):
"""
Kahn's Algorithm is used to find Topological ordering of Directed Acyclic Graph
using BFS
"""
indegree = [0] * len(graph)
queue = []
topo = []
cnt = 0
for key, values in graph.items():
for i in values:
indegree[i] += 1
for i in range(len(indegree)):
if indegree[i] == 0:
queue.append(i)
while queue:
vertex = queue.pop(0)
cnt += 1
topo.append(vertex)
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(x)
if cnt != len(graph):
print("Cycle exists")
else:
print(topo)
# Adjacency List of Graph
graph = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topologicalSort(graph)
| def topologicalSort(graph):
"""
Kahn's Algorithm is used to find Topological ordering of Directed Acyclic Graph
using BFS
"""
indegree = [0] * len(graph)
queue = []
topo = []
cnt = 0
for key, values in graph.items():
for i in values:
indegree[i] += 1
for i in range(len(indegree)):
if indegree[i] == 0:
queue.append(i)
while queue:
vertex = queue.pop(0)
cnt += 1
topo.append(vertex)
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(x)
if cnt != len(graph):
print("Cycle exists")
else:
print(topo)
# Adjacency List of Graph
graph = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topologicalSort(graph)
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Modular Exponential.
Modular exponentiation is a type of exponentiation performed over a modulus.
For more explanation, please check
https://en.wikipedia.org/wiki/Modular_exponentiation
"""
"""Calculate Modular Exponential."""
def modular_exponential(base: int, power: int, mod: int):
"""
>>> modular_exponential(5, 0, 10)
1
>>> modular_exponential(2, 8, 7)
4
>>> modular_exponential(3, -2, 9)
-1
"""
if power < 0:
return -1
base %= mod
result = 1
while power > 0:
if power & 1:
result = (result * base) % mod
power = power >> 1
base = (base * base) % mod
return result
def main():
"""Call Modular Exponential Function."""
print(modular_exponential(3, 200, 13))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| """
Modular Exponential.
Modular exponentiation is a type of exponentiation performed over a modulus.
For more explanation, please check
https://en.wikipedia.org/wiki/Modular_exponentiation
"""
"""Calculate Modular Exponential."""
def modular_exponential(base: int, power: int, mod: int):
"""
>>> modular_exponential(5, 0, 10)
1
>>> modular_exponential(2, 8, 7)
4
>>> modular_exponential(3, -2, 9)
-1
"""
if power < 0:
return -1
base %= mod
result = 1
while power > 0:
if power & 1:
result = (result * base) % mod
power = power >> 1
base = (base * base) % mod
return result
def main():
"""Call Modular Exponential Function."""
print(modular_exponential(3, 200, 13))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 7: https://projecteuler.net/problem=7
10001st prime
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.
What is the 10001st prime number?
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
import itertools
import math
def prime_check(number: int) -> bool:
"""
Determines whether a given number is prime or not
>>> prime_check(2)
True
>>> prime_check(15)
False
>>> prime_check(29)
True
"""
if number % 2 == 0 and number > 2:
return False
return all(number % i for i in range(3, int(math.sqrt(number)) + 1, 2))
def prime_generator():
"""
Generate a sequence of prime numbers
"""
num = 2
while True:
if prime_check(num):
yield num
num += 1
def solution(nth: int = 10001) -> int:
"""
Returns the n-th prime number.
>>> solution(6)
13
>>> solution(1)
2
>>> solution(3)
5
>>> solution(20)
71
>>> solution(50)
229
>>> solution(100)
541
"""
return next(itertools.islice(prime_generator(), nth - 1, nth))
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 7: https://projecteuler.net/problem=7
10001st prime
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.
What is the 10001st prime number?
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
import itertools
import math
def prime_check(number: int) -> bool:
"""
Determines whether a given number is prime or not
>>> prime_check(2)
True
>>> prime_check(15)
False
>>> prime_check(29)
True
"""
if number % 2 == 0 and number > 2:
return False
return all(number % i for i in range(3, int(math.sqrt(number)) + 1, 2))
def prime_generator():
"""
Generate a sequence of prime numbers
"""
num = 2
while True:
if prime_check(num):
yield num
num += 1
def solution(nth: int = 10001) -> int:
"""
Returns the n-th prime number.
>>> solution(6)
13
>>> solution(1)
2
>>> solution(3)
5
>>> solution(20)
71
>>> solution(50)
229
>>> solution(100)
541
"""
return next(itertools.islice(prime_generator(), nth - 1, nth))
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 1: https://projecteuler.net/problem=1
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.
Find the sum of all the multiples of 3 or 5 below 1000.
"""
def solution(n: int = 1000) -> int:
"""
Returns the sum of all the multiples of 3 or 5 below n.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
result = 0
for i in range(n):
if i % 3 == 0:
result += i
elif i % 5 == 0:
result += i
return result
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 1: https://projecteuler.net/problem=1
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.
Find the sum of all the multiples of 3 or 5 below 1000.
"""
def solution(n: int = 1000) -> int:
"""
Returns the sum of all the multiples of 3 or 5 below n.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
result = 0
for i in range(n):
if i % 3 == 0:
result += i
elif i % 5 == 0:
result += i
return result
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Coin sums
Problem 31: https://projecteuler.net/problem=31
In England the currency is made up of pound, £, and pence, p, and there are
eight coins in general circulation:
1p, 2p, 5p, 10p, 20p, 50p, £1 (100p) and £2 (200p).
It is possible to make £2 in the following way:
1×£1 + 1×50p + 2×20p + 1×5p + 1×2p + 3×1p
How many different ways can £2 be made using any number of coins?
"""
def one_pence() -> int:
return 1
def two_pence(x: int) -> int:
return 0 if x < 0 else two_pence(x - 2) + one_pence()
def five_pence(x: int) -> int:
return 0 if x < 0 else five_pence(x - 5) + two_pence(x)
def ten_pence(x: int) -> int:
return 0 if x < 0 else ten_pence(x - 10) + five_pence(x)
def twenty_pence(x: int) -> int:
return 0 if x < 0 else twenty_pence(x - 20) + ten_pence(x)
def fifty_pence(x: int) -> int:
return 0 if x < 0 else fifty_pence(x - 50) + twenty_pence(x)
def one_pound(x: int) -> int:
return 0 if x < 0 else one_pound(x - 100) + fifty_pence(x)
def two_pound(x: int) -> int:
return 0 if x < 0 else two_pound(x - 200) + one_pound(x)
def solution(n: int = 200) -> int:
"""Returns the number of different ways can n pence be made using any number of
coins?
>>> solution(500)
6295434
>>> solution(200)
73682
>>> solution(50)
451
>>> solution(10)
11
"""
return two_pound(n)
if __name__ == "__main__":
print(solution(int(input().strip())))
| """
Coin sums
Problem 31: https://projecteuler.net/problem=31
In England the currency is made up of pound, £, and pence, p, and there are
eight coins in general circulation:
1p, 2p, 5p, 10p, 20p, 50p, £1 (100p) and £2 (200p).
It is possible to make £2 in the following way:
1×£1 + 1×50p + 2×20p + 1×5p + 1×2p + 3×1p
How many different ways can £2 be made using any number of coins?
"""
def one_pence() -> int:
return 1
def two_pence(x: int) -> int:
return 0 if x < 0 else two_pence(x - 2) + one_pence()
def five_pence(x: int) -> int:
return 0 if x < 0 else five_pence(x - 5) + two_pence(x)
def ten_pence(x: int) -> int:
return 0 if x < 0 else ten_pence(x - 10) + five_pence(x)
def twenty_pence(x: int) -> int:
return 0 if x < 0 else twenty_pence(x - 20) + ten_pence(x)
def fifty_pence(x: int) -> int:
return 0 if x < 0 else fifty_pence(x - 50) + twenty_pence(x)
def one_pound(x: int) -> int:
return 0 if x < 0 else one_pound(x - 100) + fifty_pence(x)
def two_pound(x: int) -> int:
return 0 if x < 0 else two_pound(x - 200) + one_pound(x)
def solution(n: int = 200) -> int:
"""Returns the number of different ways can n pence be made using any number of
coins?
>>> solution(500)
6295434
>>> solution(200)
73682
>>> solution(50)
451
>>> solution(10)
11
"""
return two_pound(n)
if __name__ == "__main__":
print(solution(int(input().strip())))
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The number 3797 has an interesting property. Being prime itself, it is possible
to continuously remove digits from left to right, and remain prime at each stage:
3797, 797, 97, and 7. Similarly we can work from right to left: 3797, 379, 37, and 3.
Find the sum of the only eleven primes that are both truncatable from left to right
and right to left.
NOTE: 2, 3, 5, and 7 are not considered to be truncatable primes.
"""
from __future__ import annotations
seive = [True] * 1000001
seive[1] = False
i = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
seive[j] = False
i += 1
def is_prime(n: int) -> bool:
"""
Returns True if n is prime,
False otherwise, for 1 <= n <= 1000000
>>> is_prime(87)
False
>>> is_prime(1)
False
>>> is_prime(25363)
False
"""
return seive[n]
def list_truncated_nums(n: int) -> list[int]:
"""
Returns a list of all left and right truncated numbers of n
>>> list_truncated_nums(927628)
[927628, 27628, 92762, 7628, 9276, 628, 927, 28, 92, 8, 9]
>>> list_truncated_nums(467)
[467, 67, 46, 7, 4]
>>> list_truncated_nums(58)
[58, 8, 5]
"""
str_num = str(n)
list_nums = [n]
for i in range(1, len(str_num)):
list_nums.append(int(str_num[i:]))
list_nums.append(int(str_num[:-i]))
return list_nums
def validate(n: int) -> bool:
"""
To optimize the approach, we will rule out the numbers above 1000,
whose first or last three digits are not prime
>>> validate(74679)
False
>>> validate(235693)
False
>>> validate(3797)
True
"""
if len(str(n)) > 3:
if not is_prime(int(str(n)[-3:])) or not is_prime(int(str(n)[:3])):
return False
return True
def compute_truncated_primes(count: int = 11) -> list[int]:
"""
Returns the list of truncated primes
>>> compute_truncated_primes(11)
[23, 37, 53, 73, 313, 317, 373, 797, 3137, 3797, 739397]
"""
list_truncated_primes = []
num = 13
while len(list_truncated_primes) != count:
if validate(num):
list_nums = list_truncated_nums(num)
if all(is_prime(i) for i in list_nums):
list_truncated_primes.append(num)
num += 2
return list_truncated_primes
def solution() -> int:
"""
Returns the sum of truncated primes
"""
return sum(compute_truncated_primes(11))
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| """
The number 3797 has an interesting property. Being prime itself, it is possible
to continuously remove digits from left to right, and remain prime at each stage:
3797, 797, 97, and 7. Similarly we can work from right to left: 3797, 379, 37, and 3.
Find the sum of the only eleven primes that are both truncatable from left to right
and right to left.
NOTE: 2, 3, 5, and 7 are not considered to be truncatable primes.
"""
from __future__ import annotations
seive = [True] * 1000001
seive[1] = False
i = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
seive[j] = False
i += 1
def is_prime(n: int) -> bool:
"""
Returns True if n is prime,
False otherwise, for 1 <= n <= 1000000
>>> is_prime(87)
False
>>> is_prime(1)
False
>>> is_prime(25363)
False
"""
return seive[n]
def list_truncated_nums(n: int) -> list[int]:
"""
Returns a list of all left and right truncated numbers of n
>>> list_truncated_nums(927628)
[927628, 27628, 92762, 7628, 9276, 628, 927, 28, 92, 8, 9]
>>> list_truncated_nums(467)
[467, 67, 46, 7, 4]
>>> list_truncated_nums(58)
[58, 8, 5]
"""
str_num = str(n)
list_nums = [n]
for i in range(1, len(str_num)):
list_nums.append(int(str_num[i:]))
list_nums.append(int(str_num[:-i]))
return list_nums
def validate(n: int) -> bool:
"""
To optimize the approach, we will rule out the numbers above 1000,
whose first or last three digits are not prime
>>> validate(74679)
False
>>> validate(235693)
False
>>> validate(3797)
True
"""
if len(str(n)) > 3:
if not is_prime(int(str(n)[-3:])) or not is_prime(int(str(n)[:3])):
return False
return True
def compute_truncated_primes(count: int = 11) -> list[int]:
"""
Returns the list of truncated primes
>>> compute_truncated_primes(11)
[23, 37, 53, 73, 313, 317, 373, 797, 3137, 3797, 739397]
"""
list_truncated_primes = []
num = 13
while len(list_truncated_primes) != count:
if validate(num):
list_nums = list_truncated_nums(num)
if all(is_prime(i) for i in list_nums):
list_truncated_primes.append(num)
num += 2
return list_truncated_primes
def solution() -> int:
"""
Returns the sum of truncated primes
"""
return sum(compute_truncated_primes(11))
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 188: https://projecteuler.net/problem=188
The hyperexponentiation of a number
The hyperexponentiation or tetration of a number a by a positive integer b,
denoted by a↑↑b or b^a, is recursively defined by:
a↑↑1 = a,
a↑↑(k+1) = a(a↑↑k).
Thus we have e.g. 3↑↑2 = 3^3 = 27, hence 3↑↑3 = 3^27 = 7625597484987 and
3↑↑4 is roughly 103.6383346400240996*10^12.
Find the last 8 digits of 1777↑↑1855.
References:
- https://en.wikipedia.org/wiki/Tetration
"""
# small helper function for modular exponentiation
def _modexpt(base: int, exponent: int, modulo_value: int) -> int:
"""
Returns the modular exponentiation, that is the value
of `base ** exponent % modulo_value`, without calculating
the actual number.
>>> _modexpt(2, 4, 10)
6
>>> _modexpt(2, 1024, 100)
16
>>> _modexpt(13, 65535, 7)
6
"""
if exponent == 1:
return base
if exponent % 2 == 0:
x = _modexpt(base, exponent / 2, modulo_value) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(base, exponent - 1, modulo_value)) % modulo_value
def solution(base: int = 1777, height: int = 1855, digits: int = 8) -> int:
"""
Returns the last 8 digits of the hyperexponentiation of base by
height, i.e. the number base↑↑height:
>>> solution(base=3, height=2)
27
>>> solution(base=3, height=3)
97484987
>>> solution(base=123, height=456, digits=4)
2547
"""
# calculate base↑↑height by right-assiciative repeated modular
# exponentiation
result = base
for i in range(1, height):
result = _modexpt(base, result, 10 ** digits)
return result
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 188: https://projecteuler.net/problem=188
The hyperexponentiation of a number
The hyperexponentiation or tetration of a number a by a positive integer b,
denoted by a↑↑b or b^a, is recursively defined by:
a↑↑1 = a,
a↑↑(k+1) = a(a↑↑k).
Thus we have e.g. 3↑↑2 = 3^3 = 27, hence 3↑↑3 = 3^27 = 7625597484987 and
3↑↑4 is roughly 103.6383346400240996*10^12.
Find the last 8 digits of 1777↑↑1855.
References:
- https://en.wikipedia.org/wiki/Tetration
"""
# small helper function for modular exponentiation
def _modexpt(base: int, exponent: int, modulo_value: int) -> int:
"""
Returns the modular exponentiation, that is the value
of `base ** exponent % modulo_value`, without calculating
the actual number.
>>> _modexpt(2, 4, 10)
6
>>> _modexpt(2, 1024, 100)
16
>>> _modexpt(13, 65535, 7)
6
"""
if exponent == 1:
return base
if exponent % 2 == 0:
x = _modexpt(base, exponent / 2, modulo_value) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(base, exponent - 1, modulo_value)) % modulo_value
def solution(base: int = 1777, height: int = 1855, digits: int = 8) -> int:
"""
Returns the last 8 digits of the hyperexponentiation of base by
height, i.e. the number base↑↑height:
>>> solution(base=3, height=2)
27
>>> solution(base=3, height=3)
97484987
>>> solution(base=123, height=456, digits=4)
2547
"""
# calculate base↑↑height by right-assiciative repeated modular
# exponentiation
result = base
for i in range(1, height):
result = _modexpt(base, result, 10 ** digits)
return result
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Introspective Sort is hybrid sort (Quick Sort + Heap Sort + Insertion Sort)
if the size of the list is under 16, use insertion sort
https://en.wikipedia.org/wiki/Introsort
"""
import math
def insertion_sort(array: list, start: int = 0, end: int = 0) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> insertion_sort(array, 0, len(array))
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
end = end or len(array)
for i in range(start, end):
temp_index = i
temp_index_value = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
array[temp_index] = array[temp_index - 1]
temp_index -= 1
array[temp_index] = temp_index_value
return array
def heapify(array: list, index: int, heap_size: int) -> None: # Max Heap
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> heapify(array, len(array) // 2 ,len(array))
"""
largest = index
left_index = 2 * index + 1 # Left Node
right_index = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
largest = left_index
if right_index < heap_size and array[largest] < array[right_index]:
largest = right_index
if largest != index:
array[index], array[largest] = array[largest], array[index]
heapify(array, largest, heap_size)
def heap_sort(array: list) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> heap_sort(array)
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
n = len(array)
for i in range(n // 2, -1, -1):
heapify(array, i, n)
for i in range(n - 1, 0, -1):
array[i], array[0] = array[0], array[i]
heapify(array, 0, i)
return array
def median_of_3(
array: list, first_index: int, middle_index: int, last_index: int
) -> int:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> median_of_3(array, 0, 0 + ((len(array) - 0) // 2) + 1, len(array) - 1)
12
"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def partition(array: list, low: int, high: int, pivot: int) -> int:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> partition(array, 0, len(array), 12)
8
"""
i = low
j = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
array[i], array[j] = array[j], array[i]
i += 1
def sort(array: list) -> list:
"""
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> sort([4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12])
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
>>> sort([-1, -5, -3, -13, -44])
[-44, -13, -5, -3, -1]
>>> sort([])
[]
>>> sort([5])
[5]
>>> sort([-3, 0, -7, 6, 23, -34])
[-34, -7, -3, 0, 6, 23]
>>> sort([1.7, 1.0, 3.3, 2.1, 0.3 ])
[0.3, 1.0, 1.7, 2.1, 3.3]
>>> sort(['d', 'a', 'b', 'e', 'c'])
['a', 'b', 'c', 'd', 'e']
"""
if len(array) == 0:
return array
max_depth = 2 * math.ceil(math.log2(len(array)))
size_threshold = 16
return intro_sort(array, 0, len(array), size_threshold, max_depth)
def intro_sort(
array: list, start: int, end: int, size_threshold: int, max_depth: int
) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> max_depth = 2 * math.ceil(math.log2(len(array)))
>>> intro_sort(array, 0, len(array), 16, max_depth)
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(array)
max_depth -= 1
pivot = median_of_3(array, start, start + ((end - start) // 2) + 1, end - 1)
p = partition(array, start, end, pivot)
intro_sort(array, p, end, size_threshold, max_depth)
end = p
return insertion_sort(array, start, end)
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma : ").strip()
unsorted = [float(item) for item in user_input.split(",")]
print(sort(unsorted))
| """
Introspective Sort is hybrid sort (Quick Sort + Heap Sort + Insertion Sort)
if the size of the list is under 16, use insertion sort
https://en.wikipedia.org/wiki/Introsort
"""
import math
def insertion_sort(array: list, start: int = 0, end: int = 0) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> insertion_sort(array, 0, len(array))
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
end = end or len(array)
for i in range(start, end):
temp_index = i
temp_index_value = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
array[temp_index] = array[temp_index - 1]
temp_index -= 1
array[temp_index] = temp_index_value
return array
def heapify(array: list, index: int, heap_size: int) -> None: # Max Heap
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> heapify(array, len(array) // 2 ,len(array))
"""
largest = index
left_index = 2 * index + 1 # Left Node
right_index = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
largest = left_index
if right_index < heap_size and array[largest] < array[right_index]:
largest = right_index
if largest != index:
array[index], array[largest] = array[largest], array[index]
heapify(array, largest, heap_size)
def heap_sort(array: list) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> heap_sort(array)
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
n = len(array)
for i in range(n // 2, -1, -1):
heapify(array, i, n)
for i in range(n - 1, 0, -1):
array[i], array[0] = array[0], array[i]
heapify(array, 0, i)
return array
def median_of_3(
array: list, first_index: int, middle_index: int, last_index: int
) -> int:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> median_of_3(array, 0, 0 + ((len(array) - 0) // 2) + 1, len(array) - 1)
12
"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def partition(array: list, low: int, high: int, pivot: int) -> int:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> partition(array, 0, len(array), 12)
8
"""
i = low
j = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
array[i], array[j] = array[j], array[i]
i += 1
def sort(array: list) -> list:
"""
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> sort([4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12])
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
>>> sort([-1, -5, -3, -13, -44])
[-44, -13, -5, -3, -1]
>>> sort([])
[]
>>> sort([5])
[5]
>>> sort([-3, 0, -7, 6, 23, -34])
[-34, -7, -3, 0, 6, 23]
>>> sort([1.7, 1.0, 3.3, 2.1, 0.3 ])
[0.3, 1.0, 1.7, 2.1, 3.3]
>>> sort(['d', 'a', 'b', 'e', 'c'])
['a', 'b', 'c', 'd', 'e']
"""
if len(array) == 0:
return array
max_depth = 2 * math.ceil(math.log2(len(array)))
size_threshold = 16
return intro_sort(array, 0, len(array), size_threshold, max_depth)
def intro_sort(
array: list, start: int, end: int, size_threshold: int, max_depth: int
) -> list:
"""
>>> array = [4, 2, 6, 8, 1, 7, 8, 22, 14, 56, 27, 79, 23, 45, 14, 12]
>>> max_depth = 2 * math.ceil(math.log2(len(array)))
>>> intro_sort(array, 0, len(array), 16, max_depth)
[1, 2, 4, 6, 7, 8, 8, 12, 14, 14, 22, 23, 27, 45, 56, 79]
"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(array)
max_depth -= 1
pivot = median_of_3(array, start, start + ((end - start) // 2) + 1, end - 1)
p = partition(array, start, end, pivot)
intro_sort(array, p, end, size_threshold, max_depth)
end = p
return insertion_sort(array, start, end)
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma : ").strip()
unsorted = [float(item) for item in user_input.split(",")]
print(sort(unsorted))
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Chinese Remainder Theorem:
GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )
If GCD(a,b) = 1, then for any remainder ra modulo a and any remainder rb modulo b
there exists integer n, such that n = ra (mod a) and n = ra(mod b). If n1 and n2 are
two such integers, then n1=n2(mod ab)
Algorithm :
1. Use extended euclid algorithm to find x,y such that a*x + b*y = 1
2. Take n = ra*by + rb*ax
"""
from typing import Tuple
# Extended Euclid
def extended_euclid(a: int, b: int) -> Tuple[int, int]:
"""
>>> extended_euclid(10, 6)
(-1, 2)
>>> extended_euclid(7, 5)
(-2, 3)
"""
if b == 0:
return (1, 0)
(x, y) = extended_euclid(b, a % b)
k = a // b
return (y, x - k * y)
# Uses ExtendedEuclid to find inverses
def chinese_remainder_theorem(n1: int, r1: int, n2: int, r2: int) -> int:
"""
>>> chinese_remainder_theorem(5,1,7,3)
31
Explanation : 31 is the smallest number such that
(i) When we divide it by 5, we get remainder 1
(ii) When we divide it by 7, we get remainder 3
>>> chinese_remainder_theorem(6,1,4,3)
14
"""
(x, y) = extended_euclid(n1, n2)
m = n1 * n2
n = r2 * x * n1 + r1 * y * n2
return (n % m + m) % m
# ----------SAME SOLUTION USING InvertModulo instead ExtendedEuclid----------------
# This function find the inverses of a i.e., a^(-1)
def invert_modulo(a: int, n: int) -> int:
"""
>>> invert_modulo(2, 5)
3
>>> invert_modulo(8,7)
1
"""
(b, x) = extended_euclid(a, n)
if b < 0:
b = (b % n + n) % n
return b
# Same a above using InvertingModulo
def chinese_remainder_theorem2(n1: int, r1: int, n2: int, r2: int) -> int:
"""
>>> chinese_remainder_theorem2(5,1,7,3)
31
>>> chinese_remainder_theorem2(6,1,4,3)
14
"""
x, y = invert_modulo(n1, n2), invert_modulo(n2, n1)
m = n1 * n2
n = r2 * x * n1 + r1 * y * n2
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="chinese_remainder_theorem", verbose=True)
testmod(name="chinese_remainder_theorem2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_euclid", verbose=True)
| """
Chinese Remainder Theorem:
GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )
If GCD(a,b) = 1, then for any remainder ra modulo a and any remainder rb modulo b
there exists integer n, such that n = ra (mod a) and n = ra(mod b). If n1 and n2 are
two such integers, then n1=n2(mod ab)
Algorithm :
1. Use extended euclid algorithm to find x,y such that a*x + b*y = 1
2. Take n = ra*by + rb*ax
"""
from typing import Tuple
# Extended Euclid
def extended_euclid(a: int, b: int) -> Tuple[int, int]:
"""
>>> extended_euclid(10, 6)
(-1, 2)
>>> extended_euclid(7, 5)
(-2, 3)
"""
if b == 0:
return (1, 0)
(x, y) = extended_euclid(b, a % b)
k = a // b
return (y, x - k * y)
# Uses ExtendedEuclid to find inverses
def chinese_remainder_theorem(n1: int, r1: int, n2: int, r2: int) -> int:
"""
>>> chinese_remainder_theorem(5,1,7,3)
31
Explanation : 31 is the smallest number such that
(i) When we divide it by 5, we get remainder 1
(ii) When we divide it by 7, we get remainder 3
>>> chinese_remainder_theorem(6,1,4,3)
14
"""
(x, y) = extended_euclid(n1, n2)
m = n1 * n2
n = r2 * x * n1 + r1 * y * n2
return (n % m + m) % m
# ----------SAME SOLUTION USING InvertModulo instead ExtendedEuclid----------------
# This function find the inverses of a i.e., a^(-1)
def invert_modulo(a: int, n: int) -> int:
"""
>>> invert_modulo(2, 5)
3
>>> invert_modulo(8,7)
1
"""
(b, x) = extended_euclid(a, n)
if b < 0:
b = (b % n + n) % n
return b
# Same a above using InvertingModulo
def chinese_remainder_theorem2(n1: int, r1: int, n2: int, r2: int) -> int:
"""
>>> chinese_remainder_theorem2(5,1,7,3)
31
>>> chinese_remainder_theorem2(6,1,4,3)
14
"""
x, y = invert_modulo(n1, n2), invert_modulo(n2, n1)
m = n1 * n2
n = r2 * x * n1 + r1 * y * n2
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="chinese_remainder_theorem", verbose=True)
testmod(name="chinese_remainder_theorem2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_euclid", verbose=True)
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def dencrypt(s: str, n: int = 13) -> str:
"""
https://en.wikipedia.org/wiki/ROT13
>>> msg = "My secret bank account number is 173-52946 so don't tell anyone!!"
>>> s = dencrypt(msg)
>>> s
"Zl frperg onax nppbhag ahzore vf 173-52946 fb qba'g gryy nalbar!!"
>>> dencrypt(s) == msg
True
"""
out = ""
for c in s:
if "A" <= c <= "Z":
out += chr(ord("A") + (ord(c) - ord("A") + n) % 26)
elif "a" <= c <= "z":
out += chr(ord("a") + (ord(c) - ord("a") + n) % 26)
else:
out += c
return out
def main() -> None:
s0 = input("Enter message: ")
s1 = dencrypt(s0, 13)
print("Encryption:", s1)
s2 = dencrypt(s1, 13)
print("Decryption: ", s2)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| def dencrypt(s: str, n: int = 13) -> str:
"""
https://en.wikipedia.org/wiki/ROT13
>>> msg = "My secret bank account number is 173-52946 so don't tell anyone!!"
>>> s = dencrypt(msg)
>>> s
"Zl frperg onax nppbhag ahzore vf 173-52946 fb qba'g gryy nalbar!!"
>>> dencrypt(s) == msg
True
"""
out = ""
for c in s:
if "A" <= c <= "Z":
out += chr(ord("A") + (ord(c) - ord("A") + n) % 26)
elif "a" <= c <= "z":
out += chr(ord("a") + (ord(c) - ord("a") + n) % 26)
else:
out += c
return out
def main() -> None:
s0 = input("Enter message: ")
s1 = dencrypt(s0, 13)
print("Encryption:", s1)
s2 = dencrypt(s1, 13)
print("Decryption: ", s2)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def max_difference(a: list[int]) -> tuple[int, int]:
"""
We are given an array A[1..n] of integers, n >= 1. We want to
find a pair of indices (i, j) such that
1 <= i <= j <= n and A[j] - A[i] is as large as possible.
Explanation:
https://www.geeksforgeeks.org/maximum-difference-between-two-elements/
>>> max_difference([5, 11, 2, 1, 7, 9, 0, 7])
(1, 9)
"""
# base case
if len(a) == 1:
return a[0], a[0]
else:
# split A into half.
first = a[: len(a) // 2]
second = a[len(a) // 2 :]
# 2 sub problems, 1/2 of original size.
small1, big1 = max_difference(first)
small2, big2 = max_difference(second)
# get min of first and max of second
# linear time
min_first = min(first)
max_second = max(second)
# 3 cases, either (small1, big1),
# (min_first, max_second), (small2, big2)
# constant comparisons
if big2 - small2 > max_second - min_first and big2 - small2 > big1 - small1:
return small2, big2
elif big1 - small1 > max_second - min_first:
return small1, big1
else:
return min_first, max_second
if __name__ == "__main__":
import doctest
doctest.testmod()
| def max_difference(a: list[int]) -> tuple[int, int]:
"""
We are given an array A[1..n] of integers, n >= 1. We want to
find a pair of indices (i, j) such that
1 <= i <= j <= n and A[j] - A[i] is as large as possible.
Explanation:
https://www.geeksforgeeks.org/maximum-difference-between-two-elements/
>>> max_difference([5, 11, 2, 1, 7, 9, 0, 7])
(1, 9)
"""
# base case
if len(a) == 1:
return a[0], a[0]
else:
# split A into half.
first = a[: len(a) // 2]
second = a[len(a) // 2 :]
# 2 sub problems, 1/2 of original size.
small1, big1 = max_difference(first)
small2, big2 = max_difference(second)
# get min of first and max of second
# linear time
min_first = min(first)
max_second = max(second)
# 3 cases, either (small1, big1),
# (min_first, max_second), (small2, big2)
# constant comparisons
if big2 - small2 > max_second - min_first and big2 - small2 > big1 - small1:
return small2, big2
elif big1 - small1 > max_second - min_first:
return small1, big1
else:
return min_first, max_second
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import tensorflow as tf
from random import shuffle
from numpy import array
def TFKMeansCluster(vectors, noofclusters):
"""
K-Means Clustering using TensorFlow.
'vectors' should be a n*k 2-D NumPy array, where n is the number
of vectors of dimensionality k.
'noofclusters' should be an integer.
"""
noofclusters = int(noofclusters)
assert noofclusters < len(vectors)
# Find out the dimensionality
dim = len(vectors[0])
# Will help select random centroids from among the available vectors
vector_indices = list(range(len(vectors)))
shuffle(vector_indices)
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
graph = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
sess = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
centroids = [
tf.Variable(vectors[vector_indices[i]]) for i in range(noofclusters)
]
##These nodes will assign the centroid Variables the appropriate
##values
centroid_value = tf.placeholder("float64", [dim])
cent_assigns = []
for centroid in centroids:
cent_assigns.append(tf.assign(centroid, centroid_value))
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
assignments = [tf.Variable(0) for i in range(len(vectors))]
##These nodes will assign an assignment Variable the appropriate
##value
assignment_value = tf.placeholder("int32")
cluster_assigns = []
for assignment in assignments:
cluster_assigns.append(tf.assign(assignment, assignment_value))
##Now lets construct the node that will compute the mean
# The placeholder for the input
mean_input = tf.placeholder("float", [None, dim])
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
mean_op = tf.reduce_mean(mean_input, 0)
##Node for computing Euclidean distances
# Placeholders for input
v1 = tf.placeholder("float", [dim])
v2 = tf.placeholder("float", [dim])
euclid_dist = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(v1, v2), 2)))
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
centroid_distances = tf.placeholder("float", [noofclusters])
cluster_assignment = tf.argmin(centroid_distances, 0)
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
init_op = tf.initialize_all_variables()
# Initialize all variables
sess.run(init_op)
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
noofiterations = 100
for iteration_n in range(noofiterations):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(vectors)):
vect = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
distances = [
sess.run(euclid_dist, feed_dict={v1: vect, v2: sess.run(centroid)})
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
assignment = sess.run(
cluster_assignment, feed_dict={centroid_distances: distances}
)
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n], feed_dict={assignment_value: assignment}
)
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(noofclusters):
# Collect all the vectors assigned to this cluster
assigned_vects = [
vectors[i]
for i in range(len(vectors))
if sess.run(assignments[i]) == cluster_n
]
# Compute new centroid location
new_location = sess.run(
mean_op, feed_dict={mean_input: array(assigned_vects)}
)
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n], feed_dict={centroid_value: new_location}
)
# Return centroids and assignments
centroids = sess.run(centroids)
assignments = sess.run(assignments)
return centroids, assignments
| import tensorflow as tf
from random import shuffle
from numpy import array
def TFKMeansCluster(vectors, noofclusters):
"""
K-Means Clustering using TensorFlow.
'vectors' should be a n*k 2-D NumPy array, where n is the number
of vectors of dimensionality k.
'noofclusters' should be an integer.
"""
noofclusters = int(noofclusters)
assert noofclusters < len(vectors)
# Find out the dimensionality
dim = len(vectors[0])
# Will help select random centroids from among the available vectors
vector_indices = list(range(len(vectors)))
shuffle(vector_indices)
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
graph = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
sess = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
centroids = [
tf.Variable(vectors[vector_indices[i]]) for i in range(noofclusters)
]
##These nodes will assign the centroid Variables the appropriate
##values
centroid_value = tf.placeholder("float64", [dim])
cent_assigns = []
for centroid in centroids:
cent_assigns.append(tf.assign(centroid, centroid_value))
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
assignments = [tf.Variable(0) for i in range(len(vectors))]
##These nodes will assign an assignment Variable the appropriate
##value
assignment_value = tf.placeholder("int32")
cluster_assigns = []
for assignment in assignments:
cluster_assigns.append(tf.assign(assignment, assignment_value))
##Now lets construct the node that will compute the mean
# The placeholder for the input
mean_input = tf.placeholder("float", [None, dim])
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
mean_op = tf.reduce_mean(mean_input, 0)
##Node for computing Euclidean distances
# Placeholders for input
v1 = tf.placeholder("float", [dim])
v2 = tf.placeholder("float", [dim])
euclid_dist = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(v1, v2), 2)))
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
centroid_distances = tf.placeholder("float", [noofclusters])
cluster_assignment = tf.argmin(centroid_distances, 0)
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
init_op = tf.initialize_all_variables()
# Initialize all variables
sess.run(init_op)
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
noofiterations = 100
for iteration_n in range(noofiterations):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(vectors)):
vect = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
distances = [
sess.run(euclid_dist, feed_dict={v1: vect, v2: sess.run(centroid)})
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
assignment = sess.run(
cluster_assignment, feed_dict={centroid_distances: distances}
)
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n], feed_dict={assignment_value: assignment}
)
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(noofclusters):
# Collect all the vectors assigned to this cluster
assigned_vects = [
vectors[i]
for i in range(len(vectors))
if sess.run(assignments[i]) == cluster_n
]
# Compute new centroid location
new_location = sess.run(
mean_op, feed_dict={mean_input: array(assigned_vects)}
)
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n], feed_dict={centroid_value: new_location}
)
# Return centroids and assignments
centroids = sess.run(centroids)
assignments = sess.run(assignments)
return centroids, assignments
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ A Queue using a linked list like structure """
from typing import Any
class Node:
def __init__(self, data: Any) -> None:
self.data = data
self.next = None
def __str__(self) -> str:
return f"{self.data}"
class LinkedQueue:
"""
>>> queue = LinkedQueue()
>>> queue.is_empty()
True
>>> queue.put(5)
>>> queue.put(9)
>>> queue.put('python')
>>> queue.is_empty();
False
>>> queue.get()
5
>>> queue.put('algorithms')
>>> queue.get()
9
>>> queue.get()
'python'
>>> queue.get()
'algorithms'
>>> queue.is_empty()
True
>>> queue.get()
Traceback (most recent call last):
...
IndexError: dequeue from empty queue
"""
def __init__(self) -> None:
self.front = self.rear = None
def __iter__(self):
node = self.front
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
>>> queue = LinkedQueue()
>>> for i in range(1, 6):
... queue.put(i)
>>> len(queue)
5
>>> for i in range(1, 6):
... assert len(queue) == 6 - i
... _ = queue.get()
>>> len(queue)
0
"""
return len(tuple(iter(self)))
def __str__(self) -> str:
"""
>>> queue = LinkedQueue()
>>> for i in range(1, 4):
... queue.put(i)
>>> queue.put("Python")
>>> queue.put(3.14)
>>> queue.put(True)
>>> str(queue)
'1 <- 2 <- 3 <- Python <- 3.14 <- True'
"""
return " <- ".join(str(item) for item in self)
def is_empty(self) -> bool:
"""
>>> queue = LinkedQueue()
>>> queue.is_empty()
True
>>> for i in range(1, 6):
... queue.put(i)
>>> queue.is_empty()
False
"""
return len(self) == 0
def put(self, item) -> None:
"""
>>> queue = LinkedQueue()
>>> queue.get()
Traceback (most recent call last):
...
IndexError: dequeue from empty queue
>>> for i in range(1, 6):
... queue.put(i)
>>> str(queue)
'1 <- 2 <- 3 <- 4 <- 5'
"""
node = Node(item)
if self.is_empty():
self.front = self.rear = node
else:
assert isinstance(self.rear, Node)
self.rear.next = node
self.rear = node
def get(self) -> Any:
"""
>>> queue = LinkedQueue()
>>> queue.get()
Traceback (most recent call last):
...
IndexError: dequeue from empty queue
>>> queue = LinkedQueue()
>>> for i in range(1, 6):
... queue.put(i)
>>> for i in range(1, 6):
... assert queue.get() == i
>>> len(queue)
0
"""
if self.is_empty():
raise IndexError("dequeue from empty queue")
assert isinstance(self.front, Node)
node = self.front
self.front = self.front.next
if self.front is None:
self.rear = None
return node.data
def clear(self) -> None:
"""
>>> queue = LinkedQueue()
>>> for i in range(1, 6):
... queue.put(i)
>>> queue.clear()
>>> len(queue)
0
>>> str(queue)
''
"""
self.front = self.rear = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| """ A Queue using a linked list like structure """
from typing import Any
class Node:
def __init__(self, data: Any) -> None:
self.data = data
self.next = None
def __str__(self) -> str:
return f"{self.data}"
class LinkedQueue:
"""
>>> queue = LinkedQueue()
>>> queue.is_empty()
True
>>> queue.put(5)
>>> queue.put(9)
>>> queue.put('python')
>>> queue.is_empty();
False
>>> queue.get()
5
>>> queue.put('algorithms')
>>> queue.get()
9
>>> queue.get()
'python'
>>> queue.get()
'algorithms'
>>> queue.is_empty()
True
>>> queue.get()
Traceback (most recent call last):
...
IndexError: dequeue from empty queue
"""
def __init__(self) -> None:
self.front = self.rear = None
def __iter__(self):
node = self.front
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
>>> queue = LinkedQueue()
>>> for i in range(1, 6):
... queue.put(i)
>>> len(queue)
5
>>> for i in range(1, 6):
... assert len(queue) == 6 - i
... _ = queue.get()
>>> len(queue)
0
"""
return len(tuple(iter(self)))
def __str__(self) -> str:
"""
>>> queue = LinkedQueue()
>>> for i in range(1, 4):
... queue.put(i)
>>> queue.put("Python")
>>> queue.put(3.14)
>>> queue.put(True)
>>> str(queue)
'1 <- 2 <- 3 <- Python <- 3.14 <- True'
"""
return " <- ".join(str(item) for item in self)
def is_empty(self) -> bool:
"""
>>> queue = LinkedQueue()
>>> queue.is_empty()
True
>>> for i in range(1, 6):
... queue.put(i)
>>> queue.is_empty()
False
"""
return len(self) == 0
def put(self, item) -> None:
"""
>>> queue = LinkedQueue()
>>> queue.get()
Traceback (most recent call last):
...
IndexError: dequeue from empty queue
>>> for i in range(1, 6):
... queue.put(i)
>>> str(queue)
'1 <- 2 <- 3 <- 4 <- 5'
"""
node = Node(item)
if self.is_empty():
self.front = self.rear = node
else:
assert isinstance(self.rear, Node)
self.rear.next = node
self.rear = node
def get(self) -> Any:
"""
>>> queue = LinkedQueue()
>>> queue.get()
Traceback (most recent call last):
...
IndexError: dequeue from empty queue
>>> queue = LinkedQueue()
>>> for i in range(1, 6):
... queue.put(i)
>>> for i in range(1, 6):
... assert queue.get() == i
>>> len(queue)
0
"""
if self.is_empty():
raise IndexError("dequeue from empty queue")
assert isinstance(self.front, Node)
node = self.front
self.front = self.front.next
if self.front is None:
self.rear = None
return node.data
def clear(self) -> None:
"""
>>> queue = LinkedQueue()
>>> for i in range(1, 6):
... queue.put(i)
>>> queue.clear()
>>> len(queue)
0
>>> str(queue)
''
"""
self.front = self.rear = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
def climb_stairs(n: int) -> int:
"""
LeetCdoe No.70: Climbing Stairs
Distinct ways to climb a n step staircase where
each time you can either climb 1 or 2 steps.
Args:
n: number of steps of staircase
Returns:
Distinct ways to climb a n step staircase
Raises:
AssertionError: n not positive integer
>>> climb_stairs(3)
3
>>> climb_stairs(1)
1
>>> climb_stairs(-7) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError: n needs to be positive integer, your input -7
"""
assert (
isinstance(n, int) and n > 0
), f"n needs to be positive integer, your input {n}"
if n == 1:
return 1
dp = [0] * (n + 1)
dp[0], dp[1] = (1, 1)
for i in range(2, n + 1):
dp[i] = dp[i - 1] + dp[i - 2]
return dp[n]
if __name__ == "__main__":
import doctest
doctest.testmod()
| #!/usr/bin/env python3
def climb_stairs(n: int) -> int:
"""
LeetCdoe No.70: Climbing Stairs
Distinct ways to climb a n step staircase where
each time you can either climb 1 or 2 steps.
Args:
n: number of steps of staircase
Returns:
Distinct ways to climb a n step staircase
Raises:
AssertionError: n not positive integer
>>> climb_stairs(3)
3
>>> climb_stairs(1)
1
>>> climb_stairs(-7) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError: n needs to be positive integer, your input -7
"""
assert (
isinstance(n, int) and n > 0
), f"n needs to be positive integer, your input {n}"
if n == 1:
return 1
dp = [0] * (n + 1)
dp[0], dp[1] = (1, 1)
for i in range(2, n + 1):
dp[i] = dp[i - 1] + dp[i - 2]
return dp[n]
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
developed by: markmelnic
original repo: https://github.com/markmelnic/Scoring-Algorithm
Analyse data using a range based percentual proximity algorithm
and calculate the linear maximum likelihood estimation.
The basic principle is that all values supplied will be broken
down to a range from 0 to 1 and each column's score will be added
up to get the total score.
==========
Example for data of vehicles
price|mileage|registration_year
20k |60k |2012
22k |50k |2011
23k |90k |2015
16k |210k |2010
We want the vehicle with the lowest price,
lowest mileage but newest registration year.
Thus the weights for each column are as follows:
[0, 0, 1]
>>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
[[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
"""
def procentual_proximity(source_data: list, weights: list) -> list:
"""
weights - int list
possible values - 0 / 1
0 if lower values have higher weight in the data set
1 if higher values have higher weight in the data set
"""
# getting data
data_lists = []
for item in source_data:
for i in range(len(item)):
try:
data_lists[i].append(float(item[i]))
except IndexError:
# generate corresponding number of lists
data_lists.append([])
data_lists[i].append(float(item[i]))
score_lists = []
# calculating each score
for dlist, weight in zip(data_lists, weights):
mind = min(dlist)
maxd = max(dlist)
score = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)))
except ZeroDivisionError:
score.append(1)
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind))
except ZeroDivisionError:
score.append(0)
# weight not 0 or 1
else:
raise ValueError("Invalid weight of %f provided" % (weight))
score_lists.append(score)
# initialize final scores
final_scores = [0 for i in range(len(score_lists[0]))]
# generate final scores
for i, slist in enumerate(score_lists):
for j, ele in enumerate(slist):
final_scores[j] = final_scores[j] + ele
# append scores to source data
for i, ele in enumerate(final_scores):
source_data[i].append(ele)
return source_data
| """
developed by: markmelnic
original repo: https://github.com/markmelnic/Scoring-Algorithm
Analyse data using a range based percentual proximity algorithm
and calculate the linear maximum likelihood estimation.
The basic principle is that all values supplied will be broken
down to a range from 0 to 1 and each column's score will be added
up to get the total score.
==========
Example for data of vehicles
price|mileage|registration_year
20k |60k |2012
22k |50k |2011
23k |90k |2015
16k |210k |2010
We want the vehicle with the lowest price,
lowest mileage but newest registration year.
Thus the weights for each column are as follows:
[0, 0, 1]
>>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
[[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
"""
def procentual_proximity(source_data: list, weights: list) -> list:
"""
weights - int list
possible values - 0 / 1
0 if lower values have higher weight in the data set
1 if higher values have higher weight in the data set
"""
# getting data
data_lists = []
for item in source_data:
for i in range(len(item)):
try:
data_lists[i].append(float(item[i]))
except IndexError:
# generate corresponding number of lists
data_lists.append([])
data_lists[i].append(float(item[i]))
score_lists = []
# calculating each score
for dlist, weight in zip(data_lists, weights):
mind = min(dlist)
maxd = max(dlist)
score = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)))
except ZeroDivisionError:
score.append(1)
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind))
except ZeroDivisionError:
score.append(0)
# weight not 0 or 1
else:
raise ValueError("Invalid weight of %f provided" % (weight))
score_lists.append(score)
# initialize final scores
final_scores = [0 for i in range(len(score_lists[0]))]
# generate final scores
for i, slist in enumerate(score_lists):
for j, ele in enumerate(slist):
final_scores[j] = final_scores[j] + ele
# append scores to source data
for i, ele in enumerate(final_scores):
source_data[i].append(ele)
return source_data
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
== Krishnamurthy Number ==
It is also known as Peterson Number
A Krishnamurthy Number is a number whose sum of the
factorial of the digits equals to the original
number itself.
For example: 145 = 1! + 4! + 5!
So, 145 is a Krishnamurthy Number
"""
def factorial(digit: int) -> int:
"""
>>> factorial(3)
6
>>> factorial(0)
1
>>> factorial(5)
120
"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1))
def krishnamurthy(number: int) -> bool:
"""
>>> krishnamurthy(145)
True
>>> krishnamurthy(240)
False
>>> krishnamurthy(1)
True
"""
factSum = 0
duplicate = number
while duplicate > 0:
duplicate, digit = divmod(duplicate, 10)
factSum += factorial(digit)
return factSum == number
if __name__ == "__main__":
print("Program to check whether a number is a Krisnamurthy Number or not.")
number = int(input("Enter number: ").strip())
print(
f"{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."
)
| """
== Krishnamurthy Number ==
It is also known as Peterson Number
A Krishnamurthy Number is a number whose sum of the
factorial of the digits equals to the original
number itself.
For example: 145 = 1! + 4! + 5!
So, 145 is a Krishnamurthy Number
"""
def factorial(digit: int) -> int:
"""
>>> factorial(3)
6
>>> factorial(0)
1
>>> factorial(5)
120
"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1))
def krishnamurthy(number: int) -> bool:
"""
>>> krishnamurthy(145)
True
>>> krishnamurthy(240)
False
>>> krishnamurthy(1)
True
"""
factSum = 0
duplicate = number
while duplicate > 0:
duplicate, digit = divmod(duplicate, 10)
factSum += factorial(digit)
return factSum == number
if __name__ == "__main__":
print("Program to check whether a number is a Krisnamurthy Number or not.")
number = int(input("Enter number: ").strip())
print(
f"{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."
)
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def decrypt(message: str) -> None:
"""
>>> decrypt('TMDETUX PMDVU')
Decryption using Key #0: TMDETUX PMDVU
Decryption using Key #1: SLCDSTW OLCUT
Decryption using Key #2: RKBCRSV NKBTS
Decryption using Key #3: QJABQRU MJASR
Decryption using Key #4: PIZAPQT LIZRQ
Decryption using Key #5: OHYZOPS KHYQP
Decryption using Key #6: NGXYNOR JGXPO
Decryption using Key #7: MFWXMNQ IFWON
Decryption using Key #8: LEVWLMP HEVNM
Decryption using Key #9: KDUVKLO GDUML
Decryption using Key #10: JCTUJKN FCTLK
Decryption using Key #11: IBSTIJM EBSKJ
Decryption using Key #12: HARSHIL DARJI
Decryption using Key #13: GZQRGHK CZQIH
Decryption using Key #14: FYPQFGJ BYPHG
Decryption using Key #15: EXOPEFI AXOGF
Decryption using Key #16: DWNODEH ZWNFE
Decryption using Key #17: CVMNCDG YVMED
Decryption using Key #18: BULMBCF XULDC
Decryption using Key #19: ATKLABE WTKCB
Decryption using Key #20: ZSJKZAD VSJBA
Decryption using Key #21: YRIJYZC URIAZ
Decryption using Key #22: XQHIXYB TQHZY
Decryption using Key #23: WPGHWXA SPGYX
Decryption using Key #24: VOFGVWZ ROFXW
Decryption using Key #25: UNEFUVY QNEWV
"""
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
for key in range(len(LETTERS)):
translated = ""
for symbol in message:
if symbol in LETTERS:
num = LETTERS.find(symbol)
num = num - key
if num < 0:
num = num + len(LETTERS)
translated = translated + LETTERS[num]
else:
translated = translated + symbol
print(f"Decryption using Key #{key}: {translated}")
def main() -> None:
message = input("Encrypted message: ")
message = message.upper()
decrypt(message)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| def decrypt(message: str) -> None:
"""
>>> decrypt('TMDETUX PMDVU')
Decryption using Key #0: TMDETUX PMDVU
Decryption using Key #1: SLCDSTW OLCUT
Decryption using Key #2: RKBCRSV NKBTS
Decryption using Key #3: QJABQRU MJASR
Decryption using Key #4: PIZAPQT LIZRQ
Decryption using Key #5: OHYZOPS KHYQP
Decryption using Key #6: NGXYNOR JGXPO
Decryption using Key #7: MFWXMNQ IFWON
Decryption using Key #8: LEVWLMP HEVNM
Decryption using Key #9: KDUVKLO GDUML
Decryption using Key #10: JCTUJKN FCTLK
Decryption using Key #11: IBSTIJM EBSKJ
Decryption using Key #12: HARSHIL DARJI
Decryption using Key #13: GZQRGHK CZQIH
Decryption using Key #14: FYPQFGJ BYPHG
Decryption using Key #15: EXOPEFI AXOGF
Decryption using Key #16: DWNODEH ZWNFE
Decryption using Key #17: CVMNCDG YVMED
Decryption using Key #18: BULMBCF XULDC
Decryption using Key #19: ATKLABE WTKCB
Decryption using Key #20: ZSJKZAD VSJBA
Decryption using Key #21: YRIJYZC URIAZ
Decryption using Key #22: XQHIXYB TQHZY
Decryption using Key #23: WPGHWXA SPGYX
Decryption using Key #24: VOFGVWZ ROFXW
Decryption using Key #25: UNEFUVY QNEWV
"""
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
for key in range(len(LETTERS)):
translated = ""
for symbol in message:
if symbol in LETTERS:
num = LETTERS.find(symbol)
num = num - key
if num < 0:
num = num + len(LETTERS)
translated = translated + LETTERS[num]
else:
translated = translated + symbol
print(f"Decryption using Key #{key}: {translated}")
def main() -> None:
message = input("Encrypted message: ")
message = message.upper()
decrypt(message)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Check whether Graph is Bipartite or Not using DFS
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def check_bipartite_dfs(graph):
visited = [False] * len(graph)
color = [-1] * len(graph)
def dfs(v, c):
visited[v] = True
color[v] = c
for u in graph[v]:
if not visited[u]:
dfs(u, 1 - c)
for i in range(len(graph)):
if not visited[i]:
dfs(i, 0)
for i in range(len(graph)):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
graph = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| # Check whether Graph is Bipartite or Not using DFS
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def check_bipartite_dfs(graph):
visited = [False] * len(graph)
color = [-1] * len(graph)
def dfs(v, c):
visited[v] = True
color[v] = c
for u in graph[v]:
if not visited[u]:
dfs(u, 1 - c)
for i in range(len(graph)):
if not visited[i]:
dfs(i, 0)
for i in range(len(graph)):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
graph = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Slowsort is a sorting algorithm. It is of humorous nature and not useful.
It's based on the principle of multiply and surrender,
a tongue-in-cheek joke of divide and conquer.
It was published in 1986 by Andrei Broder and Jorge Stolfi
in their paper Pessimal Algorithms and Simplexity Analysis
(a parody of optimal algorithms and complexity analysis).
Source: https://en.wikipedia.org/wiki/Slowsort
"""
from typing import Optional
def slowsort(
sequence: list, start: Optional[int] = None, end: Optional[int] = None
) -> None:
"""
Sorts sequence[start..end] (both inclusive) in-place.
start defaults to 0 if not given.
end defaults to len(sequence) - 1 if not given.
It returns None.
>>> seq = [1, 6, 2, 5, 3, 4, 4, 5]; slowsort(seq); seq
[1, 2, 3, 4, 4, 5, 5, 6]
>>> seq = []; slowsort(seq); seq
[]
>>> seq = [2]; slowsort(seq); seq
[2]
>>> seq = [1, 2, 3, 4]; slowsort(seq); seq
[1, 2, 3, 4]
>>> seq = [4, 3, 2, 1]; slowsort(seq); seq
[1, 2, 3, 4]
>>> seq = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]; slowsort(seq, 2, 7); seq
[9, 8, 2, 3, 4, 5, 6, 7, 1, 0]
>>> seq = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]; slowsort(seq, end = 4); seq
[5, 6, 7, 8, 9, 4, 3, 2, 1, 0]
>>> seq = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]; slowsort(seq, start = 5); seq
[9, 8, 7, 6, 5, 0, 1, 2, 3, 4]
"""
if start is None:
start = 0
if end is None:
end = len(sequence) - 1
if start >= end:
return
mid = (start + end) // 2
slowsort(sequence, start, mid)
slowsort(sequence, mid + 1, end)
if sequence[end] < sequence[mid]:
sequence[end], sequence[mid] = sequence[mid], sequence[end]
slowsort(sequence, start, end - 1)
if __name__ == "__main__":
from doctest import testmod
testmod()
| """
Slowsort is a sorting algorithm. It is of humorous nature and not useful.
It's based on the principle of multiply and surrender,
a tongue-in-cheek joke of divide and conquer.
It was published in 1986 by Andrei Broder and Jorge Stolfi
in their paper Pessimal Algorithms and Simplexity Analysis
(a parody of optimal algorithms and complexity analysis).
Source: https://en.wikipedia.org/wiki/Slowsort
"""
from typing import Optional
def slowsort(
sequence: list, start: Optional[int] = None, end: Optional[int] = None
) -> None:
"""
Sorts sequence[start..end] (both inclusive) in-place.
start defaults to 0 if not given.
end defaults to len(sequence) - 1 if not given.
It returns None.
>>> seq = [1, 6, 2, 5, 3, 4, 4, 5]; slowsort(seq); seq
[1, 2, 3, 4, 4, 5, 5, 6]
>>> seq = []; slowsort(seq); seq
[]
>>> seq = [2]; slowsort(seq); seq
[2]
>>> seq = [1, 2, 3, 4]; slowsort(seq); seq
[1, 2, 3, 4]
>>> seq = [4, 3, 2, 1]; slowsort(seq); seq
[1, 2, 3, 4]
>>> seq = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]; slowsort(seq, 2, 7); seq
[9, 8, 2, 3, 4, 5, 6, 7, 1, 0]
>>> seq = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]; slowsort(seq, end = 4); seq
[5, 6, 7, 8, 9, 4, 3, 2, 1, 0]
>>> seq = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]; slowsort(seq, start = 5); seq
[9, 8, 7, 6, 5, 0, 1, 2, 3, 4]
"""
if start is None:
start = 0
if end is None:
end = len(sequence) - 1
if start >= end:
return
mid = (start + end) // 2
slowsort(sequence, start, mid)
slowsort(sequence, mid + 1, end)
if sequence[end] < sequence[mid]:
sequence[end], sequence[mid] = sequence[mid], sequence[end]
slowsort(sequence, start, end - 1)
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def bubble_sort(list_data: list, length: int = 0) -> list:
"""
It is similar is bubble sort but recursive.
:param list_data: mutable ordered sequence of elements
:param length: length of list data
:return: the same list in ascending order
>>> bubble_sort([0, 5, 2, 3, 2], 5)
[0, 2, 2, 3, 5]
>>> bubble_sort([], 0)
[]
>>> bubble_sort([-2, -45, -5], 3)
[-45, -5, -2]
>>> bubble_sort([-23, 0, 6, -4, 34], 5)
[-23, -4, 0, 6, 34]
>>> bubble_sort([-23, 0, 6, -4, 34], 5) == sorted([-23, 0, 6, -4, 34])
True
>>> bubble_sort(['z','a','y','b','x','c'], 6)
['a', 'b', 'c', 'x', 'y', 'z']
>>> bubble_sort([1.1, 3.3, 5.5, 7.7, 2.2, 4.4, 6.6])
[1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7]
"""
length = length or len(list_data)
swapped = False
for i in range(length - 1):
if list_data[i] > list_data[i + 1]:
list_data[i], list_data[i + 1] = list_data[i + 1], list_data[i]
swapped = True
return list_data if not swapped else bubble_sort(list_data, length - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| def bubble_sort(list_data: list, length: int = 0) -> list:
"""
It is similar is bubble sort but recursive.
:param list_data: mutable ordered sequence of elements
:param length: length of list data
:return: the same list in ascending order
>>> bubble_sort([0, 5, 2, 3, 2], 5)
[0, 2, 2, 3, 5]
>>> bubble_sort([], 0)
[]
>>> bubble_sort([-2, -45, -5], 3)
[-45, -5, -2]
>>> bubble_sort([-23, 0, 6, -4, 34], 5)
[-23, -4, 0, 6, 34]
>>> bubble_sort([-23, 0, 6, -4, 34], 5) == sorted([-23, 0, 6, -4, 34])
True
>>> bubble_sort(['z','a','y','b','x','c'], 6)
['a', 'b', 'c', 'x', 'y', 'z']
>>> bubble_sort([1.1, 3.3, 5.5, 7.7, 2.2, 4.4, 6.6])
[1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7]
"""
length = length or len(list_data)
swapped = False
for i in range(length - 1):
if list_data[i] > list_data[i + 1]:
list_data[i], list_data[i + 1] = list_data[i + 1], list_data[i]
swapped = True
return list_data if not swapped else bubble_sort(list_data, length - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 72 Counting fractions: https://projecteuler.net/problem=72
Description:
Consider the fraction, n/d, where n and d are positive integers. If n<d and HCF(n,d)=1,
it is called a reduced proper fraction.
If we list the set of reduced proper fractions for d ≤ 8 in ascending order of size, we
get: 1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2, 4/7, 3/5, 5/8, 2/3, 5/7,
3/4, 4/5, 5/6, 6/7, 7/8
It can be seen that there are 21 elements in this set.
How many elements would be contained in the set of reduced proper fractions for
d ≤ 1,000,000?
Solution:
Number of numbers between 1 and n that are coprime to n is given by the Euler's Totient
function, phi(n). So, the answer is simply the sum of phi(n) for 2 <= n <= 1,000,000
Sum of phi(d), for all d|n = n. This result can be used to find phi(n) using a sieve.
Time: 3.5 sec
"""
def solution(limit: int = 1_000_000) -> int:
"""
Returns an integer, the solution to the problem
>>> solution(10)
31
>>> solution(100)
3043
>>> solution(1_000)
304191
"""
phi = [i - 1 for i in range(limit + 1)]
for i in range(2, limit + 1):
for j in range(2 * i, limit + 1, i):
phi[j] -= phi[i]
return sum(phi[2 : limit + 1])
if __name__ == "__main__":
print(solution())
| """
Problem 72 Counting fractions: https://projecteuler.net/problem=72
Description:
Consider the fraction, n/d, where n and d are positive integers. If n<d and HCF(n,d)=1,
it is called a reduced proper fraction.
If we list the set of reduced proper fractions for d ≤ 8 in ascending order of size, we
get: 1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2, 4/7, 3/5, 5/8, 2/3, 5/7,
3/4, 4/5, 5/6, 6/7, 7/8
It can be seen that there are 21 elements in this set.
How many elements would be contained in the set of reduced proper fractions for
d ≤ 1,000,000?
Solution:
Number of numbers between 1 and n that are coprime to n is given by the Euler's Totient
function, phi(n). So, the answer is simply the sum of phi(n) for 2 <= n <= 1,000,000
Sum of phi(d), for all d|n = n. This result can be used to find phi(n) using a sieve.
Time: 3.5 sec
"""
def solution(limit: int = 1_000_000) -> int:
"""
Returns an integer, the solution to the problem
>>> solution(10)
31
>>> solution(100)
3043
>>> solution(1_000)
304191
"""
phi = [i - 1 for i in range(limit + 1)]
for i in range(2, limit + 1):
for j in range(2 * i, limit + 1, i):
phi[j] -= phi[i]
return sum(phi[2 : limit + 1])
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Create a Long Short Term Memory (LSTM) network model
An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
* http://colah.github.io/posts/2015-08-Understanding-LSTMs
* https://en.wikipedia.org/wiki/Long_short-term_memory
"""
import numpy as np
import pandas as pd
from keras.layers import LSTM, Dense
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
if __name__ == "__main__":
"""
First part of building a model is to get the data and prepare
it for our model. You can use any dataset for stock prediction
make sure you set the price column on line number 21. Here we
use a dataset which have the price on 3rd column.
"""
df = pd.read_csv("sample_data.csv", header=None)
len_data = df.shape[:1][0]
# If you're using some other dataset input the target column
actual_data = df.iloc[:, 1:2]
actual_data = actual_data.values.reshape(len_data, 1)
actual_data = MinMaxScaler().fit_transform(actual_data)
look_back = 10
forward_days = 5
periods = 20
division = len_data - periods * look_back
train_data = actual_data[:division]
test_data = actual_data[division - look_back :]
train_x, train_y = [], []
test_x, test_y = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
x_train = np.array(train_x)
x_test = np.array(test_x)
y_train = np.array([list(i.ravel()) for i in train_y])
y_test = np.array([list(i.ravel()) for i in test_y])
model = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="mean_squared_error", optimizer="adam")
history = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
pred = model.predict(x_test)
| """
Create a Long Short Term Memory (LSTM) network model
An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
* http://colah.github.io/posts/2015-08-Understanding-LSTMs
* https://en.wikipedia.org/wiki/Long_short-term_memory
"""
import numpy as np
import pandas as pd
from keras.layers import LSTM, Dense
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
if __name__ == "__main__":
"""
First part of building a model is to get the data and prepare
it for our model. You can use any dataset for stock prediction
make sure you set the price column on line number 21. Here we
use a dataset which have the price on 3rd column.
"""
df = pd.read_csv("sample_data.csv", header=None)
len_data = df.shape[:1][0]
# If you're using some other dataset input the target column
actual_data = df.iloc[:, 1:2]
actual_data = actual_data.values.reshape(len_data, 1)
actual_data = MinMaxScaler().fit_transform(actual_data)
look_back = 10
forward_days = 5
periods = 20
division = len_data - periods * look_back
train_data = actual_data[:division]
test_data = actual_data[division - look_back :]
train_x, train_y = [], []
test_x, test_y = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
x_train = np.array(train_x)
x_test = np.array(test_x)
y_train = np.array([list(i.ravel()) for i in train_y])
y_test = np.array([list(i.ravel()) for i in test_y])
model = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="mean_squared_error", optimizer="adam")
history = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
pred = model.predict(x_test)
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| ### Computer Vision
Computer vision is a field of computer science that works on enabling computers to see,
identify and process images in the same way that human vision does, and then provide appropriate output.
It is like imparting human intelligence and instincts to a computer.
Image processing and computer vision are a little different from each other. Image processing means applying some algorithms for transforming image from one form to the other like smoothing, contrasting, stretching, etc.
While computer vision comes from modelling image processing using the techniques of machine learning, computer vision applies machine learning to recognize patterns for interpretation of images (much like the process of visual reasoning of human vision).
| ### Computer Vision
Computer vision is a field of computer science that works on enabling computers to see,
identify and process images in the same way that human vision does, and then provide appropriate output.
It is like imparting human intelligence and instincts to a computer.
Image processing and computer vision are a little different from each other. Image processing means applying some algorithms for transforming image from one form to the other like smoothing, contrasting, stretching, etc.
While computer vision comes from modelling image processing using the techniques of machine learning, computer vision applies machine learning to recognize patterns for interpretation of images (much like the process of visual reasoning of human vision).
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Implements a disjoint set using Lists and some added heuristics for efficiency
Union by Rank Heuristic and Path Compression
"""
class DisjointSet:
def __init__(self, set_counts: list) -> None:
"""
Initialize with a list of the number of items in each set
and with rank = 1 for each set
"""
self.set_counts = set_counts
self.max_set = max(set_counts)
num_sets = len(set_counts)
self.ranks = [1] * num_sets
self.parents = list(range(num_sets))
def merge(self, src: int, dst: int) -> bool:
"""
Merge two sets together using Union by rank heuristic
Return True if successful
Merge two disjoint sets
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.merge(0, 2)
True
>>> A.merge(0, 1)
False
"""
src_parent = self.get_parent(src)
dst_parent = self.get_parent(dst)
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
self.set_counts[src_parent] = 0
self.parents[src_parent] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
joined_set_size = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
self.set_counts[dst_parent] = 0
self.parents[dst_parent] = src_parent
joined_set_size = self.set_counts[src_parent]
self.max_set = max(self.max_set, joined_set_size)
return True
def get_parent(self, disj_set: int) -> int:
"""
Find the Parent of a given set
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.get_parent(0)
0
>>> A.get_parent(1)
2
"""
if self.parents[disj_set] == disj_set:
return disj_set
self.parents[disj_set] = self.get_parent(self.parents[disj_set])
return self.parents[disj_set]
| """
Implements a disjoint set using Lists and some added heuristics for efficiency
Union by Rank Heuristic and Path Compression
"""
class DisjointSet:
def __init__(self, set_counts: list) -> None:
"""
Initialize with a list of the number of items in each set
and with rank = 1 for each set
"""
self.set_counts = set_counts
self.max_set = max(set_counts)
num_sets = len(set_counts)
self.ranks = [1] * num_sets
self.parents = list(range(num_sets))
def merge(self, src: int, dst: int) -> bool:
"""
Merge two sets together using Union by rank heuristic
Return True if successful
Merge two disjoint sets
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.merge(0, 2)
True
>>> A.merge(0, 1)
False
"""
src_parent = self.get_parent(src)
dst_parent = self.get_parent(dst)
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
self.set_counts[src_parent] = 0
self.parents[src_parent] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
joined_set_size = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
self.set_counts[dst_parent] = 0
self.parents[dst_parent] = src_parent
joined_set_size = self.set_counts[src_parent]
self.max_set = max(self.max_set, joined_set_size)
return True
def get_parent(self, disj_set: int) -> int:
"""
Find the Parent of a given set
>>> A = DisjointSet([1, 1, 1])
>>> A.merge(1, 2)
True
>>> A.get_parent(0)
0
>>> A.get_parent(1)
2
"""
if self.parents[disj_set] == disj_set:
return disj_set
self.parents[disj_set] = self.get_parent(self.parents[disj_set])
return self.parents[disj_set]
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Given an array of integers, return indices of the two numbers such that they add up to
a specific target.
You may assume that each input would have exactly one solution, and you may not use the
same element twice.
Example:
Given nums = [2, 7, 11, 15], target = 9,
Because nums[0] + nums[1] = 2 + 7 = 9,
return [0, 1].
"""
from __future__ import annotations
def two_sum(nums: list[int], target: int) -> list[int]:
"""
>>> two_sum([2, 7, 11, 15], 9)
[0, 1]
>>> two_sum([15, 2, 11, 7], 13)
[1, 2]
>>> two_sum([2, 7, 11, 15], 17)
[0, 3]
>>> two_sum([7, 15, 11, 2], 18)
[0, 2]
>>> two_sum([2, 7, 11, 15], 26)
[2, 3]
>>> two_sum([2, 7, 11, 15], 8)
[]
>>> two_sum([3 * i for i in range(10)], 19)
[]
"""
chk_map = {}
for index, val in enumerate(nums):
compl = target - val
if compl in chk_map:
return [chk_map[compl], index]
chk_map[val] = index
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{two_sum([2, 7, 11, 15], 9) = }")
| """
Given an array of integers, return indices of the two numbers such that they add up to
a specific target.
You may assume that each input would have exactly one solution, and you may not use the
same element twice.
Example:
Given nums = [2, 7, 11, 15], target = 9,
Because nums[0] + nums[1] = 2 + 7 = 9,
return [0, 1].
"""
from __future__ import annotations
def two_sum(nums: list[int], target: int) -> list[int]:
"""
>>> two_sum([2, 7, 11, 15], 9)
[0, 1]
>>> two_sum([15, 2, 11, 7], 13)
[1, 2]
>>> two_sum([2, 7, 11, 15], 17)
[0, 3]
>>> two_sum([7, 15, 11, 2], 18)
[0, 2]
>>> two_sum([2, 7, 11, 15], 26)
[2, 3]
>>> two_sum([2, 7, 11, 15], 8)
[]
>>> two_sum([3 * i for i in range(10)], 19)
[]
"""
chk_map = {}
for index, val in enumerate(nums):
compl = target - val
if compl in chk_map:
return [chk_map[compl], index]
chk_map[val] = index
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{two_sum([2, 7, 11, 15], 9) = }")
| -1 |
TheAlgorithms/Python | 4,317 | fix(mypy): type annotations for linear algebra algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-05T11:42:12Z" | "2021-04-05T13:37:38Z" | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | 8c2986026bc42d81a6d9386c9fe621fea8ff2d15 | fix(mypy): type annotations for linear algebra algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def get_set_bits_count(number: int) -> int:
"""
Count the number of set bits in a 32 bit integer
>>> get_set_bits_count(25)
3
>>> get_set_bits_count(37)
3
>>> get_set_bits_count(21)
3
>>> get_set_bits_count(58)
4
>>> get_set_bits_count(0)
0
>>> get_set_bits_count(256)
1
>>> get_set_bits_count(-1)
Traceback (most recent call last):
...
ValueError: the value of input must be positive
"""
if number < 0:
raise ValueError("the value of input must be positive")
result = 0
while number:
if number % 2 == 1:
result += 1
number = number >> 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| def get_set_bits_count(number: int) -> int:
"""
Count the number of set bits in a 32 bit integer
>>> get_set_bits_count(25)
3
>>> get_set_bits_count(37)
3
>>> get_set_bits_count(21)
3
>>> get_set_bits_count(58)
4
>>> get_set_bits_count(0)
0
>>> get_set_bits_count(256)
1
>>> get_set_bits_count(-1)
Traceback (most recent call last):
...
ValueError: the value of input must be positive
"""
if number < 0:
raise ValueError("the value of input must be positive")
result = 0
while number:
if number % 2 == 1:
result += 1
number = number >> 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| name: "build"
on:
pull_request:
schedule:
- cron: "0 0 * * *" # Run everyday
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/setup-python@v2
with:
python-version: "3.9"
- uses: actions/cache@v2
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip setuptools six wheel
python -m pip install mypy pytest-cov -r requirements.txt
# FIXME: #4052 fix mypy errors in the exclude directories and remove them below
- run: mypy --ignore-missing-imports
--exclude '(conversions|data_structures|digital_image_processing|dynamic_programming|graphs|linear_algebra|maths|matrix|other|project_euler|scripts|searches|strings*)/$' .
- name: Run tests
run: pytest --doctest-modules --ignore=project_euler/ --ignore=scripts/ --cov-report=term-missing:skip-covered --cov=. .
- if: ${{ success() }}
run: scripts/build_directory_md.py 2>&1 | tee DIRECTORY.md
| name: "build"
on:
pull_request:
schedule:
- cron: "0 0 * * *" # Run everyday
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/setup-python@v2
with:
python-version: "3.9"
- uses: actions/cache@v2
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip setuptools six wheel
python -m pip install mypy pytest-cov -r requirements.txt
# FIXME: #4052 fix mypy errors in the exclude directories and remove them below
- run: mypy --ignore-missing-imports
--exclude '(data_structures|digital_image_processing|dynamic_programming|graphs|linear_algebra|maths|matrix|other|project_euler|scripts|searches|strings*)/$' .
- name: Run tests
run: pytest --doctest-modules --ignore=project_euler/ --ignore=scripts/ --cov-report=term-missing:skip-covered --cov=. .
- if: ${{ success() }}
run: scripts/build_directory_md.py 2>&1 | tee DIRECTORY.md
| 1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The function below will convert any binary string to the octal equivalent.
>>> bin_to_octal("1111")
'17'
>>> bin_to_octal("101010101010011")
'52523'
>>> bin_to_octal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> bin_to_octal("a-1")
Traceback (most recent call last):
...
ValueError: Non-binary value was passed to the function
"""
def bin_to_octal(bin_string: str) -> str:
if not all(char in "01" for char in bin_string):
raise ValueError("Non-binary value was passed to the function")
if not bin_string:
raise ValueError("Empty string was passed to the function")
oct_string = ""
while len(bin_string) % 3 != 0:
bin_string = "0" + bin_string
bin_string_in_3_list = [
bin_string[index : index + 3]
for index, value in enumerate(bin_string)
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
oct_val = 0
for index, val in enumerate(bin_group):
oct_val += int(2 ** (2 - index) * int(val))
oct_string += str(oct_val)
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| """
The function below will convert any binary string to the octal equivalent.
>>> bin_to_octal("1111")
'17'
>>> bin_to_octal("101010101010011")
'52523'
>>> bin_to_octal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> bin_to_octal("a-1")
Traceback (most recent call last):
...
ValueError: Non-binary value was passed to the function
"""
def bin_to_octal(bin_string: str) -> str:
if not all(char in "01" for char in bin_string):
raise ValueError("Non-binary value was passed to the function")
if not bin_string:
raise ValueError("Empty string was passed to the function")
oct_string = ""
while len(bin_string) % 3 != 0:
bin_string = "0" + bin_string
bin_string_in_3_list = [
bin_string[index : index + 3]
for index in range(len(bin_string))
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
oct_val = 0
for index, val in enumerate(bin_group):
oct_val += int(2 ** (2 - index) * int(val))
oct_string += str(oct_val)
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Convert a Decimal Number to a Binary Number."""
def decimal_to_binary(num: int) -> str:
"""
Convert an Integer Decimal Number to a Binary Number as str.
>>> decimal_to_binary(0)
'0b0'
>>> decimal_to_binary(2)
'0b10'
>>> decimal_to_binary(7)
'0b111'
>>> decimal_to_binary(35)
'0b100011'
>>> # negatives work too
>>> decimal_to_binary(-2)
'-0b10'
>>> # other floats will error
>>> decimal_to_binary(16.16) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> # strings will error as well
>>> decimal_to_binary('0xfffff') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'str' object cannot be interpreted as an integer
"""
if type(num) == float:
raise TypeError("'float' object cannot be interpreted as an integer")
if type(num) == str:
raise TypeError("'str' object cannot be interpreted as an integer")
if num == 0:
return "0b0"
negative = False
if num < 0:
negative = True
num = -num
binary = []
while num > 0:
binary.insert(0, num % 2)
num >>= 1
if negative:
return "-0b" + "".join(str(e) for e in binary)
return "0b" + "".join(str(e) for e in binary)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """Convert a Decimal Number to a Binary Number."""
def decimal_to_binary(num: int) -> str:
"""
Convert an Integer Decimal Number to a Binary Number as str.
>>> decimal_to_binary(0)
'0b0'
>>> decimal_to_binary(2)
'0b10'
>>> decimal_to_binary(7)
'0b111'
>>> decimal_to_binary(35)
'0b100011'
>>> # negatives work too
>>> decimal_to_binary(-2)
'-0b10'
>>> # other floats will error
>>> decimal_to_binary(16.16) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> # strings will error as well
>>> decimal_to_binary('0xfffff') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'str' object cannot be interpreted as an integer
"""
if isinstance(num, float):
raise TypeError("'float' object cannot be interpreted as an integer")
if isinstance(num, str):
raise TypeError("'str' object cannot be interpreted as an integer")
if num == 0:
return "0b0"
negative = False
if num < 0:
negative = True
num = -num
binary: list[int] = []
while num > 0:
binary.insert(0, num % 2)
num >>= 1
if negative:
return "-0b" + "".join(str(e) for e in binary)
return "0b" + "".join(str(e) for e in binary)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ Convert Base 10 (Decimal) Values to Hexadecimal Representations """
# set decimal value for each hexadecimal digit
values = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def decimal_to_hexadecimal(decimal):
"""
take integer decimal value, return hexadecimal representation as str beginning
with 0x
>>> decimal_to_hexadecimal(5)
'0x5'
>>> decimal_to_hexadecimal(15)
'0xf'
>>> decimal_to_hexadecimal(37)
'0x25'
>>> decimal_to_hexadecimal(255)
'0xff'
>>> decimal_to_hexadecimal(4096)
'0x1000'
>>> decimal_to_hexadecimal(999098)
'0xf3eba'
>>> # negatives work too
>>> decimal_to_hexadecimal(-256)
'-0x100'
>>> # floats are acceptable if equivalent to an int
>>> decimal_to_hexadecimal(17.0)
'0x11'
>>> # other floats will error
>>> decimal_to_hexadecimal(16.16) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError
>>> # strings will error as well
>>> decimal_to_hexadecimal('0xfffff') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError
>>> # results are the same when compared to Python's default hex function
>>> decimal_to_hexadecimal(-256) == hex(-256)
True
"""
assert type(decimal) in (int, float) and decimal == int(decimal)
hexadecimal = ""
negative = False
if decimal < 0:
negative = True
decimal *= -1
while decimal > 0:
decimal, remainder = divmod(decimal, 16)
hexadecimal = values[remainder] + hexadecimal
hexadecimal = "0x" + hexadecimal
if negative:
hexadecimal = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| """ Convert Base 10 (Decimal) Values to Hexadecimal Representations """
# set decimal value for each hexadecimal digit
values = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def decimal_to_hexadecimal(decimal: float) -> str:
"""
take integer decimal value, return hexadecimal representation as str beginning
with 0x
>>> decimal_to_hexadecimal(5)
'0x5'
>>> decimal_to_hexadecimal(15)
'0xf'
>>> decimal_to_hexadecimal(37)
'0x25'
>>> decimal_to_hexadecimal(255)
'0xff'
>>> decimal_to_hexadecimal(4096)
'0x1000'
>>> decimal_to_hexadecimal(999098)
'0xf3eba'
>>> # negatives work too
>>> decimal_to_hexadecimal(-256)
'-0x100'
>>> # floats are acceptable if equivalent to an int
>>> decimal_to_hexadecimal(17.0)
'0x11'
>>> # other floats will error
>>> decimal_to_hexadecimal(16.16) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError
>>> # strings will error as well
>>> decimal_to_hexadecimal('0xfffff') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError
>>> # results are the same when compared to Python's default hex function
>>> decimal_to_hexadecimal(-256) == hex(-256)
True
"""
assert type(decimal) in (int, float) and decimal == int(decimal)
decimal = int(decimal)
hexadecimal = ""
negative = False
if decimal < 0:
negative = True
decimal *= -1
while decimal > 0:
decimal, remainder = divmod(decimal, 16)
hexadecimal = values[remainder] + hexadecimal
hexadecimal = "0x" + hexadecimal
if negative:
hexadecimal = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Convert a Decimal Number to an Octal Number."""
import math
# Modified from:
# https://github.com/TheAlgorithms/Javascript/blob/master/Conversions/DecimalToOctal.js
def decimal_to_octal(num: int) -> str:
"""Convert a Decimal Number to an Octal Number.
>>> all(decimal_to_octal(i) == oct(i) for i
... in (0, 2, 8, 64, 65, 216, 255, 256, 512))
True
"""
octal = 0
counter = 0
while num > 0:
remainder = num % 8
octal = octal + (remainder * math.pow(10, counter))
counter += 1
num = math.floor(num / 8) # basically /= 8 without remainder if any
# This formatting removes trailing '.0' from `octal`.
return f"0o{int(octal)}"
def main():
"""Print octal equivalents of decimal numbers."""
print("\n2 in octal is:")
print(decimal_to_octal(2)) # = 2
print("\n8 in octal is:")
print(decimal_to_octal(8)) # = 10
print("\n65 in octal is:")
print(decimal_to_octal(65)) # = 101
print("\n216 in octal is:")
print(decimal_to_octal(216)) # = 330
print("\n512 in octal is:")
print(decimal_to_octal(512)) # = 1000
print("\n")
if __name__ == "__main__":
main()
| """Convert a Decimal Number to an Octal Number."""
import math
# Modified from:
# https://github.com/TheAlgorithms/Javascript/blob/master/Conversions/DecimalToOctal.js
def decimal_to_octal(num: int) -> str:
"""Convert a Decimal Number to an Octal Number.
>>> all(decimal_to_octal(i) == oct(i) for i
... in (0, 2, 8, 64, 65, 216, 255, 256, 512))
True
"""
octal = 0
counter = 0
while num > 0:
remainder = num % 8
octal = octal + (remainder * math.floor(math.pow(10, counter)))
counter += 1
num = math.floor(num / 8) # basically /= 8 without remainder if any
# This formatting removes trailing '.0' from `octal`.
return f"0o{int(octal)}"
def main() -> None:
"""Print octal equivalents of decimal numbers."""
print("\n2 in octal is:")
print(decimal_to_octal(2)) # = 2
print("\n8 in octal is:")
print(decimal_to_octal(8)) # = 10
print("\n65 in octal is:")
print(decimal_to_octal(65)) # = 101
print("\n216 in octal is:")
print(decimal_to_octal(216)) # = 330
print("\n512 in octal is:")
print(decimal_to_octal(512)) # = 1000
print("\n")
if __name__ == "__main__":
main()
| 1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Convert International System of Units (SI) and Binary prefixes
"""
from enum import Enum
from typing import Union
class SI_Unit(Enum):
yotta = 24
zetta = 21
exa = 18
peta = 15
tera = 12
giga = 9
mega = 6
kilo = 3
hecto = 2
deca = 1
deci = -1
centi = -2
milli = -3
micro = -6
nano = -9
pico = -12
femto = -15
atto = -18
zepto = -21
yocto = -24
class Binary_Unit(Enum):
yotta = 8
zetta = 7
exa = 6
peta = 5
tera = 4
giga = 3
mega = 2
kilo = 1
def convert_si_prefix(
known_amount: float,
known_prefix: Union[str, SI_Unit],
unknown_prefix: Union[str, SI_Unit],
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Binary_prefix
Wikipedia reference: https://en.wikipedia.org/wiki/International_System_of_Units
>>> convert_si_prefix(1, SI_Unit.giga, SI_Unit.mega)
1000
>>> convert_si_prefix(1, SI_Unit.mega, SI_Unit.giga)
0.001
>>> convert_si_prefix(1, SI_Unit.kilo, SI_Unit.kilo)
1
>>> convert_si_prefix(1, 'giga', 'mega')
1000
>>> convert_si_prefix(1, 'gIGa', 'mEGa')
1000
"""
if isinstance(known_prefix, str):
known_prefix: SI_Unit = SI_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix: SI_Unit = SI_Unit[unknown_prefix.lower()]
unknown_amount = known_amount * (10 ** (known_prefix.value - unknown_prefix.value))
return unknown_amount
def convert_binary_prefix(
known_amount: float,
known_prefix: Union[str, Binary_Unit],
unknown_prefix: Union[str, Binary_Unit],
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Metric_prefix
>>> convert_binary_prefix(1, Binary_Unit.giga, Binary_Unit.mega)
1024
>>> convert_binary_prefix(1, Binary_Unit.mega, Binary_Unit.giga)
0.0009765625
>>> convert_binary_prefix(1, Binary_Unit.kilo, Binary_Unit.kilo)
1
>>> convert_binary_prefix(1, 'giga', 'mega')
1024
>>> convert_binary_prefix(1, 'gIGa', 'mEGa')
1024
"""
if isinstance(known_prefix, str):
known_prefix: Binary_Unit = Binary_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix: Binary_Unit = Binary_Unit[unknown_prefix.lower()]
unknown_amount = known_amount * (
2 ** ((known_prefix.value - unknown_prefix.value) * 10)
)
return unknown_amount
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Convert International System of Units (SI) and Binary prefixes
"""
from enum import Enum
from typing import Union
class SI_Unit(Enum):
yotta = 24
zetta = 21
exa = 18
peta = 15
tera = 12
giga = 9
mega = 6
kilo = 3
hecto = 2
deca = 1
deci = -1
centi = -2
milli = -3
micro = -6
nano = -9
pico = -12
femto = -15
atto = -18
zepto = -21
yocto = -24
class Binary_Unit(Enum):
yotta = 8
zetta = 7
exa = 6
peta = 5
tera = 4
giga = 3
mega = 2
kilo = 1
def convert_si_prefix(
known_amount: float,
known_prefix: Union[str, SI_Unit],
unknown_prefix: Union[str, SI_Unit],
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Binary_prefix
Wikipedia reference: https://en.wikipedia.org/wiki/International_System_of_Units
>>> convert_si_prefix(1, SI_Unit.giga, SI_Unit.mega)
1000
>>> convert_si_prefix(1, SI_Unit.mega, SI_Unit.giga)
0.001
>>> convert_si_prefix(1, SI_Unit.kilo, SI_Unit.kilo)
1
>>> convert_si_prefix(1, 'giga', 'mega')
1000
>>> convert_si_prefix(1, 'gIGa', 'mEGa')
1000
"""
if isinstance(known_prefix, str):
known_prefix = SI_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = SI_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
10 ** (known_prefix.value - unknown_prefix.value)
)
return unknown_amount
def convert_binary_prefix(
known_amount: float,
known_prefix: Union[str, Binary_Unit],
unknown_prefix: Union[str, Binary_Unit],
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Metric_prefix
>>> convert_binary_prefix(1, Binary_Unit.giga, Binary_Unit.mega)
1024
>>> convert_binary_prefix(1, Binary_Unit.mega, Binary_Unit.giga)
0.0009765625
>>> convert_binary_prefix(1, Binary_Unit.kilo, Binary_Unit.kilo)
1
>>> convert_binary_prefix(1, 'giga', 'mega')
1024
>>> convert_binary_prefix(1, 'gIGa', 'mEGa')
1024
"""
if isinstance(known_prefix, str):
known_prefix = Binary_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = Binary_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
2 ** ((known_prefix.value - unknown_prefix.value) * 10)
)
return unknown_amount
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Conversion of weight units.
__author__ = "Anubhav Solanki"
__license__ = "MIT"
__version__ = "1.0.0"
__maintainer__ = "Anubhav Solanki"
__email__ = "[email protected]"
USAGE :
-> Import this file into their respective project.
-> Use the function weight_conversion() for conversion of weight units.
-> Parameters :
-> from_type : From which type you want to convert
-> to_type : To which type you want to convert
-> value : the value which you want to convert
REFERENCES :
-> Wikipedia reference: https://en.wikipedia.org/wiki/Kilogram
-> Wikipedia reference: https://en.wikipedia.org/wiki/Gram
-> Wikipedia reference: https://en.wikipedia.org/wiki/Millimetre
-> Wikipedia reference: https://en.wikipedia.org/wiki/Tonne
-> Wikipedia reference: https://en.wikipedia.org/wiki/Long_ton
-> Wikipedia reference: https://en.wikipedia.org/wiki/Short_ton
-> Wikipedia reference: https://en.wikipedia.org/wiki/Pound
-> Wikipedia reference: https://en.wikipedia.org/wiki/Ounce
-> Wikipedia reference: https://en.wikipedia.org/wiki/Fineness#Karat
-> Wikipedia reference: https://en.wikipedia.org/wiki/Dalton_(unit)
"""
KILOGRAM_CHART = {
"kilogram": 1,
"gram": pow(10, 3),
"milligram": pow(10, 6),
"metric-ton": pow(10, -3),
"long-ton": 0.0009842073,
"short-ton": 0.0011023122,
"pound": 2.2046244202,
"ounce": 35.273990723,
"carrat": 5000,
"atomic-mass-unit": 6.022136652e26,
}
WEIGHT_TYPE_CHART = {
"kilogram": 1,
"gram": pow(10, -3),
"milligram": pow(10, -6),
"metric-ton": pow(10, 3),
"long-ton": 1016.04608,
"short-ton": 907.184,
"pound": 0.453592,
"ounce": 0.0283495,
"carrat": 0.0002,
"atomic-mass-unit": 1.660540199e-27,
}
def weight_conversion(from_type: str, to_type: str, value: float) -> float:
"""
Conversion of weight unit with the help of KILOGRAM_CHART
"kilogram" : 1,
"gram" : pow(10, 3),
"milligram" : pow(10, 6),
"metric-ton" : pow(10, -3),
"long-ton" : 0.0009842073,
"short-ton" : 0.0011023122,
"pound" : 2.2046244202,
"ounce" : 35.273990723,
"carrat" : 5000,
"atomic-mass-unit" : 6.022136652E+26
>>> weight_conversion("kilogram","kilogram",4)
4
>>> weight_conversion("kilogram","gram",1)
1000
>>> weight_conversion("kilogram","milligram",4)
4000000
>>> weight_conversion("kilogram","metric-ton",4)
0.004
>>> weight_conversion("kilogram","long-ton",3)
0.0029526219
>>> weight_conversion("kilogram","short-ton",1)
0.0011023122
>>> weight_conversion("kilogram","pound",4)
8.8184976808
>>> weight_conversion("kilogram","ounce",4)
141.095962892
>>> weight_conversion("kilogram","carrat",3)
15000
>>> weight_conversion("kilogram","atomic-mass-unit",1)
6.022136652e+26
>>> weight_conversion("gram","kilogram",1)
0.001
>>> weight_conversion("gram","gram",3)
3.0
>>> weight_conversion("gram","milligram",2)
2000.0
>>> weight_conversion("gram","metric-ton",4)
4e-06
>>> weight_conversion("gram","long-ton",3)
2.9526219e-06
>>> weight_conversion("gram","short-ton",3)
3.3069366000000003e-06
>>> weight_conversion("gram","pound",3)
0.0066138732606
>>> weight_conversion("gram","ounce",1)
0.035273990723
>>> weight_conversion("gram","carrat",2)
10.0
>>> weight_conversion("gram","atomic-mass-unit",1)
6.022136652e+23
>>> weight_conversion("milligram","kilogram",1)
1e-06
>>> weight_conversion("milligram","gram",2)
0.002
>>> weight_conversion("milligram","milligram",3)
3.0
>>> weight_conversion("milligram","metric-ton",3)
3e-09
>>> weight_conversion("milligram","long-ton",3)
2.9526219e-09
>>> weight_conversion("milligram","short-ton",1)
1.1023122e-09
>>> weight_conversion("milligram","pound",3)
6.6138732605999995e-06
>>> weight_conversion("milligram","ounce",2)
7.054798144599999e-05
>>> weight_conversion("milligram","carrat",1)
0.005
>>> weight_conversion("milligram","atomic-mass-unit",1)
6.022136652e+20
>>> weight_conversion("metric-ton","kilogram",2)
2000
>>> weight_conversion("metric-ton","gram",2)
2000000
>>> weight_conversion("metric-ton","milligram",3)
3000000000
>>> weight_conversion("metric-ton","metric-ton",2)
2.0
>>> weight_conversion("metric-ton","long-ton",3)
2.9526219
>>> weight_conversion("metric-ton","short-ton",2)
2.2046244
>>> weight_conversion("metric-ton","pound",3)
6613.8732606
>>> weight_conversion("metric-ton","ounce",4)
141095.96289199998
>>> weight_conversion("metric-ton","carrat",4)
20000000
>>> weight_conversion("metric-ton","atomic-mass-unit",1)
6.022136652e+29
>>> weight_conversion("long-ton","kilogram",4)
4064.18432
>>> weight_conversion("long-ton","gram",4)
4064184.32
>>> weight_conversion("long-ton","milligram",3)
3048138240.0
>>> weight_conversion("long-ton","metric-ton",4)
4.06418432
>>> weight_conversion("long-ton","long-ton",3)
2.999999907217152
>>> weight_conversion("long-ton","short-ton",1)
1.119999989746176
>>> weight_conversion("long-ton","pound",3)
6720.000000049448
>>> weight_conversion("long-ton","ounce",1)
35840.000000060514
>>> weight_conversion("long-ton","carrat",4)
20320921.599999998
>>> weight_conversion("long-ton","atomic-mass-unit",4)
2.4475073353955697e+30
>>> weight_conversion("short-ton","kilogram",3)
2721.5519999999997
>>> weight_conversion("short-ton","gram",3)
2721552.0
>>> weight_conversion("short-ton","milligram",1)
907184000.0
>>> weight_conversion("short-ton","metric-ton",4)
3.628736
>>> weight_conversion("short-ton","long-ton",3)
2.6785713457296
>>> weight_conversion("short-ton","short-ton",3)
2.9999999725344
>>> weight_conversion("short-ton","pound",2)
4000.0000000294335
>>> weight_conversion("short-ton","ounce",4)
128000.00000021611
>>> weight_conversion("short-ton","carrat",4)
18143680.0
>>> weight_conversion("short-ton","atomic-mass-unit",1)
5.463186016507968e+29
>>> weight_conversion("pound","kilogram",4)
1.814368
>>> weight_conversion("pound","gram",2)
907.184
>>> weight_conversion("pound","milligram",3)
1360776.0
>>> weight_conversion("pound","metric-ton",3)
0.001360776
>>> weight_conversion("pound","long-ton",2)
0.0008928571152432
>>> weight_conversion("pound","short-ton",1)
0.0004999999954224
>>> weight_conversion("pound","pound",3)
3.0000000000220752
>>> weight_conversion("pound","ounce",1)
16.000000000027015
>>> weight_conversion("pound","carrat",1)
2267.96
>>> weight_conversion("pound","atomic-mass-unit",4)
1.0926372033015936e+27
>>> weight_conversion("ounce","kilogram",3)
0.0850485
>>> weight_conversion("ounce","gram",3)
85.0485
>>> weight_conversion("ounce","milligram",4)
113398.0
>>> weight_conversion("ounce","metric-ton",4)
0.000113398
>>> weight_conversion("ounce","long-ton",4)
0.0001116071394054
>>> weight_conversion("ounce","short-ton",4)
0.0001249999988556
>>> weight_conversion("ounce","pound",1)
0.0625000000004599
>>> weight_conversion("ounce","ounce",2)
2.000000000003377
>>> weight_conversion("ounce","carrat",1)
141.7475
>>> weight_conversion("ounce","atomic-mass-unit",1)
1.70724563015874e+25
>>> weight_conversion("carrat","kilogram",1)
0.0002
>>> weight_conversion("carrat","gram",4)
0.8
>>> weight_conversion("carrat","milligram",2)
400.0
>>> weight_conversion("carrat","metric-ton",2)
4.0000000000000003e-07
>>> weight_conversion("carrat","long-ton",3)
5.9052438e-07
>>> weight_conversion("carrat","short-ton",4)
8.818497600000002e-07
>>> weight_conversion("carrat","pound",1)
0.00044092488404000004
>>> weight_conversion("carrat","ounce",2)
0.0141095962892
>>> weight_conversion("carrat","carrat",4)
4.0
>>> weight_conversion("carrat","atomic-mass-unit",4)
4.8177093216e+23
>>> weight_conversion("atomic-mass-unit","kilogram",4)
6.642160796e-27
>>> weight_conversion("atomic-mass-unit","gram",2)
3.321080398e-24
>>> weight_conversion("atomic-mass-unit","milligram",2)
3.3210803980000002e-21
>>> weight_conversion("atomic-mass-unit","metric-ton",3)
4.9816205970000004e-30
>>> weight_conversion("atomic-mass-unit","long-ton",3)
4.9029473573977584e-30
>>> weight_conversion("atomic-mass-unit","short-ton",1)
1.830433719948128e-30
>>> weight_conversion("atomic-mass-unit","pound",3)
1.0982602420317504e-26
>>> weight_conversion("atomic-mass-unit","ounce",2)
1.1714775914938915e-25
>>> weight_conversion("atomic-mass-unit","carrat",2)
1.660540199e-23
>>> weight_conversion("atomic-mass-unit","atomic-mass-unit",2)
1.999999998903455
"""
if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
raise ValueError(
f"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
f"Supported values are: {', '.join(WEIGHT_TYPE_CHART)}"
)
return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Conversion of weight units.
__author__ = "Anubhav Solanki"
__license__ = "MIT"
__version__ = "1.0.0"
__maintainer__ = "Anubhav Solanki"
__email__ = "[email protected]"
USAGE :
-> Import this file into their respective project.
-> Use the function weight_conversion() for conversion of weight units.
-> Parameters :
-> from_type : From which type you want to convert
-> to_type : To which type you want to convert
-> value : the value which you want to convert
REFERENCES :
-> Wikipedia reference: https://en.wikipedia.org/wiki/Kilogram
-> Wikipedia reference: https://en.wikipedia.org/wiki/Gram
-> Wikipedia reference: https://en.wikipedia.org/wiki/Millimetre
-> Wikipedia reference: https://en.wikipedia.org/wiki/Tonne
-> Wikipedia reference: https://en.wikipedia.org/wiki/Long_ton
-> Wikipedia reference: https://en.wikipedia.org/wiki/Short_ton
-> Wikipedia reference: https://en.wikipedia.org/wiki/Pound
-> Wikipedia reference: https://en.wikipedia.org/wiki/Ounce
-> Wikipedia reference: https://en.wikipedia.org/wiki/Fineness#Karat
-> Wikipedia reference: https://en.wikipedia.org/wiki/Dalton_(unit)
"""
KILOGRAM_CHART: dict[str, float] = {
"kilogram": 1,
"gram": pow(10, 3),
"milligram": pow(10, 6),
"metric-ton": pow(10, -3),
"long-ton": 0.0009842073,
"short-ton": 0.0011023122,
"pound": 2.2046244202,
"ounce": 35.273990723,
"carrat": 5000,
"atomic-mass-unit": 6.022136652e26,
}
WEIGHT_TYPE_CHART: dict[str, float] = {
"kilogram": 1,
"gram": pow(10, -3),
"milligram": pow(10, -6),
"metric-ton": pow(10, 3),
"long-ton": 1016.04608,
"short-ton": 907.184,
"pound": 0.453592,
"ounce": 0.0283495,
"carrat": 0.0002,
"atomic-mass-unit": 1.660540199e-27,
}
def weight_conversion(from_type: str, to_type: str, value: float) -> float:
"""
Conversion of weight unit with the help of KILOGRAM_CHART
"kilogram" : 1,
"gram" : pow(10, 3),
"milligram" : pow(10, 6),
"metric-ton" : pow(10, -3),
"long-ton" : 0.0009842073,
"short-ton" : 0.0011023122,
"pound" : 2.2046244202,
"ounce" : 35.273990723,
"carrat" : 5000,
"atomic-mass-unit" : 6.022136652E+26
>>> weight_conversion("kilogram","kilogram",4)
4
>>> weight_conversion("kilogram","gram",1)
1000
>>> weight_conversion("kilogram","milligram",4)
4000000
>>> weight_conversion("kilogram","metric-ton",4)
0.004
>>> weight_conversion("kilogram","long-ton",3)
0.0029526219
>>> weight_conversion("kilogram","short-ton",1)
0.0011023122
>>> weight_conversion("kilogram","pound",4)
8.8184976808
>>> weight_conversion("kilogram","ounce",4)
141.095962892
>>> weight_conversion("kilogram","carrat",3)
15000
>>> weight_conversion("kilogram","atomic-mass-unit",1)
6.022136652e+26
>>> weight_conversion("gram","kilogram",1)
0.001
>>> weight_conversion("gram","gram",3)
3.0
>>> weight_conversion("gram","milligram",2)
2000.0
>>> weight_conversion("gram","metric-ton",4)
4e-06
>>> weight_conversion("gram","long-ton",3)
2.9526219e-06
>>> weight_conversion("gram","short-ton",3)
3.3069366000000003e-06
>>> weight_conversion("gram","pound",3)
0.0066138732606
>>> weight_conversion("gram","ounce",1)
0.035273990723
>>> weight_conversion("gram","carrat",2)
10.0
>>> weight_conversion("gram","atomic-mass-unit",1)
6.022136652e+23
>>> weight_conversion("milligram","kilogram",1)
1e-06
>>> weight_conversion("milligram","gram",2)
0.002
>>> weight_conversion("milligram","milligram",3)
3.0
>>> weight_conversion("milligram","metric-ton",3)
3e-09
>>> weight_conversion("milligram","long-ton",3)
2.9526219e-09
>>> weight_conversion("milligram","short-ton",1)
1.1023122e-09
>>> weight_conversion("milligram","pound",3)
6.6138732605999995e-06
>>> weight_conversion("milligram","ounce",2)
7.054798144599999e-05
>>> weight_conversion("milligram","carrat",1)
0.005
>>> weight_conversion("milligram","atomic-mass-unit",1)
6.022136652e+20
>>> weight_conversion("metric-ton","kilogram",2)
2000
>>> weight_conversion("metric-ton","gram",2)
2000000
>>> weight_conversion("metric-ton","milligram",3)
3000000000
>>> weight_conversion("metric-ton","metric-ton",2)
2.0
>>> weight_conversion("metric-ton","long-ton",3)
2.9526219
>>> weight_conversion("metric-ton","short-ton",2)
2.2046244
>>> weight_conversion("metric-ton","pound",3)
6613.8732606
>>> weight_conversion("metric-ton","ounce",4)
141095.96289199998
>>> weight_conversion("metric-ton","carrat",4)
20000000
>>> weight_conversion("metric-ton","atomic-mass-unit",1)
6.022136652e+29
>>> weight_conversion("long-ton","kilogram",4)
4064.18432
>>> weight_conversion("long-ton","gram",4)
4064184.32
>>> weight_conversion("long-ton","milligram",3)
3048138240.0
>>> weight_conversion("long-ton","metric-ton",4)
4.06418432
>>> weight_conversion("long-ton","long-ton",3)
2.999999907217152
>>> weight_conversion("long-ton","short-ton",1)
1.119999989746176
>>> weight_conversion("long-ton","pound",3)
6720.000000049448
>>> weight_conversion("long-ton","ounce",1)
35840.000000060514
>>> weight_conversion("long-ton","carrat",4)
20320921.599999998
>>> weight_conversion("long-ton","atomic-mass-unit",4)
2.4475073353955697e+30
>>> weight_conversion("short-ton","kilogram",3)
2721.5519999999997
>>> weight_conversion("short-ton","gram",3)
2721552.0
>>> weight_conversion("short-ton","milligram",1)
907184000.0
>>> weight_conversion("short-ton","metric-ton",4)
3.628736
>>> weight_conversion("short-ton","long-ton",3)
2.6785713457296
>>> weight_conversion("short-ton","short-ton",3)
2.9999999725344
>>> weight_conversion("short-ton","pound",2)
4000.0000000294335
>>> weight_conversion("short-ton","ounce",4)
128000.00000021611
>>> weight_conversion("short-ton","carrat",4)
18143680.0
>>> weight_conversion("short-ton","atomic-mass-unit",1)
5.463186016507968e+29
>>> weight_conversion("pound","kilogram",4)
1.814368
>>> weight_conversion("pound","gram",2)
907.184
>>> weight_conversion("pound","milligram",3)
1360776.0
>>> weight_conversion("pound","metric-ton",3)
0.001360776
>>> weight_conversion("pound","long-ton",2)
0.0008928571152432
>>> weight_conversion("pound","short-ton",1)
0.0004999999954224
>>> weight_conversion("pound","pound",3)
3.0000000000220752
>>> weight_conversion("pound","ounce",1)
16.000000000027015
>>> weight_conversion("pound","carrat",1)
2267.96
>>> weight_conversion("pound","atomic-mass-unit",4)
1.0926372033015936e+27
>>> weight_conversion("ounce","kilogram",3)
0.0850485
>>> weight_conversion("ounce","gram",3)
85.0485
>>> weight_conversion("ounce","milligram",4)
113398.0
>>> weight_conversion("ounce","metric-ton",4)
0.000113398
>>> weight_conversion("ounce","long-ton",4)
0.0001116071394054
>>> weight_conversion("ounce","short-ton",4)
0.0001249999988556
>>> weight_conversion("ounce","pound",1)
0.0625000000004599
>>> weight_conversion("ounce","ounce",2)
2.000000000003377
>>> weight_conversion("ounce","carrat",1)
141.7475
>>> weight_conversion("ounce","atomic-mass-unit",1)
1.70724563015874e+25
>>> weight_conversion("carrat","kilogram",1)
0.0002
>>> weight_conversion("carrat","gram",4)
0.8
>>> weight_conversion("carrat","milligram",2)
400.0
>>> weight_conversion("carrat","metric-ton",2)
4.0000000000000003e-07
>>> weight_conversion("carrat","long-ton",3)
5.9052438e-07
>>> weight_conversion("carrat","short-ton",4)
8.818497600000002e-07
>>> weight_conversion("carrat","pound",1)
0.00044092488404000004
>>> weight_conversion("carrat","ounce",2)
0.0141095962892
>>> weight_conversion("carrat","carrat",4)
4.0
>>> weight_conversion("carrat","atomic-mass-unit",4)
4.8177093216e+23
>>> weight_conversion("atomic-mass-unit","kilogram",4)
6.642160796e-27
>>> weight_conversion("atomic-mass-unit","gram",2)
3.321080398e-24
>>> weight_conversion("atomic-mass-unit","milligram",2)
3.3210803980000002e-21
>>> weight_conversion("atomic-mass-unit","metric-ton",3)
4.9816205970000004e-30
>>> weight_conversion("atomic-mass-unit","long-ton",3)
4.9029473573977584e-30
>>> weight_conversion("atomic-mass-unit","short-ton",1)
1.830433719948128e-30
>>> weight_conversion("atomic-mass-unit","pound",3)
1.0982602420317504e-26
>>> weight_conversion("atomic-mass-unit","ounce",2)
1.1714775914938915e-25
>>> weight_conversion("atomic-mass-unit","carrat",2)
1.660540199e-23
>>> weight_conversion("atomic-mass-unit","atomic-mass-unit",2)
1.999999998903455
"""
if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
raise ValueError(
f"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
f"Supported values are: {', '.join(WEIGHT_TYPE_CHART)}"
)
return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Test cases:
Do you want to enter your denominations ? (Y/N) :N
Enter the change you want to make in Indian Currency: 987
Following is minimal change for 987 :
500 100 100 100 100 50 20 10 5 2
Do you want to enter your denominations ? (Y/N) :Y
Enter number of denomination:10
1
5
10
20
50
100
200
500
1000
2000
Enter the change you want to make: 18745
Following is minimal change for 18745 :
2000 2000 2000 2000 2000 2000 2000 2000 2000 500 200 20 20 5
Do you want to enter your denominations ? (Y/N) :N
Enter the change you want to make: 0
The total value cannot be zero or negative.
Do you want to enter your denominations ? (Y/N) :N
Enter the change you want to make: -98
The total value cannot be zero or negative.
Do you want to enter your denominations ? (Y/N) :Y
Enter number of denomination:5
1
5
100
500
1000
Enter the change you want to make: 456
Following is minimal change for 456 :
100 100 100 100 5 5 5 5 5 5 5 5 5 5 5 1
"""
def find_minimum_change(denominations: list[int], value: int) -> list[int]:
"""
Find the minimum change from the given denominations and value
>>> find_minimum_change([1, 5, 10, 20, 50, 100, 200, 500, 1000,2000], 18745)
[2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 500, 200, 20, 20, 5]
>>> find_minimum_change([1, 2, 5, 10, 20, 50, 100, 500, 2000], 987)
[500, 100, 100, 100, 100, 50, 20, 10, 5, 2]
>>> find_minimum_change([1, 2, 5, 10, 20, 50, 100, 500, 2000], 0)
[]
>>> find_minimum_change([1, 2, 5, 10, 20, 50, 100, 500, 2000], -98)
[]
>>> find_minimum_change([1, 5, 100, 500, 1000], 456)
[100, 100, 100, 100, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1]
"""
total_value = int(value)
# Initialize Result
answer = []
# Traverse through all denomination
for denomination in reversed(denominations):
# Find denominations
while int(total_value) >= int(denomination):
total_value -= int(denomination)
answer.append(denomination) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
denominations = list()
value = 0
if (
input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
== "y"
):
n = int(input("Enter the number of denominations you want to add: ").strip())
for i in range(0, n):
denominations.append(int(input(f"Denomination {i}: ").strip()))
value = input("Enter the change you want to make in Indian Currency: ").strip()
else:
# All denominations of Indian Currency if user does not enter
denominations = [1, 2, 5, 10, 20, 50, 100, 500, 2000]
value = input("Enter the change you want to make: ").strip()
if int(value) == 0 or int(value) < 0:
print("The total value cannot be zero or negative.")
else:
print(f"Following is minimal change for {value}: ")
answer = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=" ")
| """
Test cases:
Do you want to enter your denominations ? (Y/N) :N
Enter the change you want to make in Indian Currency: 987
Following is minimal change for 987 :
500 100 100 100 100 50 20 10 5 2
Do you want to enter your denominations ? (Y/N) :Y
Enter number of denomination:10
1
5
10
20
50
100
200
500
1000
2000
Enter the change you want to make: 18745
Following is minimal change for 18745 :
2000 2000 2000 2000 2000 2000 2000 2000 2000 500 200 20 20 5
Do you want to enter your denominations ? (Y/N) :N
Enter the change you want to make: 0
The total value cannot be zero or negative.
Do you want to enter your denominations ? (Y/N) :N
Enter the change you want to make: -98
The total value cannot be zero or negative.
Do you want to enter your denominations ? (Y/N) :Y
Enter number of denomination:5
1
5
100
500
1000
Enter the change you want to make: 456
Following is minimal change for 456 :
100 100 100 100 5 5 5 5 5 5 5 5 5 5 5 1
"""
def find_minimum_change(denominations: list[int], value: int) -> list[int]:
"""
Find the minimum change from the given denominations and value
>>> find_minimum_change([1, 5, 10, 20, 50, 100, 200, 500, 1000,2000], 18745)
[2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 500, 200, 20, 20, 5]
>>> find_minimum_change([1, 2, 5, 10, 20, 50, 100, 500, 2000], 987)
[500, 100, 100, 100, 100, 50, 20, 10, 5, 2]
>>> find_minimum_change([1, 2, 5, 10, 20, 50, 100, 500, 2000], 0)
[]
>>> find_minimum_change([1, 2, 5, 10, 20, 50, 100, 500, 2000], -98)
[]
>>> find_minimum_change([1, 5, 100, 500, 1000], 456)
[100, 100, 100, 100, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1]
"""
total_value = int(value)
# Initialize Result
answer = []
# Traverse through all denomination
for denomination in reversed(denominations):
# Find denominations
while int(total_value) >= int(denomination):
total_value -= int(denomination)
answer.append(denomination) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
denominations = list()
value = 0
if (
input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
== "y"
):
n = int(input("Enter the number of denominations you want to add: ").strip())
for i in range(0, n):
denominations.append(int(input(f"Denomination {i}: ").strip()))
value = input("Enter the change you want to make in Indian Currency: ").strip()
else:
# All denominations of Indian Currency if user does not enter
denominations = [1, 2, 5, 10, 20, 50, 100, 500, 2000]
value = input("Enter the change you want to make: ").strip()
if int(value) == 0 or int(value) < 0:
print("The total value cannot be zero or negative.")
else:
print(f"Following is minimal change for {value}: ")
answer = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=" ")
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from math import log2
def binary_count_trailing_zeros(a: int) -> int:
"""
Take in 1 integer, return a number that is
the number of trailing zeros in binary representation of that number.
>>> binary_count_trailing_zeros(25)
0
>>> binary_count_trailing_zeros(36)
2
>>> binary_count_trailing_zeros(16)
4
>>> binary_count_trailing_zeros(58)
1
>>> binary_count_trailing_zeros(4294967296)
32
>>> binary_count_trailing_zeros(0)
0
>>> binary_count_trailing_zeros(-10)
Traceback (most recent call last):
...
ValueError: Input value must be a positive integer
>>> binary_count_trailing_zeros(0.8)
Traceback (most recent call last):
...
TypeError: Input value must be a 'int' type
>>> binary_count_trailing_zeros("0")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0:
raise ValueError("Input value must be a positive integer")
elif isinstance(a, float):
raise TypeError("Input value must be a 'int' type")
return 0 if (a == 0) else int(log2(a & -a))
if __name__ == "__main__":
import doctest
doctest.testmod()
| from math import log2
def binary_count_trailing_zeros(a: int) -> int:
"""
Take in 1 integer, return a number that is
the number of trailing zeros in binary representation of that number.
>>> binary_count_trailing_zeros(25)
0
>>> binary_count_trailing_zeros(36)
2
>>> binary_count_trailing_zeros(16)
4
>>> binary_count_trailing_zeros(58)
1
>>> binary_count_trailing_zeros(4294967296)
32
>>> binary_count_trailing_zeros(0)
0
>>> binary_count_trailing_zeros(-10)
Traceback (most recent call last):
...
ValueError: Input value must be a positive integer
>>> binary_count_trailing_zeros(0.8)
Traceback (most recent call last):
...
TypeError: Input value must be a 'int' type
>>> binary_count_trailing_zeros("0")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0:
raise ValueError("Input value must be a positive integer")
elif isinstance(a, float):
raise TypeError("Input value must be a 'int' type")
return 0 if (a == 0) else int(log2(a & -a))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os
class FileSplitter:
BLOCK_FILENAME_FORMAT = "block_{0}.dat"
def __init__(self, filename):
self.filename = filename
self.block_filenames = []
def write_block(self, data, block_number):
filename = self.BLOCK_FILENAME_FORMAT.format(block_number)
with open(filename, "w") as file:
file.write(data)
self.block_filenames.append(filename)
def get_block_filenames(self):
return self.block_filenames
def split(self, block_size, sort_key=None):
i = 0
with open(self.filename) as file:
while True:
lines = file.readlines(block_size)
if lines == []:
break
if sort_key is None:
lines.sort()
else:
lines.sort(key=sort_key)
self.write_block("".join(lines), i)
i += 1
def cleanup(self):
map(lambda f: os.remove(f), self.block_filenames)
class NWayMerge:
def select(self, choices):
min_index = -1
min_str = None
for i in range(len(choices)):
if min_str is None or choices[i] < min_str:
min_index = i
return min_index
class FilesArray:
def __init__(self, files):
self.files = files
self.empty = set()
self.num_buffers = len(files)
self.buffers = {i: None for i in range(self.num_buffers)}
def get_dict(self):
return {
i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty
}
def refresh(self):
for i in range(self.num_buffers):
if self.buffers[i] is None and i not in self.empty:
self.buffers[i] = self.files[i].readline()
if self.buffers[i] == "":
self.empty.add(i)
self.files[i].close()
if len(self.empty) == self.num_buffers:
return False
return True
def unshift(self, index):
value = self.buffers[index]
self.buffers[index] = None
return value
class FileMerger:
def __init__(self, merge_strategy):
self.merge_strategy = merge_strategy
def merge(self, filenames, outfilename, buffer_size):
buffers = FilesArray(self.get_file_handles(filenames, buffer_size))
with open(outfilename, "w", buffer_size) as outfile:
while buffers.refresh():
min_index = self.merge_strategy.select(buffers.get_dict())
outfile.write(buffers.unshift(min_index))
def get_file_handles(self, filenames, buffer_size):
files = {}
for i in range(len(filenames)):
files[i] = open(filenames[i], "r", buffer_size)
return files
class ExternalSort:
def __init__(self, block_size):
self.block_size = block_size
def sort(self, filename, sort_key=None):
num_blocks = self.get_number_blocks(filename, self.block_size)
splitter = FileSplitter(filename)
splitter.split(self.block_size, sort_key)
merger = FileMerger(NWayMerge())
buffer_size = self.block_size / (num_blocks + 1)
merger.merge(splitter.get_block_filenames(), filename + ".out", buffer_size)
splitter.cleanup()
def get_number_blocks(self, filename, block_size):
return (os.stat(filename).st_size / block_size) + 1
def parse_memory(string):
if string[-1].lower() == "k":
return int(string[:-1]) * 1024
elif string[-1].lower() == "m":
return int(string[:-1]) * 1024 * 1024
elif string[-1].lower() == "g":
return int(string[:-1]) * 1024 * 1024 * 1024
else:
return int(string)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-m", "--mem", help="amount of memory to use for sorting", default="100M"
)
parser.add_argument(
"filename", metavar="<filename>", nargs=1, help="name of file to sort"
)
args = parser.parse_args()
sorter = ExternalSort(parse_memory(args.mem))
sorter.sort(args.filename[0])
if __name__ == "__main__":
main()
| #!/usr/bin/env python
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os
class FileSplitter:
BLOCK_FILENAME_FORMAT = "block_{0}.dat"
def __init__(self, filename):
self.filename = filename
self.block_filenames = []
def write_block(self, data, block_number):
filename = self.BLOCK_FILENAME_FORMAT.format(block_number)
with open(filename, "w") as file:
file.write(data)
self.block_filenames.append(filename)
def get_block_filenames(self):
return self.block_filenames
def split(self, block_size, sort_key=None):
i = 0
with open(self.filename) as file:
while True:
lines = file.readlines(block_size)
if lines == []:
break
if sort_key is None:
lines.sort()
else:
lines.sort(key=sort_key)
self.write_block("".join(lines), i)
i += 1
def cleanup(self):
map(lambda f: os.remove(f), self.block_filenames)
class NWayMerge:
def select(self, choices):
min_index = -1
min_str = None
for i in range(len(choices)):
if min_str is None or choices[i] < min_str:
min_index = i
return min_index
class FilesArray:
def __init__(self, files):
self.files = files
self.empty = set()
self.num_buffers = len(files)
self.buffers = {i: None for i in range(self.num_buffers)}
def get_dict(self):
return {
i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty
}
def refresh(self):
for i in range(self.num_buffers):
if self.buffers[i] is None and i not in self.empty:
self.buffers[i] = self.files[i].readline()
if self.buffers[i] == "":
self.empty.add(i)
self.files[i].close()
if len(self.empty) == self.num_buffers:
return False
return True
def unshift(self, index):
value = self.buffers[index]
self.buffers[index] = None
return value
class FileMerger:
def __init__(self, merge_strategy):
self.merge_strategy = merge_strategy
def merge(self, filenames, outfilename, buffer_size):
buffers = FilesArray(self.get_file_handles(filenames, buffer_size))
with open(outfilename, "w", buffer_size) as outfile:
while buffers.refresh():
min_index = self.merge_strategy.select(buffers.get_dict())
outfile.write(buffers.unshift(min_index))
def get_file_handles(self, filenames, buffer_size):
files = {}
for i in range(len(filenames)):
files[i] = open(filenames[i], "r", buffer_size)
return files
class ExternalSort:
def __init__(self, block_size):
self.block_size = block_size
def sort(self, filename, sort_key=None):
num_blocks = self.get_number_blocks(filename, self.block_size)
splitter = FileSplitter(filename)
splitter.split(self.block_size, sort_key)
merger = FileMerger(NWayMerge())
buffer_size = self.block_size / (num_blocks + 1)
merger.merge(splitter.get_block_filenames(), filename + ".out", buffer_size)
splitter.cleanup()
def get_number_blocks(self, filename, block_size):
return (os.stat(filename).st_size / block_size) + 1
def parse_memory(string):
if string[-1].lower() == "k":
return int(string[:-1]) * 1024
elif string[-1].lower() == "m":
return int(string[:-1]) * 1024 * 1024
elif string[-1].lower() == "g":
return int(string[:-1]) * 1024 * 1024 * 1024
else:
return int(string)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-m", "--mem", help="amount of memory to use for sorting", default="100M"
)
parser.add_argument(
"filename", metavar="<filename>", nargs=1, help="name of file to sort"
)
args = parser.parse_args()
sorter = ExternalSort(parse_memory(args.mem))
sorter.sort(args.filename[0])
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://www.hackerrank.com/challenges/abbr/problem
You can perform the following operation on some string, :
1. Capitalize zero or more of 's lowercase letters at some index i
(i.e., make them uppercase).
2. Delete all of the remaining lowercase letters in .
Example:
a=daBcd and b="ABC"
daBcd -> capitalize a and c(dABCd) -> remove d (ABC)
"""
def abbr(a: str, b: str) -> bool:
"""
>>> abbr("daBcd", "ABC")
True
>>> abbr("dBcd", "ABC")
False
"""
n = len(a)
m = len(b)
dp = [[False for _ in range(m + 1)] for _ in range(n + 1)]
dp[0][0] = True
for i in range(n):
for j in range(m + 1):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
dp[i + 1][j + 1] = True
if a[i].islower():
dp[i + 1][j] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
https://www.hackerrank.com/challenges/abbr/problem
You can perform the following operation on some string, :
1. Capitalize zero or more of 's lowercase letters at some index i
(i.e., make them uppercase).
2. Delete all of the remaining lowercase letters in .
Example:
a=daBcd and b="ABC"
daBcd -> capitalize a and c(dABCd) -> remove d (ABC)
"""
def abbr(a: str, b: str) -> bool:
"""
>>> abbr("daBcd", "ABC")
True
>>> abbr("dBcd", "ABC")
False
"""
n = len(a)
m = len(b)
dp = [[False for _ in range(m + 1)] for _ in range(n + 1)]
dp[0][0] = True
for i in range(n):
for j in range(m + 1):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
dp[i + 1][j + 1] = True
if a[i].islower():
dp[i + 1][j] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Greatest Common Divisor.
Wikipedia reference: https://en.wikipedia.org/wiki/Greatest_common_divisor
gcd(a, b) = gcd(a, -b) = gcd(-a, b) = gcd(-a, -b) by definition of divisibility
"""
def greatest_common_divisor(a: int, b: int) -> int:
"""
Calculate Greatest Common Divisor (GCD).
>>> greatest_common_divisor(24, 40)
8
>>> greatest_common_divisor(1, 1)
1
>>> greatest_common_divisor(1, 800)
1
>>> greatest_common_divisor(11, 37)
1
>>> greatest_common_divisor(3, 5)
1
>>> greatest_common_divisor(16, 4)
4
>>> greatest_common_divisor(-3, 9)
3
>>> greatest_common_divisor(9, -3)
3
>>> greatest_common_divisor(3, -9)
3
>>> greatest_common_divisor(-3, -9)
3
"""
return abs(b) if a == 0 else greatest_common_divisor(b % a, a)
def gcd_by_iterative(x: int, y: int) -> int:
"""
Below method is more memory efficient because it does not create additional
stack frames for recursive functions calls (as done in the above method).
>>> gcd_by_iterative(24, 40)
8
>>> greatest_common_divisor(24, 40) == gcd_by_iterative(24, 40)
True
>>> gcd_by_iterative(-3, -9)
3
>>> gcd_by_iterative(3, -9)
3
>>> gcd_by_iterative(1, -800)
1
>>> gcd_by_iterative(11, 37)
1
"""
while y: # --> when y=0 then loop will terminate and return x as final GCD.
x, y = y, x % y
return abs(x)
def main():
"""
Call Greatest Common Divisor function.
"""
try:
nums = input("Enter two integers separated by comma (,): ").split(",")
num_1 = int(nums[0])
num_2 = int(nums[1])
print(
f"greatest_common_divisor({num_1}, {num_2}) = "
f"{greatest_common_divisor(num_1, num_2)}"
)
print(f"By iterative gcd({num_1}, {num_2}) = {gcd_by_iterative(num_1, num_2)}")
except (IndexError, UnboundLocalError, ValueError):
print("Wrong input")
if __name__ == "__main__":
main()
| """
Greatest Common Divisor.
Wikipedia reference: https://en.wikipedia.org/wiki/Greatest_common_divisor
gcd(a, b) = gcd(a, -b) = gcd(-a, b) = gcd(-a, -b) by definition of divisibility
"""
def greatest_common_divisor(a: int, b: int) -> int:
"""
Calculate Greatest Common Divisor (GCD).
>>> greatest_common_divisor(24, 40)
8
>>> greatest_common_divisor(1, 1)
1
>>> greatest_common_divisor(1, 800)
1
>>> greatest_common_divisor(11, 37)
1
>>> greatest_common_divisor(3, 5)
1
>>> greatest_common_divisor(16, 4)
4
>>> greatest_common_divisor(-3, 9)
3
>>> greatest_common_divisor(9, -3)
3
>>> greatest_common_divisor(3, -9)
3
>>> greatest_common_divisor(-3, -9)
3
"""
return abs(b) if a == 0 else greatest_common_divisor(b % a, a)
def gcd_by_iterative(x: int, y: int) -> int:
"""
Below method is more memory efficient because it does not create additional
stack frames for recursive functions calls (as done in the above method).
>>> gcd_by_iterative(24, 40)
8
>>> greatest_common_divisor(24, 40) == gcd_by_iterative(24, 40)
True
>>> gcd_by_iterative(-3, -9)
3
>>> gcd_by_iterative(3, -9)
3
>>> gcd_by_iterative(1, -800)
1
>>> gcd_by_iterative(11, 37)
1
"""
while y: # --> when y=0 then loop will terminate and return x as final GCD.
x, y = y, x % y
return abs(x)
def main():
"""
Call Greatest Common Divisor function.
"""
try:
nums = input("Enter two integers separated by comma (,): ").split(",")
num_1 = int(nums[0])
num_2 = int(nums[1])
print(
f"greatest_common_divisor({num_1}, {num_2}) = "
f"{greatest_common_divisor(num_1, num_2)}"
)
print(f"By iterative gcd({num_1}, {num_2}) = {gcd_by_iterative(num_1, num_2)}")
except (IndexError, UnboundLocalError, ValueError):
print("Wrong input")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
from typing import Callable, Union
def line_length(
fnc: Callable[[Union[int, float]], Union[int, float]],
x_start: Union[int, float],
x_end: Union[int, float],
steps: int = 100,
) -> float:
"""
Approximates the arc length of a line segment by treating the curve as a
sequence of linear lines and summing their lengths
:param fnc: a function which defines a curve
:param x_start: left end point to indicate the start of line segment
:param x_end: right end point to indicate end of line segment
:param steps: an accuracy gauge; more steps increases accuracy
:return: a float representing the length of the curve
>>> def f(x):
... return x
>>> f"{line_length(f, 0, 1, 10):.6f}"
'1.414214'
>>> def f(x):
... return 1
>>> f"{line_length(f, -5.5, 4.5):.6f}"
'10.000000'
>>> def f(x):
... return math.sin(5 * x) + math.cos(10 * x) + x * x/10
>>> f"{line_length(f, 0.0, 10.0, 10000):.6f}"
'69.534930'
"""
x1 = x_start
fx1 = fnc(x_start)
length = 0.0
for i in range(steps):
# Approximates curve as a sequence of linear lines and sums their length
x2 = (x_end - x_start) / steps + x1
fx2 = fnc(x2)
length += math.hypot(x2 - x1, fx2 - fx1)
# Increment step
x1 = x2
fx1 = fx2
return length
if __name__ == "__main__":
def f(x):
return math.sin(10 * x)
print("f(x) = sin(10 * x)")
print("The length of the curve from x = -10 to x = 10 is:")
i = 10
while i <= 100000:
print(f"With {i} steps: {line_length(f, -10, 10, i)}")
i *= 10
| import math
from typing import Callable, Union
def line_length(
fnc: Callable[[Union[int, float]], Union[int, float]],
x_start: Union[int, float],
x_end: Union[int, float],
steps: int = 100,
) -> float:
"""
Approximates the arc length of a line segment by treating the curve as a
sequence of linear lines and summing their lengths
:param fnc: a function which defines a curve
:param x_start: left end point to indicate the start of line segment
:param x_end: right end point to indicate end of line segment
:param steps: an accuracy gauge; more steps increases accuracy
:return: a float representing the length of the curve
>>> def f(x):
... return x
>>> f"{line_length(f, 0, 1, 10):.6f}"
'1.414214'
>>> def f(x):
... return 1
>>> f"{line_length(f, -5.5, 4.5):.6f}"
'10.000000'
>>> def f(x):
... return math.sin(5 * x) + math.cos(10 * x) + x * x/10
>>> f"{line_length(f, 0.0, 10.0, 10000):.6f}"
'69.534930'
"""
x1 = x_start
fx1 = fnc(x_start)
length = 0.0
for i in range(steps):
# Approximates curve as a sequence of linear lines and sums their length
x2 = (x_end - x_start) / steps + x1
fx2 = fnc(x2)
length += math.hypot(x2 - x1, fx2 - fx1)
# Increment step
x1 = x2
fx1 = fx2
return length
if __name__ == "__main__":
def f(x):
return math.sin(10 * x)
print("f(x) = sin(10 * x)")
print("The length of the curve from x = -10 to x = 10 is:")
i = 10
while i <= 100000:
print(f"With {i} steps: {line_length(f, -10, 10, i)}")
i *= 10
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import Any
class Node:
def __init__(self, data: Any):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def print_list(self):
temp = self.head
while temp is not None:
print(temp.data, end=" ")
temp = temp.next
print()
# adding nodes
def push(self, new_data: Any):
new_node = Node(new_data)
new_node.next = self.head
self.head = new_node
# swapping nodes
def swap_nodes(self, node_data_1, node_data_2):
if node_data_1 == node_data_2:
return
else:
node_1 = self.head
while node_1 is not None and node_1.data != node_data_1:
node_1 = node_1.next
node_2 = self.head
while node_2 is not None and node_2.data != node_data_2:
node_2 = node_2.next
if node_1 is None or node_2 is None:
return
node_1.data, node_2.data = node_2.data, node_1.data
if __name__ == "__main__":
ll = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print("After swapping")
ll.print_list()
| from typing import Any
class Node:
def __init__(self, data: Any):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def print_list(self):
temp = self.head
while temp is not None:
print(temp.data, end=" ")
temp = temp.next
print()
# adding nodes
def push(self, new_data: Any):
new_node = Node(new_data)
new_node.next = self.head
self.head = new_node
# swapping nodes
def swap_nodes(self, node_data_1, node_data_2):
if node_data_1 == node_data_2:
return
else:
node_1 = self.head
while node_1 is not None and node_1.data != node_data_1:
node_1 = node_1.next
node_2 = self.head
while node_2 is not None and node_2.data != node_data_2:
node_2 = node_2.next
if node_1 is None or node_2 is None:
return
node_1.data, node_2.data = node_2.data, node_1.data
if __name__ == "__main__":
ll = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print("After swapping")
ll.print_list()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the merge sort algorithm
For doctests run following command:
python -m doctest -v merge_sort.py
or
python3 -m doctest -v merge_sort.py
For manual testing run:
python merge_sort.py
"""
def merge_sort(collection: list) -> list:
"""Pure implementation of the merge sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> merge_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> merge_sort([])
[]
>>> merge_sort([-2, -5, -45])
[-45, -5, -2]
"""
def merge(left: list, right: list) -> list:
"""merge left and right
:param left: left collection
:param right: right collection
:return: merge result
"""
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0)
yield from left
yield from right
return list(_merge())
if len(collection) <= 1:
return collection
mid = len(collection) // 2
return merge(merge_sort(collection[:mid]), merge_sort(collection[mid:]))
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| """
This is a pure Python implementation of the merge sort algorithm
For doctests run following command:
python -m doctest -v merge_sort.py
or
python3 -m doctest -v merge_sort.py
For manual testing run:
python merge_sort.py
"""
def merge_sort(collection: list) -> list:
"""Pure implementation of the merge sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> merge_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> merge_sort([])
[]
>>> merge_sort([-2, -5, -45])
[-45, -5, -2]
"""
def merge(left: list, right: list) -> list:
"""merge left and right
:param left: left collection
:param right: right collection
:return: merge result
"""
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0)
yield from left
yield from right
return list(_merge())
if len(collection) <= 1:
return collection
mid = len(collection) // 2
return merge(merge_sort(collection[:mid]), merge_sort(collection[mid:]))
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 50: https://projecteuler.net/problem=50
Consecutive prime sum
The prime 41, can be written as the sum of six consecutive primes:
41 = 2 + 3 + 5 + 7 + 11 + 13
This is the longest sum of consecutive primes that adds to a prime below
one-hundred.
The longest sum of consecutive primes below one-thousand that adds to a prime,
contains 21 terms, and is equal to 953.
Which prime, below one-million, can be written as the sum of the most
consecutive primes?
"""
from typing import List
def prime_sieve(limit: int) -> List[int]:
"""
Sieve of Erotosthenes
Function to return all the prime numbers up to a number 'limit'
https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
>>> prime_sieve(3)
[2]
>>> prime_sieve(50)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
"""
is_prime = [True] * limit
is_prime[0] = False
is_prime[1] = False
is_prime[2] = True
for i in range(3, int(limit ** 0.5 + 1), 2):
index = i * 2
while index < limit:
is_prime[index] = False
index = index + i
primes = [2]
for i in range(3, limit, 2):
if is_prime[i]:
primes.append(i)
return primes
def solution(ceiling: int = 1_000_000) -> int:
"""
Returns the biggest prime, below the celing, that can be written as the sum
of consecutive the most consecutive primes.
>>> solution(500)
499
>>> solution(1_000)
953
>>> solution(10_000)
9521
"""
primes = prime_sieve(ceiling)
length = 0
largest = 0
for i in range(len(primes)):
for j in range(i + length, len(primes)):
sol = sum(primes[i:j])
if sol >= ceiling:
break
if sol in primes:
length = j - i
largest = sol
return largest
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 50: https://projecteuler.net/problem=50
Consecutive prime sum
The prime 41, can be written as the sum of six consecutive primes:
41 = 2 + 3 + 5 + 7 + 11 + 13
This is the longest sum of consecutive primes that adds to a prime below
one-hundred.
The longest sum of consecutive primes below one-thousand that adds to a prime,
contains 21 terms, and is equal to 953.
Which prime, below one-million, can be written as the sum of the most
consecutive primes?
"""
from typing import List
def prime_sieve(limit: int) -> List[int]:
"""
Sieve of Erotosthenes
Function to return all the prime numbers up to a number 'limit'
https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
>>> prime_sieve(3)
[2]
>>> prime_sieve(50)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
"""
is_prime = [True] * limit
is_prime[0] = False
is_prime[1] = False
is_prime[2] = True
for i in range(3, int(limit ** 0.5 + 1), 2):
index = i * 2
while index < limit:
is_prime[index] = False
index = index + i
primes = [2]
for i in range(3, limit, 2):
if is_prime[i]:
primes.append(i)
return primes
def solution(ceiling: int = 1_000_000) -> int:
"""
Returns the biggest prime, below the celing, that can be written as the sum
of consecutive the most consecutive primes.
>>> solution(500)
499
>>> solution(1_000)
953
>>> solution(10_000)
9521
"""
primes = prime_sieve(ceiling)
length = 0
largest = 0
for i in range(len(primes)):
for j in range(i + length, len(primes)):
sol = sum(primes[i:j])
if sol >= ceiling:
break
if sol in primes:
length = j - i
largest = sol
return largest
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Information on binary shifts:
# https://docs.python.org/3/library/stdtypes.html#bitwise-operations-on-integer-types
# https://www.interviewcake.com/concept/java/bit-shift
def logical_left_shift(number: int, shift_amount: int) -> str:
"""
Take in 2 positive integers.
'number' is the integer to be logically left shifted 'shift_amount' times.
i.e. (number << shift_amount)
Return the shifted binary representation.
>>> logical_left_shift(0, 1)
'0b00'
>>> logical_left_shift(1, 1)
'0b10'
>>> logical_left_shift(1, 5)
'0b100000'
>>> logical_left_shift(17, 2)
'0b1000100'
>>> logical_left_shift(1983, 4)
'0b111101111110000'
>>> logical_left_shift(1, -1)
Traceback (most recent call last):
...
ValueError: both inputs must be positive integers
"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers")
binary_number = str(bin(number))
binary_number += "0" * shift_amount
return binary_number
def logical_right_shift(number: int, shift_amount: int) -> str:
"""
Take in positive 2 integers.
'number' is the integer to be logically right shifted 'shift_amount' times.
i.e. (number >>> shift_amount)
Return the shifted binary representation.
>>> logical_right_shift(0, 1)
'0b0'
>>> logical_right_shift(1, 1)
'0b0'
>>> logical_right_shift(1, 5)
'0b0'
>>> logical_right_shift(17, 2)
'0b100'
>>> logical_right_shift(1983, 4)
'0b1111011'
>>> logical_right_shift(1, -1)
Traceback (most recent call last):
...
ValueError: both inputs must be positive integers
"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers")
binary_number = str(bin(number))[2:]
if shift_amount >= len(binary_number):
return "0b0"
shifted_binary_number = binary_number[: len(binary_number) - shift_amount]
return "0b" + shifted_binary_number
def arithmetic_right_shift(number: int, shift_amount: int) -> str:
"""
Take in 2 integers.
'number' is the integer to be arithmetically right shifted 'shift_amount' times.
i.e. (number >> shift_amount)
Return the shifted binary representation.
>>> arithmetic_right_shift(0, 1)
'0b00'
>>> arithmetic_right_shift(1, 1)
'0b00'
>>> arithmetic_right_shift(-1, 1)
'0b11'
>>> arithmetic_right_shift(17, 2)
'0b000100'
>>> arithmetic_right_shift(-17, 2)
'0b111011'
>>> arithmetic_right_shift(-1983, 4)
'0b111110000100'
"""
if number >= 0: # Get binary representation of positive number
binary_number = "0" + str(bin(number)).strip("-")[2:]
else: # Get binary (2's complement) representation of negative number
binary_number_length = len(bin(number)[3:]) # Find 2's complement of number
binary_number = bin(abs(number) - (1 << binary_number_length))[3:]
binary_number = (
"1" + "0" * (binary_number_length - len(binary_number)) + binary_number
)
if shift_amount >= len(binary_number):
return "0b" + binary_number[0] * len(binary_number)
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(binary_number) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # Information on binary shifts:
# https://docs.python.org/3/library/stdtypes.html#bitwise-operations-on-integer-types
# https://www.interviewcake.com/concept/java/bit-shift
def logical_left_shift(number: int, shift_amount: int) -> str:
"""
Take in 2 positive integers.
'number' is the integer to be logically left shifted 'shift_amount' times.
i.e. (number << shift_amount)
Return the shifted binary representation.
>>> logical_left_shift(0, 1)
'0b00'
>>> logical_left_shift(1, 1)
'0b10'
>>> logical_left_shift(1, 5)
'0b100000'
>>> logical_left_shift(17, 2)
'0b1000100'
>>> logical_left_shift(1983, 4)
'0b111101111110000'
>>> logical_left_shift(1, -1)
Traceback (most recent call last):
...
ValueError: both inputs must be positive integers
"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers")
binary_number = str(bin(number))
binary_number += "0" * shift_amount
return binary_number
def logical_right_shift(number: int, shift_amount: int) -> str:
"""
Take in positive 2 integers.
'number' is the integer to be logically right shifted 'shift_amount' times.
i.e. (number >>> shift_amount)
Return the shifted binary representation.
>>> logical_right_shift(0, 1)
'0b0'
>>> logical_right_shift(1, 1)
'0b0'
>>> logical_right_shift(1, 5)
'0b0'
>>> logical_right_shift(17, 2)
'0b100'
>>> logical_right_shift(1983, 4)
'0b1111011'
>>> logical_right_shift(1, -1)
Traceback (most recent call last):
...
ValueError: both inputs must be positive integers
"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers")
binary_number = str(bin(number))[2:]
if shift_amount >= len(binary_number):
return "0b0"
shifted_binary_number = binary_number[: len(binary_number) - shift_amount]
return "0b" + shifted_binary_number
def arithmetic_right_shift(number: int, shift_amount: int) -> str:
"""
Take in 2 integers.
'number' is the integer to be arithmetically right shifted 'shift_amount' times.
i.e. (number >> shift_amount)
Return the shifted binary representation.
>>> arithmetic_right_shift(0, 1)
'0b00'
>>> arithmetic_right_shift(1, 1)
'0b00'
>>> arithmetic_right_shift(-1, 1)
'0b11'
>>> arithmetic_right_shift(17, 2)
'0b000100'
>>> arithmetic_right_shift(-17, 2)
'0b111011'
>>> arithmetic_right_shift(-1983, 4)
'0b111110000100'
"""
if number >= 0: # Get binary representation of positive number
binary_number = "0" + str(bin(number)).strip("-")[2:]
else: # Get binary (2's complement) representation of negative number
binary_number_length = len(bin(number)[3:]) # Find 2's complement of number
binary_number = bin(abs(number) - (1 << binary_number_length))[3:]
binary_number = (
"1" + "0" * (binary_number_length - len(binary_number)) + binary_number
)
if shift_amount >= len(binary_number):
return "0b" + binary_number[0] * len(binary_number)
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(binary_number) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The Mandelbrot set is the set of complex numbers "c" for which the series
"z_(n+1) = z_n * z_n + c" does not diverge, i.e. remains bounded. Thus, a
complex number "c" is a member of the Mandelbrot set if, when starting with
"z_0 = 0" and applying the iteration repeatedly, the absolute value of
"z_n" remains bounded for all "n > 0". Complex numbers can be written as
"a + b*i": "a" is the real component, usually drawn on the x-axis, and "b*i"
is the imaginary component, usually drawn on the y-axis. Most visualizations
of the Mandelbrot set use a color-coding to indicate after how many steps in
the series the numbers outside the set diverge. Images of the Mandelbrot set
exhibit an elaborate and infinitely complicated boundary that reveals
progressively ever-finer recursive detail at increasing magnifications, making
the boundary of the Mandelbrot set a fractal curve.
(description adapted from https://en.wikipedia.org/wiki/Mandelbrot_set )
(see also https://en.wikipedia.org/wiki/Plotting_algorithms_for_the_Mandelbrot_set )
"""
import colorsys
from PIL import Image # type: ignore
def get_distance(x: float, y: float, max_step: int) -> float:
"""
Return the relative distance (= step/max_step) after which the complex number
constituted by this x-y-pair diverges. Members of the Mandelbrot set do not
diverge so their distance is 1.
>>> get_distance(0, 0, 50)
1.0
>>> get_distance(0.5, 0.5, 50)
0.061224489795918366
>>> get_distance(2, 0, 50)
0.0
"""
a = x
b = y
for step in range(max_step):
a_new = a * a - b * b + x
b = 2 * a * b + y
a = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def get_black_and_white_rgb(distance: float) -> tuple:
"""
Black&white color-coding that ignores the relative distance. The Mandelbrot
set is black, everything else is white.
>>> get_black_and_white_rgb(0)
(255, 255, 255)
>>> get_black_and_white_rgb(0.5)
(255, 255, 255)
>>> get_black_and_white_rgb(1)
(0, 0, 0)
"""
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def get_color_coded_rgb(distance: float) -> tuple:
"""
Color-coding taking the relative distance into account. The Mandelbrot set
is black.
>>> get_color_coded_rgb(0)
(255, 0, 0)
>>> get_color_coded_rgb(0.5)
(0, 255, 255)
>>> get_color_coded_rgb(1)
(0, 0, 0)
"""
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(distance, 1, 1))
def get_image(
image_width: int = 800,
image_height: int = 600,
figure_center_x: float = -0.6,
figure_center_y: float = 0,
figure_width: float = 3.2,
max_step: int = 50,
use_distance_color_coding: bool = True,
) -> Image.Image:
"""
Function to generate the image of the Mandelbrot set. Two types of coordinates
are used: image-coordinates that refer to the pixels and figure-coordinates
that refer to the complex numbers inside and outside the Mandelbrot set. The
figure-coordinates in the arguments of this function determine which section
of the Mandelbrot set is viewed. The main area of the Mandelbrot set is
roughly between "-1.5 < x < 0.5" and "-1 < y < 1" in the figure-coordinates.
>>> get_image().load()[0,0]
(255, 0, 0)
>>> get_image(use_distance_color_coding = False).load()[0,0]
(255, 255, 255)
"""
img = Image.new("RGB", (image_width, image_height))
pixels = img.load()
# loop through the image-coordinates
for image_x in range(image_width):
for image_y in range(image_height):
# determine the figure-coordinates based on the image-coordinates
figure_height = figure_width / image_width * image_height
figure_x = figure_center_x + (image_x / image_width - 0.5) * figure_width
figure_y = figure_center_y + (image_y / image_height - 0.5) * figure_height
distance = get_distance(figure_x, figure_y, max_step)
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
pixels[image_x, image_y] = get_color_coded_rgb(distance)
else:
pixels[image_x, image_y] = get_black_and_white_rgb(distance)
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
img = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| """
The Mandelbrot set is the set of complex numbers "c" for which the series
"z_(n+1) = z_n * z_n + c" does not diverge, i.e. remains bounded. Thus, a
complex number "c" is a member of the Mandelbrot set if, when starting with
"z_0 = 0" and applying the iteration repeatedly, the absolute value of
"z_n" remains bounded for all "n > 0". Complex numbers can be written as
"a + b*i": "a" is the real component, usually drawn on the x-axis, and "b*i"
is the imaginary component, usually drawn on the y-axis. Most visualizations
of the Mandelbrot set use a color-coding to indicate after how many steps in
the series the numbers outside the set diverge. Images of the Mandelbrot set
exhibit an elaborate and infinitely complicated boundary that reveals
progressively ever-finer recursive detail at increasing magnifications, making
the boundary of the Mandelbrot set a fractal curve.
(description adapted from https://en.wikipedia.org/wiki/Mandelbrot_set )
(see also https://en.wikipedia.org/wiki/Plotting_algorithms_for_the_Mandelbrot_set )
"""
import colorsys
from PIL import Image # type: ignore
def get_distance(x: float, y: float, max_step: int) -> float:
"""
Return the relative distance (= step/max_step) after which the complex number
constituted by this x-y-pair diverges. Members of the Mandelbrot set do not
diverge so their distance is 1.
>>> get_distance(0, 0, 50)
1.0
>>> get_distance(0.5, 0.5, 50)
0.061224489795918366
>>> get_distance(2, 0, 50)
0.0
"""
a = x
b = y
for step in range(max_step):
a_new = a * a - b * b + x
b = 2 * a * b + y
a = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def get_black_and_white_rgb(distance: float) -> tuple:
"""
Black&white color-coding that ignores the relative distance. The Mandelbrot
set is black, everything else is white.
>>> get_black_and_white_rgb(0)
(255, 255, 255)
>>> get_black_and_white_rgb(0.5)
(255, 255, 255)
>>> get_black_and_white_rgb(1)
(0, 0, 0)
"""
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def get_color_coded_rgb(distance: float) -> tuple:
"""
Color-coding taking the relative distance into account. The Mandelbrot set
is black.
>>> get_color_coded_rgb(0)
(255, 0, 0)
>>> get_color_coded_rgb(0.5)
(0, 255, 255)
>>> get_color_coded_rgb(1)
(0, 0, 0)
"""
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(distance, 1, 1))
def get_image(
image_width: int = 800,
image_height: int = 600,
figure_center_x: float = -0.6,
figure_center_y: float = 0,
figure_width: float = 3.2,
max_step: int = 50,
use_distance_color_coding: bool = True,
) -> Image.Image:
"""
Function to generate the image of the Mandelbrot set. Two types of coordinates
are used: image-coordinates that refer to the pixels and figure-coordinates
that refer to the complex numbers inside and outside the Mandelbrot set. The
figure-coordinates in the arguments of this function determine which section
of the Mandelbrot set is viewed. The main area of the Mandelbrot set is
roughly between "-1.5 < x < 0.5" and "-1 < y < 1" in the figure-coordinates.
>>> get_image().load()[0,0]
(255, 0, 0)
>>> get_image(use_distance_color_coding = False).load()[0,0]
(255, 255, 255)
"""
img = Image.new("RGB", (image_width, image_height))
pixels = img.load()
# loop through the image-coordinates
for image_x in range(image_width):
for image_y in range(image_height):
# determine the figure-coordinates based on the image-coordinates
figure_height = figure_width / image_width * image_height
figure_x = figure_center_x + (image_x / image_width - 0.5) * figure_width
figure_y = figure_center_y + (image_y / image_height - 0.5) * figure_height
distance = get_distance(figure_x, figure_y, max_step)
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
pixels[image_x, image_y] = get_color_coded_rgb(distance)
else:
pixels[image_x, image_y] = get_black_and_white_rgb(distance)
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
img = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import List
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
def __repr__(self):
"""Returns a visual representation of the node and all its following nodes."""
string_rep = []
temp = self
while temp:
string_rep.append(f"{temp.data}")
temp = temp.next
return "->".join(string_rep)
def make_linked_list(elements_list: List):
"""Creates a Linked List from the elements of the given sequence
(list/tuple) and returns the head of the Linked List.
>>> make_linked_list([])
Traceback (most recent call last):
...
Exception: The Elements List is empty
>>> make_linked_list([7])
7
>>> make_linked_list(['abc'])
abc
>>> make_linked_list([7, 25])
7->25
"""
if not elements_list:
raise Exception("The Elements List is empty")
current = head = Node(elements_list[0])
for i in range(1, len(elements_list)):
current.next = Node(elements_list[i])
current = current.next
return head
def print_reverse(head_node: Node) -> None:
"""Prints the elements of the given Linked List in reverse order
>>> print_reverse([])
>>> linked_list = make_linked_list([69, 88, 73])
>>> print_reverse(linked_list)
73
88
69
"""
if head_node is not None and isinstance(head_node, Node):
print_reverse(head_node.next)
print(head_node.data)
def main():
from doctest import testmod
testmod()
linked_list = make_linked_list([14, 52, 14, 12, 43])
print("Linked List:")
print(linked_list)
print("Elements in Reverse:")
print_reverse(linked_list)
if __name__ == "__main__":
main()
| from typing import List
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
def __repr__(self):
"""Returns a visual representation of the node and all its following nodes."""
string_rep = []
temp = self
while temp:
string_rep.append(f"{temp.data}")
temp = temp.next
return "->".join(string_rep)
def make_linked_list(elements_list: List):
"""Creates a Linked List from the elements of the given sequence
(list/tuple) and returns the head of the Linked List.
>>> make_linked_list([])
Traceback (most recent call last):
...
Exception: The Elements List is empty
>>> make_linked_list([7])
7
>>> make_linked_list(['abc'])
abc
>>> make_linked_list([7, 25])
7->25
"""
if not elements_list:
raise Exception("The Elements List is empty")
current = head = Node(elements_list[0])
for i in range(1, len(elements_list)):
current.next = Node(elements_list[i])
current = current.next
return head
def print_reverse(head_node: Node) -> None:
"""Prints the elements of the given Linked List in reverse order
>>> print_reverse([])
>>> linked_list = make_linked_list([69, 88, 73])
>>> print_reverse(linked_list)
73
88
69
"""
if head_node is not None and isinstance(head_node, Node):
print_reverse(head_node.next)
print(head_node.data)
def main():
from doctest import testmod
testmod()
linked_list = make_linked_list([14, 52, 14, 12, 43])
print("Linked List:")
print(linked_list)
print("Elements in Reverse:")
print_reverse(linked_list)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| class FlowNetwork:
def __init__(self, graph, sources, sinks):
self.sourceIndex = None
self.sinkIndex = None
self.graph = graph
self._normalizeGraph(sources, sinks)
self.verticesCount = len(graph)
self.maximumFlowAlgorithm = None
# make only one source and one sink
def _normalizeGraph(self, sources, sinks):
if sources is int:
sources = [sources]
if sinks is int:
sinks = [sinks]
if len(sources) == 0 or len(sinks) == 0:
return
self.sourceIndex = sources[0]
self.sinkIndex = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(sources) > 1 or len(sinks) > 1:
maxInputFlow = 0
for i in sources:
maxInputFlow += sum(self.graph[i])
size = len(self.graph) + 1
for room in self.graph:
room.insert(0, 0)
self.graph.insert(0, [0] * size)
for i in sources:
self.graph[0][i + 1] = maxInputFlow
self.sourceIndex = 0
size = len(self.graph) + 1
for room in self.graph:
room.append(0)
self.graph.append([0] * size)
for i in sinks:
self.graph[i + 1][size - 1] = maxInputFlow
self.sinkIndex = size - 1
def findMaximumFlow(self):
if self.maximumFlowAlgorithm is None:
raise Exception("You need to set maximum flow algorithm before.")
if self.sourceIndex is None or self.sinkIndex is None:
return 0
self.maximumFlowAlgorithm.execute()
return self.maximumFlowAlgorithm.getMaximumFlow()
def setMaximumFlowAlgorithm(self, Algorithm):
self.maximumFlowAlgorithm = Algorithm(self)
class FlowNetworkAlgorithmExecutor:
def __init__(self, flowNetwork):
self.flowNetwork = flowNetwork
self.verticesCount = flowNetwork.verticesCount
self.sourceIndex = flowNetwork.sourceIndex
self.sinkIndex = flowNetwork.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
self.graph = flowNetwork.graph
self.executed = False
def execute(self):
if not self.executed:
self._algorithm()
self.executed = True
# You should override it
def _algorithm(self):
pass
class MaximumFlowAlgorithmExecutor(FlowNetworkAlgorithmExecutor):
def __init__(self, flowNetwork):
super().__init__(flowNetwork)
# use this to save your result
self.maximumFlow = -1
def getMaximumFlow(self):
if not self.executed:
raise Exception("You should execute algorithm before using its result!")
return self.maximumFlow
class PushRelabelExecutor(MaximumFlowAlgorithmExecutor):
def __init__(self, flowNetwork):
super().__init__(flowNetwork)
self.preflow = [[0] * self.verticesCount for i in range(self.verticesCount)]
self.heights = [0] * self.verticesCount
self.excesses = [0] * self.verticesCount
def _algorithm(self):
self.heights[self.sourceIndex] = self.verticesCount
# push some substance to graph
for nextVertexIndex, bandwidth in enumerate(self.graph[self.sourceIndex]):
self.preflow[self.sourceIndex][nextVertexIndex] += bandwidth
self.preflow[nextVertexIndex][self.sourceIndex] -= bandwidth
self.excesses[nextVertexIndex] += bandwidth
# Relabel-to-front selection rule
verticesList = [
i
for i in range(self.verticesCount)
if i != self.sourceIndex and i != self.sinkIndex
]
# move through list
i = 0
while i < len(verticesList):
vertexIndex = verticesList[i]
previousHeight = self.heights[vertexIndex]
self.processVertex(vertexIndex)
if self.heights[vertexIndex] > previousHeight:
# if it was relabeled, swap elements
# and start from 0 index
verticesList.insert(0, verticesList.pop(i))
i = 0
else:
i += 1
self.maximumFlow = sum(self.preflow[self.sourceIndex])
def processVertex(self, vertexIndex):
while self.excesses[vertexIndex] > 0:
for neighbourIndex in range(self.verticesCount):
# if it's neighbour and current vertex is higher
if (
self.graph[vertexIndex][neighbourIndex]
- self.preflow[vertexIndex][neighbourIndex]
> 0
and self.heights[vertexIndex] > self.heights[neighbourIndex]
):
self.push(vertexIndex, neighbourIndex)
self.relabel(vertexIndex)
def push(self, fromIndex, toIndex):
preflowDelta = min(
self.excesses[fromIndex],
self.graph[fromIndex][toIndex] - self.preflow[fromIndex][toIndex],
)
self.preflow[fromIndex][toIndex] += preflowDelta
self.preflow[toIndex][fromIndex] -= preflowDelta
self.excesses[fromIndex] -= preflowDelta
self.excesses[toIndex] += preflowDelta
def relabel(self, vertexIndex):
minHeight = None
for toIndex in range(self.verticesCount):
if (
self.graph[vertexIndex][toIndex] - self.preflow[vertexIndex][toIndex]
> 0
):
if minHeight is None or self.heights[toIndex] < minHeight:
minHeight = self.heights[toIndex]
if minHeight is not None:
self.heights[vertexIndex] = minHeight + 1
if __name__ == "__main__":
entrances = [0]
exits = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
graph = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
flowNetwork = FlowNetwork(graph, entrances, exits)
# set algorithm
flowNetwork.setMaximumFlowAlgorithm(PushRelabelExecutor)
# and calculate
maximumFlow = flowNetwork.findMaximumFlow()
print(f"maximum flow is {maximumFlow}")
| class FlowNetwork:
def __init__(self, graph, sources, sinks):
self.sourceIndex = None
self.sinkIndex = None
self.graph = graph
self._normalizeGraph(sources, sinks)
self.verticesCount = len(graph)
self.maximumFlowAlgorithm = None
# make only one source and one sink
def _normalizeGraph(self, sources, sinks):
if sources is int:
sources = [sources]
if sinks is int:
sinks = [sinks]
if len(sources) == 0 or len(sinks) == 0:
return
self.sourceIndex = sources[0]
self.sinkIndex = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(sources) > 1 or len(sinks) > 1:
maxInputFlow = 0
for i in sources:
maxInputFlow += sum(self.graph[i])
size = len(self.graph) + 1
for room in self.graph:
room.insert(0, 0)
self.graph.insert(0, [0] * size)
for i in sources:
self.graph[0][i + 1] = maxInputFlow
self.sourceIndex = 0
size = len(self.graph) + 1
for room in self.graph:
room.append(0)
self.graph.append([0] * size)
for i in sinks:
self.graph[i + 1][size - 1] = maxInputFlow
self.sinkIndex = size - 1
def findMaximumFlow(self):
if self.maximumFlowAlgorithm is None:
raise Exception("You need to set maximum flow algorithm before.")
if self.sourceIndex is None or self.sinkIndex is None:
return 0
self.maximumFlowAlgorithm.execute()
return self.maximumFlowAlgorithm.getMaximumFlow()
def setMaximumFlowAlgorithm(self, Algorithm):
self.maximumFlowAlgorithm = Algorithm(self)
class FlowNetworkAlgorithmExecutor:
def __init__(self, flowNetwork):
self.flowNetwork = flowNetwork
self.verticesCount = flowNetwork.verticesCount
self.sourceIndex = flowNetwork.sourceIndex
self.sinkIndex = flowNetwork.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
self.graph = flowNetwork.graph
self.executed = False
def execute(self):
if not self.executed:
self._algorithm()
self.executed = True
# You should override it
def _algorithm(self):
pass
class MaximumFlowAlgorithmExecutor(FlowNetworkAlgorithmExecutor):
def __init__(self, flowNetwork):
super().__init__(flowNetwork)
# use this to save your result
self.maximumFlow = -1
def getMaximumFlow(self):
if not self.executed:
raise Exception("You should execute algorithm before using its result!")
return self.maximumFlow
class PushRelabelExecutor(MaximumFlowAlgorithmExecutor):
def __init__(self, flowNetwork):
super().__init__(flowNetwork)
self.preflow = [[0] * self.verticesCount for i in range(self.verticesCount)]
self.heights = [0] * self.verticesCount
self.excesses = [0] * self.verticesCount
def _algorithm(self):
self.heights[self.sourceIndex] = self.verticesCount
# push some substance to graph
for nextVertexIndex, bandwidth in enumerate(self.graph[self.sourceIndex]):
self.preflow[self.sourceIndex][nextVertexIndex] += bandwidth
self.preflow[nextVertexIndex][self.sourceIndex] -= bandwidth
self.excesses[nextVertexIndex] += bandwidth
# Relabel-to-front selection rule
verticesList = [
i
for i in range(self.verticesCount)
if i != self.sourceIndex and i != self.sinkIndex
]
# move through list
i = 0
while i < len(verticesList):
vertexIndex = verticesList[i]
previousHeight = self.heights[vertexIndex]
self.processVertex(vertexIndex)
if self.heights[vertexIndex] > previousHeight:
# if it was relabeled, swap elements
# and start from 0 index
verticesList.insert(0, verticesList.pop(i))
i = 0
else:
i += 1
self.maximumFlow = sum(self.preflow[self.sourceIndex])
def processVertex(self, vertexIndex):
while self.excesses[vertexIndex] > 0:
for neighbourIndex in range(self.verticesCount):
# if it's neighbour and current vertex is higher
if (
self.graph[vertexIndex][neighbourIndex]
- self.preflow[vertexIndex][neighbourIndex]
> 0
and self.heights[vertexIndex] > self.heights[neighbourIndex]
):
self.push(vertexIndex, neighbourIndex)
self.relabel(vertexIndex)
def push(self, fromIndex, toIndex):
preflowDelta = min(
self.excesses[fromIndex],
self.graph[fromIndex][toIndex] - self.preflow[fromIndex][toIndex],
)
self.preflow[fromIndex][toIndex] += preflowDelta
self.preflow[toIndex][fromIndex] -= preflowDelta
self.excesses[fromIndex] -= preflowDelta
self.excesses[toIndex] += preflowDelta
def relabel(self, vertexIndex):
minHeight = None
for toIndex in range(self.verticesCount):
if (
self.graph[vertexIndex][toIndex] - self.preflow[vertexIndex][toIndex]
> 0
):
if minHeight is None or self.heights[toIndex] < minHeight:
minHeight = self.heights[toIndex]
if minHeight is not None:
self.heights[vertexIndex] = minHeight + 1
if __name__ == "__main__":
entrances = [0]
exits = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
graph = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
flowNetwork = FlowNetwork(graph, entrances, exits)
# set algorithm
flowNetwork.setMaximumFlowAlgorithm(PushRelabelExecutor)
# and calculate
maximumFlow = flowNetwork.findMaximumFlow()
print(f"maximum flow is {maximumFlow}")
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This algorithm (k=33) was first reported by Dan Bernstein many years ago in comp.lang.c
Another version of this algorithm (now favored by Bernstein) uses xor:
hash(i) = hash(i - 1) * 33 ^ str[i];
First Magic constant 33:
It has never been adequately explained.
It's magic because it works better than many other constants, prime or not.
Second Magic Constant 5381:
1. odd number
2. prime number
3. deficient number
4. 001/010/100/000/101 b
source: http://www.cse.yorku.ca/~oz/hash.html
"""
def djb2(s: str) -> int:
"""
Implementation of djb2 hash algorithm that
is popular because of it's magic constants.
>>> djb2('Algorithms')
3782405311
>>> djb2('scramble bits')
1609059040
"""
hash = 5381
for x in s:
hash = ((hash << 5) + hash) + ord(x)
return hash & 0xFFFFFFFF
| """
This algorithm (k=33) was first reported by Dan Bernstein many years ago in comp.lang.c
Another version of this algorithm (now favored by Bernstein) uses xor:
hash(i) = hash(i - 1) * 33 ^ str[i];
First Magic constant 33:
It has never been adequately explained.
It's magic because it works better than many other constants, prime or not.
Second Magic Constant 5381:
1. odd number
2. prime number
3. deficient number
4. 001/010/100/000/101 b
source: http://www.cse.yorku.ca/~oz/hash.html
"""
def djb2(s: str) -> int:
"""
Implementation of djb2 hash algorithm that
is popular because of it's magic constants.
>>> djb2('Algorithms')
3782405311
>>> djb2('scramble bits')
1609059040
"""
hash = 5381
for x in s:
hash = ((hash << 5) + hash) + ord(x)
return hash & 0xFFFFFFFF
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ Convert between different units of temperature """
def celsius_to_fahrenheit(celsius: float, ndigits: int = 2) -> float:
"""
Convert a given value from Celsius to Fahrenheit and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
>>> celsius_to_fahrenheit(273.354, 3)
524.037
>>> celsius_to_fahrenheit(273.354, 0)
524.0
>>> celsius_to_fahrenheit(-40.0)
-40.0
>>> celsius_to_fahrenheit(-20.0)
-4.0
>>> celsius_to_fahrenheit(0)
32.0
>>> celsius_to_fahrenheit(20)
68.0
>>> celsius_to_fahrenheit("40")
104.0
>>> celsius_to_fahrenheit("celsius")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'celsius'
"""
return round((float(celsius) * 9 / 5) + 32, ndigits)
def celsius_to_kelvin(celsius: float, ndigits: int = 2) -> float:
"""
Convert a given value from Celsius to Kelvin and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
>>> celsius_to_kelvin(273.354, 3)
546.504
>>> celsius_to_kelvin(273.354, 0)
547.0
>>> celsius_to_kelvin(0)
273.15
>>> celsius_to_kelvin(20.0)
293.15
>>> celsius_to_kelvin("40")
313.15
>>> celsius_to_kelvin("celsius")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'celsius'
"""
return round(float(celsius) + 273.15, ndigits)
def celsius_to_rankine(celsius: float, ndigits: int = 2) -> float:
"""
Convert a given value from Celsius to Rankine and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
>>> celsius_to_rankine(273.354, 3)
983.707
>>> celsius_to_rankine(273.354, 0)
984.0
>>> celsius_to_rankine(0)
491.67
>>> celsius_to_rankine(20.0)
527.67
>>> celsius_to_rankine("40")
563.67
>>> celsius_to_rankine("celsius")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'celsius'
"""
return round((float(celsius) * 9 / 5) + 491.67, ndigits)
def fahrenheit_to_celsius(fahrenheit: float, ndigits: int = 2) -> float:
"""
Convert a given value from Fahrenheit to Celsius and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
>>> fahrenheit_to_celsius(273.354, 3)
134.086
>>> fahrenheit_to_celsius(273.354, 0)
134.0
>>> fahrenheit_to_celsius(0)
-17.78
>>> fahrenheit_to_celsius(20.0)
-6.67
>>> fahrenheit_to_celsius(40.0)
4.44
>>> fahrenheit_to_celsius(60)
15.56
>>> fahrenheit_to_celsius(80)
26.67
>>> fahrenheit_to_celsius("100")
37.78
>>> fahrenheit_to_celsius("fahrenheit")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'fahrenheit'
"""
return round((float(fahrenheit) - 32) * 5 / 9, ndigits)
def fahrenheit_to_kelvin(fahrenheit: float, ndigits: int = 2) -> float:
"""
Convert a given value from Fahrenheit to Kelvin and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
>>> fahrenheit_to_kelvin(273.354, 3)
407.236
>>> fahrenheit_to_kelvin(273.354, 0)
407.0
>>> fahrenheit_to_kelvin(0)
255.37
>>> fahrenheit_to_kelvin(20.0)
266.48
>>> fahrenheit_to_kelvin(40.0)
277.59
>>> fahrenheit_to_kelvin(60)
288.71
>>> fahrenheit_to_kelvin(80)
299.82
>>> fahrenheit_to_kelvin("100")
310.93
>>> fahrenheit_to_kelvin("fahrenheit")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'fahrenheit'
"""
return round(((float(fahrenheit) - 32) * 5 / 9) + 273.15, ndigits)
def fahrenheit_to_rankine(fahrenheit: float, ndigits: int = 2) -> float:
"""
Convert a given value from Fahrenheit to Rankine and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
>>> fahrenheit_to_rankine(273.354, 3)
733.024
>>> fahrenheit_to_rankine(273.354, 0)
733.0
>>> fahrenheit_to_rankine(0)
459.67
>>> fahrenheit_to_rankine(20.0)
479.67
>>> fahrenheit_to_rankine(40.0)
499.67
>>> fahrenheit_to_rankine(60)
519.67
>>> fahrenheit_to_rankine(80)
539.67
>>> fahrenheit_to_rankine("100")
559.67
>>> fahrenheit_to_rankine("fahrenheit")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'fahrenheit'
"""
return round(float(fahrenheit) + 459.67, ndigits)
def kelvin_to_celsius(kelvin: float, ndigits: int = 2) -> float:
"""
Convert a given value from Kelvin to Celsius and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
>>> kelvin_to_celsius(273.354, 3)
0.204
>>> kelvin_to_celsius(273.354, 0)
0.0
>>> kelvin_to_celsius(273.15)
0.0
>>> kelvin_to_celsius(300)
26.85
>>> kelvin_to_celsius("315.5")
42.35
>>> kelvin_to_celsius("kelvin")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'kelvin'
"""
return round(float(kelvin) - 273.15, ndigits)
def kelvin_to_fahrenheit(kelvin: float, ndigits: int = 2) -> float:
"""
Convert a given value from Kelvin to Fahrenheit and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
>>> kelvin_to_fahrenheit(273.354, 3)
32.367
>>> kelvin_to_fahrenheit(273.354, 0)
32.0
>>> kelvin_to_fahrenheit(273.15)
32.0
>>> kelvin_to_fahrenheit(300)
80.33
>>> kelvin_to_fahrenheit("315.5")
108.23
>>> kelvin_to_fahrenheit("kelvin")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'kelvin'
"""
return round(((float(kelvin) - 273.15) * 9 / 5) + 32, ndigits)
def kelvin_to_rankine(kelvin: float, ndigits: int = 2) -> float:
"""
Convert a given value from Kelvin to Rankine and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
>>> kelvin_to_rankine(273.354, 3)
492.037
>>> kelvin_to_rankine(273.354, 0)
492.0
>>> kelvin_to_rankine(0)
0.0
>>> kelvin_to_rankine(20.0)
36.0
>>> kelvin_to_rankine("40")
72.0
>>> kelvin_to_rankine("kelvin")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'kelvin'
"""
return round((float(kelvin) * 9 / 5), ndigits)
def rankine_to_celsius(rankine: float, ndigits: int = 2) -> float:
"""
Convert a given value from Rankine to Celsius and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
>>> rankine_to_celsius(273.354, 3)
-121.287
>>> rankine_to_celsius(273.354, 0)
-121.0
>>> rankine_to_celsius(273.15)
-121.4
>>> rankine_to_celsius(300)
-106.48
>>> rankine_to_celsius("315.5")
-97.87
>>> rankine_to_celsius("rankine")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'rankine'
"""
return round((float(rankine) - 491.67) * 5 / 9, ndigits)
def rankine_to_fahrenheit(rankine: float, ndigits: int = 2) -> float:
"""
Convert a given value from Rankine to Fahrenheit and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
>>> rankine_to_fahrenheit(273.15)
-186.52
>>> rankine_to_fahrenheit(300)
-159.67
>>> rankine_to_fahrenheit("315.5")
-144.17
>>> rankine_to_fahrenheit("rankine")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'rankine'
"""
return round(float(rankine) - 459.67, ndigits)
def rankine_to_kelvin(rankine: float, ndigits: int = 2) -> float:
"""
Convert a given value from Rankine to Kelvin and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
>>> rankine_to_kelvin(0)
0.0
>>> rankine_to_kelvin(20.0)
11.11
>>> rankine_to_kelvin("40")
22.22
>>> rankine_to_kelvin("rankine")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'rankine'
"""
return round((float(rankine) * 5 / 9), ndigits)
def reaumur_to_kelvin(reaumur: float, ndigits: int = 2) -> float:
"""
Convert a given value from reaumur to Kelvin and round it to 2 decimal places.
Reference:- http://www.csgnetwork.com/temp2conv.html
>>> reaumur_to_kelvin(0)
273.15
>>> reaumur_to_kelvin(20.0)
298.15
>>> reaumur_to_kelvin(40)
323.15
>>> reaumur_to_kelvin("reaumur")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'reaumur'
"""
return round((float(reaumur) * 1.25 + 273.15), ndigits)
def reaumur_to_fahrenheit(reaumur: float, ndigits: int = 2) -> float:
"""
Convert a given value from reaumur to fahrenheit and round it to 2 decimal places.
Reference:- http://www.csgnetwork.com/temp2conv.html
>>> reaumur_to_fahrenheit(0)
32.0
>>> reaumur_to_fahrenheit(20.0)
77.0
>>> reaumur_to_fahrenheit(40)
122.0
>>> reaumur_to_fahrenheit("reaumur")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'reaumur'
"""
return round((float(reaumur) * 2.25 + 32), ndigits)
def reaumur_to_celsius(reaumur: float, ndigits: int = 2) -> float:
"""
Convert a given value from reaumur to celsius and round it to 2 decimal places.
Reference:- http://www.csgnetwork.com/temp2conv.html
>>> reaumur_to_celsius(0)
0.0
>>> reaumur_to_celsius(20.0)
25.0
>>> reaumur_to_celsius(40)
50.0
>>> reaumur_to_celsius("reaumur")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'reaumur'
"""
return round((float(reaumur) * 1.25), ndigits)
def reaumur_to_rankine(reaumur: float, ndigits: int = 2) -> float:
"""
Convert a given value from reaumur to rankine and round it to 2 decimal places.
Reference:- http://www.csgnetwork.com/temp2conv.html
>>> reaumur_to_rankine(0)
491.67
>>> reaumur_to_rankine(20.0)
536.67
>>> reaumur_to_rankine(40)
581.67
>>> reaumur_to_rankine("reaumur")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'reaumur'
"""
return round((float(reaumur) * 2.25 + 32 + 459.67), ndigits)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """ Convert between different units of temperature """
def celsius_to_fahrenheit(celsius: float, ndigits: int = 2) -> float:
"""
Convert a given value from Celsius to Fahrenheit and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
>>> celsius_to_fahrenheit(273.354, 3)
524.037
>>> celsius_to_fahrenheit(273.354, 0)
524.0
>>> celsius_to_fahrenheit(-40.0)
-40.0
>>> celsius_to_fahrenheit(-20.0)
-4.0
>>> celsius_to_fahrenheit(0)
32.0
>>> celsius_to_fahrenheit(20)
68.0
>>> celsius_to_fahrenheit("40")
104.0
>>> celsius_to_fahrenheit("celsius")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'celsius'
"""
return round((float(celsius) * 9 / 5) + 32, ndigits)
def celsius_to_kelvin(celsius: float, ndigits: int = 2) -> float:
"""
Convert a given value from Celsius to Kelvin and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
>>> celsius_to_kelvin(273.354, 3)
546.504
>>> celsius_to_kelvin(273.354, 0)
547.0
>>> celsius_to_kelvin(0)
273.15
>>> celsius_to_kelvin(20.0)
293.15
>>> celsius_to_kelvin("40")
313.15
>>> celsius_to_kelvin("celsius")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'celsius'
"""
return round(float(celsius) + 273.15, ndigits)
def celsius_to_rankine(celsius: float, ndigits: int = 2) -> float:
"""
Convert a given value from Celsius to Rankine and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
>>> celsius_to_rankine(273.354, 3)
983.707
>>> celsius_to_rankine(273.354, 0)
984.0
>>> celsius_to_rankine(0)
491.67
>>> celsius_to_rankine(20.0)
527.67
>>> celsius_to_rankine("40")
563.67
>>> celsius_to_rankine("celsius")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'celsius'
"""
return round((float(celsius) * 9 / 5) + 491.67, ndigits)
def fahrenheit_to_celsius(fahrenheit: float, ndigits: int = 2) -> float:
"""
Convert a given value from Fahrenheit to Celsius and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
>>> fahrenheit_to_celsius(273.354, 3)
134.086
>>> fahrenheit_to_celsius(273.354, 0)
134.0
>>> fahrenheit_to_celsius(0)
-17.78
>>> fahrenheit_to_celsius(20.0)
-6.67
>>> fahrenheit_to_celsius(40.0)
4.44
>>> fahrenheit_to_celsius(60)
15.56
>>> fahrenheit_to_celsius(80)
26.67
>>> fahrenheit_to_celsius("100")
37.78
>>> fahrenheit_to_celsius("fahrenheit")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'fahrenheit'
"""
return round((float(fahrenheit) - 32) * 5 / 9, ndigits)
def fahrenheit_to_kelvin(fahrenheit: float, ndigits: int = 2) -> float:
"""
Convert a given value from Fahrenheit to Kelvin and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
>>> fahrenheit_to_kelvin(273.354, 3)
407.236
>>> fahrenheit_to_kelvin(273.354, 0)
407.0
>>> fahrenheit_to_kelvin(0)
255.37
>>> fahrenheit_to_kelvin(20.0)
266.48
>>> fahrenheit_to_kelvin(40.0)
277.59
>>> fahrenheit_to_kelvin(60)
288.71
>>> fahrenheit_to_kelvin(80)
299.82
>>> fahrenheit_to_kelvin("100")
310.93
>>> fahrenheit_to_kelvin("fahrenheit")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'fahrenheit'
"""
return round(((float(fahrenheit) - 32) * 5 / 9) + 273.15, ndigits)
def fahrenheit_to_rankine(fahrenheit: float, ndigits: int = 2) -> float:
"""
Convert a given value from Fahrenheit to Rankine and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
>>> fahrenheit_to_rankine(273.354, 3)
733.024
>>> fahrenheit_to_rankine(273.354, 0)
733.0
>>> fahrenheit_to_rankine(0)
459.67
>>> fahrenheit_to_rankine(20.0)
479.67
>>> fahrenheit_to_rankine(40.0)
499.67
>>> fahrenheit_to_rankine(60)
519.67
>>> fahrenheit_to_rankine(80)
539.67
>>> fahrenheit_to_rankine("100")
559.67
>>> fahrenheit_to_rankine("fahrenheit")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'fahrenheit'
"""
return round(float(fahrenheit) + 459.67, ndigits)
def kelvin_to_celsius(kelvin: float, ndigits: int = 2) -> float:
"""
Convert a given value from Kelvin to Celsius and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
>>> kelvin_to_celsius(273.354, 3)
0.204
>>> kelvin_to_celsius(273.354, 0)
0.0
>>> kelvin_to_celsius(273.15)
0.0
>>> kelvin_to_celsius(300)
26.85
>>> kelvin_to_celsius("315.5")
42.35
>>> kelvin_to_celsius("kelvin")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'kelvin'
"""
return round(float(kelvin) - 273.15, ndigits)
def kelvin_to_fahrenheit(kelvin: float, ndigits: int = 2) -> float:
"""
Convert a given value from Kelvin to Fahrenheit and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
>>> kelvin_to_fahrenheit(273.354, 3)
32.367
>>> kelvin_to_fahrenheit(273.354, 0)
32.0
>>> kelvin_to_fahrenheit(273.15)
32.0
>>> kelvin_to_fahrenheit(300)
80.33
>>> kelvin_to_fahrenheit("315.5")
108.23
>>> kelvin_to_fahrenheit("kelvin")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'kelvin'
"""
return round(((float(kelvin) - 273.15) * 9 / 5) + 32, ndigits)
def kelvin_to_rankine(kelvin: float, ndigits: int = 2) -> float:
"""
Convert a given value from Kelvin to Rankine and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
>>> kelvin_to_rankine(273.354, 3)
492.037
>>> kelvin_to_rankine(273.354, 0)
492.0
>>> kelvin_to_rankine(0)
0.0
>>> kelvin_to_rankine(20.0)
36.0
>>> kelvin_to_rankine("40")
72.0
>>> kelvin_to_rankine("kelvin")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'kelvin'
"""
return round((float(kelvin) * 9 / 5), ndigits)
def rankine_to_celsius(rankine: float, ndigits: int = 2) -> float:
"""
Convert a given value from Rankine to Celsius and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
Wikipedia reference: https://en.wikipedia.org/wiki/Celsius
>>> rankine_to_celsius(273.354, 3)
-121.287
>>> rankine_to_celsius(273.354, 0)
-121.0
>>> rankine_to_celsius(273.15)
-121.4
>>> rankine_to_celsius(300)
-106.48
>>> rankine_to_celsius("315.5")
-97.87
>>> rankine_to_celsius("rankine")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'rankine'
"""
return round((float(rankine) - 491.67) * 5 / 9, ndigits)
def rankine_to_fahrenheit(rankine: float, ndigits: int = 2) -> float:
"""
Convert a given value from Rankine to Fahrenheit and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
Wikipedia reference: https://en.wikipedia.org/wiki/Fahrenheit
>>> rankine_to_fahrenheit(273.15)
-186.52
>>> rankine_to_fahrenheit(300)
-159.67
>>> rankine_to_fahrenheit("315.5")
-144.17
>>> rankine_to_fahrenheit("rankine")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'rankine'
"""
return round(float(rankine) - 459.67, ndigits)
def rankine_to_kelvin(rankine: float, ndigits: int = 2) -> float:
"""
Convert a given value from Rankine to Kelvin and round it to 2 decimal places.
Wikipedia reference: https://en.wikipedia.org/wiki/Rankine_scale
Wikipedia reference: https://en.wikipedia.org/wiki/Kelvin
>>> rankine_to_kelvin(0)
0.0
>>> rankine_to_kelvin(20.0)
11.11
>>> rankine_to_kelvin("40")
22.22
>>> rankine_to_kelvin("rankine")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'rankine'
"""
return round((float(rankine) * 5 / 9), ndigits)
def reaumur_to_kelvin(reaumur: float, ndigits: int = 2) -> float:
"""
Convert a given value from reaumur to Kelvin and round it to 2 decimal places.
Reference:- http://www.csgnetwork.com/temp2conv.html
>>> reaumur_to_kelvin(0)
273.15
>>> reaumur_to_kelvin(20.0)
298.15
>>> reaumur_to_kelvin(40)
323.15
>>> reaumur_to_kelvin("reaumur")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'reaumur'
"""
return round((float(reaumur) * 1.25 + 273.15), ndigits)
def reaumur_to_fahrenheit(reaumur: float, ndigits: int = 2) -> float:
"""
Convert a given value from reaumur to fahrenheit and round it to 2 decimal places.
Reference:- http://www.csgnetwork.com/temp2conv.html
>>> reaumur_to_fahrenheit(0)
32.0
>>> reaumur_to_fahrenheit(20.0)
77.0
>>> reaumur_to_fahrenheit(40)
122.0
>>> reaumur_to_fahrenheit("reaumur")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'reaumur'
"""
return round((float(reaumur) * 2.25 + 32), ndigits)
def reaumur_to_celsius(reaumur: float, ndigits: int = 2) -> float:
"""
Convert a given value from reaumur to celsius and round it to 2 decimal places.
Reference:- http://www.csgnetwork.com/temp2conv.html
>>> reaumur_to_celsius(0)
0.0
>>> reaumur_to_celsius(20.0)
25.0
>>> reaumur_to_celsius(40)
50.0
>>> reaumur_to_celsius("reaumur")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'reaumur'
"""
return round((float(reaumur) * 1.25), ndigits)
def reaumur_to_rankine(reaumur: float, ndigits: int = 2) -> float:
"""
Convert a given value from reaumur to rankine and round it to 2 decimal places.
Reference:- http://www.csgnetwork.com/temp2conv.html
>>> reaumur_to_rankine(0)
491.67
>>> reaumur_to_rankine(20.0)
536.67
>>> reaumur_to_rankine(40)
581.67
>>> reaumur_to_rankine("reaumur")
Traceback (most recent call last):
...
ValueError: could not convert string to float: 'reaumur'
"""
return round((float(reaumur) * 2.25 + 32 + 459.67), ndigits)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Minimum cut on Ford_Fulkerson algorithm.
test_graph = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def BFS(graph, s, t, parent):
# Return True if there is node that has not iterated.
visited = [False] * len(graph)
queue = [s]
visited[s] = True
while queue:
u = queue.pop(0)
for ind in range(len(graph[u])):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(ind)
visited[ind] = True
parent[ind] = u
return True if visited[t] else False
def mincut(graph, source, sink):
"""This array is filled by BFS and to store path
>>> mincut(test_graph, source=0, sink=5)
[(1, 3), (4, 3), (4, 5)]
"""
parent = [-1] * (len(graph))
max_flow = 0
res = []
temp = [i[:] for i in graph] # Record original cut, copy.
while BFS(graph, source, sink, parent):
path_flow = float("Inf")
s = sink
while s != source:
# Find the minimum value in select path
path_flow = min(path_flow, graph[parent[s]][s])
s = parent[s]
max_flow += path_flow
v = sink
while v != source:
u = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
v = parent[v]
for i in range(len(graph)):
for j in range(len(graph[0])):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j))
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| # Minimum cut on Ford_Fulkerson algorithm.
test_graph = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def BFS(graph, s, t, parent):
# Return True if there is node that has not iterated.
visited = [False] * len(graph)
queue = [s]
visited[s] = True
while queue:
u = queue.pop(0)
for ind in range(len(graph[u])):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(ind)
visited[ind] = True
parent[ind] = u
return True if visited[t] else False
def mincut(graph, source, sink):
"""This array is filled by BFS and to store path
>>> mincut(test_graph, source=0, sink=5)
[(1, 3), (4, 3), (4, 5)]
"""
parent = [-1] * (len(graph))
max_flow = 0
res = []
temp = [i[:] for i in graph] # Record original cut, copy.
while BFS(graph, source, sink, parent):
path_flow = float("Inf")
s = sink
while s != source:
# Find the minimum value in select path
path_flow = min(path_flow, graph[parent[s]][s])
s = parent[s]
max_flow += path_flow
v = sink
while v != source:
u = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
v = parent[v]
for i in range(len(graph)):
for j in range(len(graph[0])):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j))
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # flake8: noqa
"""
Binomial Heap
Reference: Advanced Data Structures, Peter Brass
"""
class Node:
"""
Node in a doubly-linked binomial tree, containing:
- value
- size of left subtree
- link to left, right and parent nodes
"""
def __init__(self, val):
self.val = val
# Number of nodes in left subtree
self.left_tree_size = 0
self.left = None
self.right = None
self.parent = None
def mergeTrees(self, other):
"""
In-place merge of two binomial trees of equal size.
Returns the root of the resulting tree
"""
assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
if self.val < other.val:
other.left = self.right
other.parent = None
if self.right:
self.right.parent = other
self.right = other
self.left_tree_size = self.left_tree_size * 2 + 1
return self
else:
self.left = other.right
self.parent = None
if other.right:
other.right.parent = self
other.right = self
other.left_tree_size = other.left_tree_size * 2 + 1
return other
class BinomialHeap:
r"""
Min-oriented priority queue implemented with the Binomial Heap data
structure implemented with the BinomialHeap class. It supports:
- Insert element in a heap with n elements: Guaranteed logn, amoratized 1
- Merge (meld) heaps of size m and n: O(logn + logm)
- Delete Min: O(logn)
- Peek (return min without deleting it): O(1)
Example:
Create a random permutation of 30 integers to be inserted and 19 of them deleted
>>> import numpy as np
>>> permutation = np.random.permutation(list(range(30)))
Create a Heap and insert the 30 integers
__init__() test
>>> first_heap = BinomialHeap()
30 inserts - insert() test
>>> for number in permutation:
... first_heap.insert(number)
Size test
>>> print(first_heap.size)
30
Deleting - delete() test
>>> for i in range(25):
... print(first_heap.deleteMin(), end=" ")
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Create a new Heap
>>> second_heap = BinomialHeap()
>>> vals = [17, 20, 31, 34]
>>> for value in vals:
... second_heap.insert(value)
The heap should have the following structure:
17
/ \
# 31
/ \
20 34
/ \ / \
# # # #
preOrder() test
>>> print(second_heap.preOrder())
[(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)]
printing Heap - __str__() test
>>> print(second_heap)
17
-#
-31
--20
---#
---#
--34
---#
---#
mergeHeaps() test
>>> merged = second_heap.mergeHeaps(first_heap)
>>> merged.peek()
17
values in merged heap; (merge is inplace)
>>> while not first_heap.isEmpty():
... print(first_heap.deleteMin(), end=" ")
17 20 25 26 27 28 29 31 34
"""
def __init__(self, bottom_root=None, min_node=None, heap_size=0):
self.size = heap_size
self.bottom_root = bottom_root
self.min_node = min_node
def mergeHeaps(self, other):
"""
In-place merge of two binomial heaps.
Both of them become the resulting merged heap
"""
# Empty heaps corner cases
if other.size == 0:
return
if self.size == 0:
self.size = other.size
self.bottom_root = other.bottom_root
self.min_node = other.min_node
return
# Update size
self.size = self.size + other.size
# Update min.node
if self.min_node.val > other.min_node.val:
self.min_node = other.min_node
# Merge
# Order roots by left_subtree_size
combined_roots_list = []
i, j = self.bottom_root, other.bottom_root
while i or j:
if i and ((not j) or i.left_tree_size < j.left_tree_size):
combined_roots_list.append((i, True))
i = i.parent
else:
combined_roots_list.append((j, False))
j = j.parent
# Insert links between them
for i in range(len(combined_roots_list) - 1):
if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
combined_roots_list[i][0].parent = combined_roots_list[i + 1][0]
combined_roots_list[i + 1][0].left = combined_roots_list[i][0]
# Consecutively merge roots with same left_tree_size
i = combined_roots_list[0][0]
while i.parent:
if (
(i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
) or (
i.left_tree_size == i.parent.left_tree_size
and i.left_tree_size != i.parent.parent.left_tree_size
):
# Neighbouring Nodes
previous_node = i.left
next_node = i.parent.parent
# Merging trees
i = i.mergeTrees(i.parent)
# Updating links
i.left = previous_node
i.parent = next_node
if previous_node:
previous_node.parent = i
if next_node:
next_node.left = i
else:
i = i.parent
# Updating self.bottom_root
while i.left:
i = i.left
self.bottom_root = i
# Update other
other.size = self.size
other.bottom_root = self.bottom_root
other.min_node = self.min_node
# Return the merged heap
return self
def insert(self, val):
"""
insert a value in the heap
"""
if self.size == 0:
self.bottom_root = Node(val)
self.size = 1
self.min_node = self.bottom_root
else:
# Create new node
new_node = Node(val)
# Update size
self.size += 1
# update min_node
if val < self.min_node.val:
self.min_node = new_node
# Put new_node as a bottom_root in heap
self.bottom_root.left = new_node
new_node.parent = self.bottom_root
self.bottom_root = new_node
# Consecutively merge roots with same left_tree_size
while (
self.bottom_root.parent
and self.bottom_root.left_tree_size
== self.bottom_root.parent.left_tree_size
):
# Next node
next_node = self.bottom_root.parent.parent
# Merge
self.bottom_root = self.bottom_root.mergeTrees(self.bottom_root.parent)
# Update Links
self.bottom_root.parent = next_node
self.bottom_root.left = None
if next_node:
next_node.left = self.bottom_root
def peek(self):
"""
return min element without deleting it
"""
return self.min_node.val
def isEmpty(self):
return self.size == 0
def deleteMin(self):
"""
delete min element and return it
"""
# assert not self.isEmpty(), "Empty Heap"
# Save minimal value
min_value = self.min_node.val
# Last element in heap corner case
if self.size == 1:
# Update size
self.size = 0
# Update bottom root
self.bottom_root = None
# Update min_node
self.min_node = None
return min_value
# No right subtree corner case
# The structure of the tree implies that this should be the bottom root
# and there is at least one other root
if self.min_node.right is None:
# Update size
self.size -= 1
# Update bottom root
self.bottom_root = self.bottom_root.parent
self.bottom_root.left = None
# Update min_node
self.min_node = self.bottom_root
i = self.bottom_root.parent
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
return min_value
# General case
# Find the BinomialHeap of the right subtree of min_node
bottom_of_new = self.min_node.right
bottom_of_new.parent = None
min_of_new = bottom_of_new
size_of_new = 1
# Size, min_node and bottom_root
while bottom_of_new.left:
size_of_new = size_of_new * 2 + 1
bottom_of_new = bottom_of_new.left
if bottom_of_new.val < min_of_new.val:
min_of_new = bottom_of_new
# Corner case of single root on top left path
if (not self.min_node.left) and (not self.min_node.parent):
self.size = size_of_new
self.bottom_root = bottom_of_new
self.min_node = min_of_new
# print("Single root, multiple nodes case")
return min_value
# Remaining cases
# Construct heap of right subtree
newHeap = BinomialHeap(
bottom_root=bottom_of_new, min_node=min_of_new, heap_size=size_of_new
)
# Update size
self.size = self.size - 1 - size_of_new
# Neighbour nodes
previous_node = self.min_node.left
next_node = self.min_node.parent
# Initialize new bottom_root and min_node
self.min_node = previous_node or next_node
self.bottom_root = next_node
# Update links of previous_node and search below for new min_node and
# bottom_root
if previous_node:
previous_node.parent = next_node
# Update bottom_root and search for min_node below
self.bottom_root = previous_node
self.min_node = previous_node
while self.bottom_root.left:
self.bottom_root = self.bottom_root.left
if self.bottom_root.val < self.min_node.val:
self.min_node = self.bottom_root
if next_node:
next_node.left = previous_node
# Search for new min_node above min_node
i = next_node
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
# Merge heaps
self.mergeHeaps(newHeap)
return min_value
def preOrder(self):
"""
Returns the Pre-order representation of the heap including
values of nodes plus their level distance from the root;
Empty nodes appear as #
"""
# Find top root
top_root = self.bottom_root
while top_root.parent:
top_root = top_root.parent
# preorder
heap_preOrder = []
self.__traversal(top_root, heap_preOrder)
return heap_preOrder
def __traversal(self, curr_node, preorder, level=0):
"""
Pre-order traversal of nodes
"""
if curr_node:
preorder.append((curr_node.val, level))
self.__traversal(curr_node.left, preorder, level + 1)
self.__traversal(curr_node.right, preorder, level + 1)
else:
preorder.append(("#", level))
def __str__(self):
"""
Overwriting str for a pre-order print of nodes in heap;
Performance is poor, so use only for small examples
"""
if self.isEmpty():
return ""
preorder_heap = self.preOrder()
return "\n".join(("-" * level + str(value)) for value, level in preorder_heap)
# Unit Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| # flake8: noqa
"""
Binomial Heap
Reference: Advanced Data Structures, Peter Brass
"""
class Node:
"""
Node in a doubly-linked binomial tree, containing:
- value
- size of left subtree
- link to left, right and parent nodes
"""
def __init__(self, val):
self.val = val
# Number of nodes in left subtree
self.left_tree_size = 0
self.left = None
self.right = None
self.parent = None
def mergeTrees(self, other):
"""
In-place merge of two binomial trees of equal size.
Returns the root of the resulting tree
"""
assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
if self.val < other.val:
other.left = self.right
other.parent = None
if self.right:
self.right.parent = other
self.right = other
self.left_tree_size = self.left_tree_size * 2 + 1
return self
else:
self.left = other.right
self.parent = None
if other.right:
other.right.parent = self
other.right = self
other.left_tree_size = other.left_tree_size * 2 + 1
return other
class BinomialHeap:
r"""
Min-oriented priority queue implemented with the Binomial Heap data
structure implemented with the BinomialHeap class. It supports:
- Insert element in a heap with n elements: Guaranteed logn, amoratized 1
- Merge (meld) heaps of size m and n: O(logn + logm)
- Delete Min: O(logn)
- Peek (return min without deleting it): O(1)
Example:
Create a random permutation of 30 integers to be inserted and 19 of them deleted
>>> import numpy as np
>>> permutation = np.random.permutation(list(range(30)))
Create a Heap and insert the 30 integers
__init__() test
>>> first_heap = BinomialHeap()
30 inserts - insert() test
>>> for number in permutation:
... first_heap.insert(number)
Size test
>>> print(first_heap.size)
30
Deleting - delete() test
>>> for i in range(25):
... print(first_heap.deleteMin(), end=" ")
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Create a new Heap
>>> second_heap = BinomialHeap()
>>> vals = [17, 20, 31, 34]
>>> for value in vals:
... second_heap.insert(value)
The heap should have the following structure:
17
/ \
# 31
/ \
20 34
/ \ / \
# # # #
preOrder() test
>>> print(second_heap.preOrder())
[(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)]
printing Heap - __str__() test
>>> print(second_heap)
17
-#
-31
--20
---#
---#
--34
---#
---#
mergeHeaps() test
>>> merged = second_heap.mergeHeaps(first_heap)
>>> merged.peek()
17
values in merged heap; (merge is inplace)
>>> while not first_heap.isEmpty():
... print(first_heap.deleteMin(), end=" ")
17 20 25 26 27 28 29 31 34
"""
def __init__(self, bottom_root=None, min_node=None, heap_size=0):
self.size = heap_size
self.bottom_root = bottom_root
self.min_node = min_node
def mergeHeaps(self, other):
"""
In-place merge of two binomial heaps.
Both of them become the resulting merged heap
"""
# Empty heaps corner cases
if other.size == 0:
return
if self.size == 0:
self.size = other.size
self.bottom_root = other.bottom_root
self.min_node = other.min_node
return
# Update size
self.size = self.size + other.size
# Update min.node
if self.min_node.val > other.min_node.val:
self.min_node = other.min_node
# Merge
# Order roots by left_subtree_size
combined_roots_list = []
i, j = self.bottom_root, other.bottom_root
while i or j:
if i and ((not j) or i.left_tree_size < j.left_tree_size):
combined_roots_list.append((i, True))
i = i.parent
else:
combined_roots_list.append((j, False))
j = j.parent
# Insert links between them
for i in range(len(combined_roots_list) - 1):
if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
combined_roots_list[i][0].parent = combined_roots_list[i + 1][0]
combined_roots_list[i + 1][0].left = combined_roots_list[i][0]
# Consecutively merge roots with same left_tree_size
i = combined_roots_list[0][0]
while i.parent:
if (
(i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
) or (
i.left_tree_size == i.parent.left_tree_size
and i.left_tree_size != i.parent.parent.left_tree_size
):
# Neighbouring Nodes
previous_node = i.left
next_node = i.parent.parent
# Merging trees
i = i.mergeTrees(i.parent)
# Updating links
i.left = previous_node
i.parent = next_node
if previous_node:
previous_node.parent = i
if next_node:
next_node.left = i
else:
i = i.parent
# Updating self.bottom_root
while i.left:
i = i.left
self.bottom_root = i
# Update other
other.size = self.size
other.bottom_root = self.bottom_root
other.min_node = self.min_node
# Return the merged heap
return self
def insert(self, val):
"""
insert a value in the heap
"""
if self.size == 0:
self.bottom_root = Node(val)
self.size = 1
self.min_node = self.bottom_root
else:
# Create new node
new_node = Node(val)
# Update size
self.size += 1
# update min_node
if val < self.min_node.val:
self.min_node = new_node
# Put new_node as a bottom_root in heap
self.bottom_root.left = new_node
new_node.parent = self.bottom_root
self.bottom_root = new_node
# Consecutively merge roots with same left_tree_size
while (
self.bottom_root.parent
and self.bottom_root.left_tree_size
== self.bottom_root.parent.left_tree_size
):
# Next node
next_node = self.bottom_root.parent.parent
# Merge
self.bottom_root = self.bottom_root.mergeTrees(self.bottom_root.parent)
# Update Links
self.bottom_root.parent = next_node
self.bottom_root.left = None
if next_node:
next_node.left = self.bottom_root
def peek(self):
"""
return min element without deleting it
"""
return self.min_node.val
def isEmpty(self):
return self.size == 0
def deleteMin(self):
"""
delete min element and return it
"""
# assert not self.isEmpty(), "Empty Heap"
# Save minimal value
min_value = self.min_node.val
# Last element in heap corner case
if self.size == 1:
# Update size
self.size = 0
# Update bottom root
self.bottom_root = None
# Update min_node
self.min_node = None
return min_value
# No right subtree corner case
# The structure of the tree implies that this should be the bottom root
# and there is at least one other root
if self.min_node.right is None:
# Update size
self.size -= 1
# Update bottom root
self.bottom_root = self.bottom_root.parent
self.bottom_root.left = None
# Update min_node
self.min_node = self.bottom_root
i = self.bottom_root.parent
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
return min_value
# General case
# Find the BinomialHeap of the right subtree of min_node
bottom_of_new = self.min_node.right
bottom_of_new.parent = None
min_of_new = bottom_of_new
size_of_new = 1
# Size, min_node and bottom_root
while bottom_of_new.left:
size_of_new = size_of_new * 2 + 1
bottom_of_new = bottom_of_new.left
if bottom_of_new.val < min_of_new.val:
min_of_new = bottom_of_new
# Corner case of single root on top left path
if (not self.min_node.left) and (not self.min_node.parent):
self.size = size_of_new
self.bottom_root = bottom_of_new
self.min_node = min_of_new
# print("Single root, multiple nodes case")
return min_value
# Remaining cases
# Construct heap of right subtree
newHeap = BinomialHeap(
bottom_root=bottom_of_new, min_node=min_of_new, heap_size=size_of_new
)
# Update size
self.size = self.size - 1 - size_of_new
# Neighbour nodes
previous_node = self.min_node.left
next_node = self.min_node.parent
# Initialize new bottom_root and min_node
self.min_node = previous_node or next_node
self.bottom_root = next_node
# Update links of previous_node and search below for new min_node and
# bottom_root
if previous_node:
previous_node.parent = next_node
# Update bottom_root and search for min_node below
self.bottom_root = previous_node
self.min_node = previous_node
while self.bottom_root.left:
self.bottom_root = self.bottom_root.left
if self.bottom_root.val < self.min_node.val:
self.min_node = self.bottom_root
if next_node:
next_node.left = previous_node
# Search for new min_node above min_node
i = next_node
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
# Merge heaps
self.mergeHeaps(newHeap)
return min_value
def preOrder(self):
"""
Returns the Pre-order representation of the heap including
values of nodes plus their level distance from the root;
Empty nodes appear as #
"""
# Find top root
top_root = self.bottom_root
while top_root.parent:
top_root = top_root.parent
# preorder
heap_preOrder = []
self.__traversal(top_root, heap_preOrder)
return heap_preOrder
def __traversal(self, curr_node, preorder, level=0):
"""
Pre-order traversal of nodes
"""
if curr_node:
preorder.append((curr_node.val, level))
self.__traversal(curr_node.left, preorder, level + 1)
self.__traversal(curr_node.right, preorder, level + 1)
else:
preorder.append(("#", level))
def __str__(self):
"""
Overwriting str for a pre-order print of nodes in heap;
Performance is poor, so use only for small examples
"""
if self.isEmpty():
return ""
preorder_heap = self.preOrder()
return "\n".join(("-" * level + str(value)) for value, level in preorder_heap)
# Unit Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
This program calculates the nth Fibonacci number in O(log(n)).
It's possible to calculate F(1_000_000) in less than a second.
"""
from __future__ import annotations
import sys
def fibonacci(n: int) -> int:
"""
return F(n)
>>> [fibonacci(i) for i in range(13)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]
"""
if n < 0:
raise ValueError("Negative arguments are not supported")
return _fib(n)[0]
# returns (F(n), F(n-1))
def _fib(n: int) -> tuple[int, int]:
if n == 0: # (F(0), F(1))
return (0, 1)
# F(2n) = F(n)[2F(n+1) − F(n)]
# F(2n+1) = F(n+1)^2+F(n)^2
a, b = _fib(n // 2)
c = a * (b * 2 - a)
d = a * a + b * b
return (d, c + d) if n % 2 else (c, d)
if __name__ == "__main__":
n = int(sys.argv[1])
print(f"fibonacci({n}) is {fibonacci(n)}")
| #!/usr/bin/env python3
"""
This program calculates the nth Fibonacci number in O(log(n)).
It's possible to calculate F(1_000_000) in less than a second.
"""
from __future__ import annotations
import sys
def fibonacci(n: int) -> int:
"""
return F(n)
>>> [fibonacci(i) for i in range(13)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144]
"""
if n < 0:
raise ValueError("Negative arguments are not supported")
return _fib(n)[0]
# returns (F(n), F(n-1))
def _fib(n: int) -> tuple[int, int]:
if n == 0: # (F(0), F(1))
return (0, 1)
# F(2n) = F(n)[2F(n+1) − F(n)]
# F(2n+1) = F(n+1)^2+F(n)^2
a, b = _fib(n // 2)
c = a * (b * 2 - a)
d = a * a + b * b
return (d, c + d) if n % 2 else (c, d)
if __name__ == "__main__":
n = int(sys.argv[1])
print(f"fibonacci({n}) is {fibonacci(n)}")
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
A Python implementation of the quick select algorithm, which is efficient for
calculating the value that would appear in the index of a list if it would be
sorted, even if it is not already sorted
https://en.wikipedia.org/wiki/Quickselect
"""
import random
def _partition(data: list, pivot) -> tuple:
"""
Three way partition the data into smaller, equal and greater lists,
in relationship to the pivot
:param data: The data to be sorted (a list)
:param pivot: The value to partition the data on
:return: Three list: smaller, equal and greater
"""
less, equal, greater = [], [], []
for element in data:
if element < pivot:
less.append(element)
elif element > pivot:
greater.append(element)
else:
equal.append(element)
return less, equal, greater
def quick_select(items: list, index: int):
"""
>>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
54
>>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
4
>>> quick_select([5, 4, 3, 2], 2)
4
>>> quick_select([3, 5, 7, 10, 2, 12], 3)
7
"""
# index = len(items) // 2 when trying to find the median
# (value of index when items is sorted)
# invalid input
if index >= len(items) or index < 0:
return None
pivot = items[random.randint(0, len(items) - 1)]
count = 0
smaller, equal, larger = _partition(items, pivot)
count = len(equal)
m = len(smaller)
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(smaller, index)
# must be in larger
else:
return quick_select(larger, index - (m + count))
| """
A Python implementation of the quick select algorithm, which is efficient for
calculating the value that would appear in the index of a list if it would be
sorted, even if it is not already sorted
https://en.wikipedia.org/wiki/Quickselect
"""
import random
def _partition(data: list, pivot) -> tuple:
"""
Three way partition the data into smaller, equal and greater lists,
in relationship to the pivot
:param data: The data to be sorted (a list)
:param pivot: The value to partition the data on
:return: Three list: smaller, equal and greater
"""
less, equal, greater = [], [], []
for element in data:
if element < pivot:
less.append(element)
elif element > pivot:
greater.append(element)
else:
equal.append(element)
return less, equal, greater
def quick_select(items: list, index: int):
"""
>>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
54
>>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
4
>>> quick_select([5, 4, 3, 2], 2)
4
>>> quick_select([3, 5, 7, 10, 2, 12], 3)
7
"""
# index = len(items) // 2 when trying to find the median
# (value of index when items is sorted)
# invalid input
if index >= len(items) or index < 0:
return None
pivot = items[random.randint(0, len(items) - 1)]
count = 0
smaller, equal, larger = _partition(items, pivot)
count = len(equal)
m = len(smaller)
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(smaller, index)
# must be in larger
else:
return quick_select(larger, index - (m + count))
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_cubes = (n * (n + 1) // 2) ** 2
sum_squares = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_cubes = (n * (n + 1) // 2) ** 2
sum_squares = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Implementation of GradientBoostingRegressor in sklearn using the
boston dataset which is very popular for regression problem to
predict house price.
"""
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
def main():
# loading the dataset from the sklearn
df = load_boston()
print(df.keys())
# now let construct a data frame
df_boston = pd.DataFrame(df.data, columns=df.feature_names)
# let add the target to the dataframe
df_boston["Price"] = df.target
# print the first five rows using the head function
print(df_boston.head())
# Summary statistics
print(df_boston.describe().T)
# Feature selection
X = df_boston.iloc[:, :-1]
y = df_boston.iloc[:, -1] # target variable
# split the data with 75% train and 25% test sets.
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=0, test_size=0.25
)
model = GradientBoostingRegressor(
n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01
)
# training the model
model.fit(X_train, y_train)
# to see how good the model fit the data
training_score = model.score(X_train, y_train).round(3)
test_score = model.score(X_test, y_test).round(3)
print("Training score of GradientBoosting is :", training_score)
print("The test score of GradientBoosting is :", test_score)
# Let us evaluation the model by finding the errors
y_pred = model.predict(X_test)
# The mean squared error
print("Mean squared error: %.2f" % mean_squared_error(y_test, y_pred))
# Explained variance score: 1 is perfect prediction
print("Test Variance score: %.2f" % r2_score(y_test, y_pred))
# So let's run the model against the test data
fig, ax = plt.subplots()
ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=4)
ax.set_xlabel("Actual")
ax.set_ylabel("Predicted")
ax.set_title("Truth vs Predicted")
# this show function will display the plotting
plt.show()
if __name__ == "__main__":
main()
| """Implementation of GradientBoostingRegressor in sklearn using the
boston dataset which is very popular for regression problem to
predict house price.
"""
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
def main():
# loading the dataset from the sklearn
df = load_boston()
print(df.keys())
# now let construct a data frame
df_boston = pd.DataFrame(df.data, columns=df.feature_names)
# let add the target to the dataframe
df_boston["Price"] = df.target
# print the first five rows using the head function
print(df_boston.head())
# Summary statistics
print(df_boston.describe().T)
# Feature selection
X = df_boston.iloc[:, :-1]
y = df_boston.iloc[:, -1] # target variable
# split the data with 75% train and 25% test sets.
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=0, test_size=0.25
)
model = GradientBoostingRegressor(
n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01
)
# training the model
model.fit(X_train, y_train)
# to see how good the model fit the data
training_score = model.score(X_train, y_train).round(3)
test_score = model.score(X_test, y_test).round(3)
print("Training score of GradientBoosting is :", training_score)
print("The test score of GradientBoosting is :", test_score)
# Let us evaluation the model by finding the errors
y_pred = model.predict(X_test)
# The mean squared error
print("Mean squared error: %.2f" % mean_squared_error(y_test, y_pred))
# Explained variance score: 1 is perfect prediction
print("Test Variance score: %.2f" % r2_score(y_test, y_pred))
# So let's run the model against the test data
fig, ax = plt.subplots()
ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=4)
ax.set_xlabel("Actual")
ax.set_ylabel("Predicted")
ax.set_title("Truth vs Predicted")
# this show function will display the plotting
plt.show()
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import string
from math import log10
"""
tf-idf Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf
tf-idf and other word frequency algorithms are often used
as a weighting factor in information retrieval and text
mining. 83% of text-based recommender systems use
tf-idf for term weighting. In Layman's terms, tf-idf
is a statistic intended to reflect how important a word
is to a document in a corpus (a collection of documents)
Here I've implemented several word frequency algorithms
that are commonly used in information retrieval: Term Frequency,
Document Frequency, and TF-IDF (Term-Frequency*Inverse-Document-Frequency)
are included.
Term Frequency is a statistical function that
returns a number representing how frequently
an expression occurs in a document. This
indicates how significant a particular term is in
a given document.
Document Frequency is a statistical function that returns
an integer representing the number of documents in a
corpus that a term occurs in (where the max number returned
would be the number of documents in the corpus).
Inverse Document Frequency is mathematically written as
log10(N/df), where N is the number of documents in your
corpus and df is the Document Frequency. If df is 0, a
ZeroDivisionError will be thrown.
Term-Frequency*Inverse-Document-Frequency is a measure
of the originality of a term. It is mathematically written
as tf*log10(N/df). It compares the number of times
a term appears in a document with the number of documents
the term appears in. If df is 0, a ZeroDivisionError will be thrown.
"""
def term_frequency(term: str, document: str) -> int:
"""
Return the number of times a term occurs within
a given document.
@params: term, the term to search a document for, and document,
the document to search within
@returns: an integer representing the number of times a term is
found within the document
@examples:
>>> term_frequency("to", "To be, or not to be")
2
"""
# strip all punctuation and newlines and replace it with ''
document_without_punctuation = document.translate(
str.maketrans("", "", string.punctuation)
).replace("\n", "")
tokenize_document = document_without_punctuation.split(" ") # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()])
def document_frequency(term: str, corpus: str) -> tuple[int, int]:
"""
Calculate the number of documents in a corpus that contain a
given term
@params : term, the term to search each document for, and corpus, a collection of
documents. Each document should be separated by a newline.
@returns : the number of documents in the corpus that contain the term you are
searching for and the number of documents in the corpus
@examples :
>>> document_frequency("first", "This is the first document in the corpus.\\nThIs\
is the second document in the corpus.\\nTHIS is \
the third document in the corpus.")
(1, 3)
"""
corpus_without_punctuation = corpus.lower().translate(
str.maketrans("", "", string.punctuation)
) # strip all punctuation and replace it with ''
docs = corpus_without_punctuation.split("\n")
term = term.lower()
return (len([doc for doc in docs if term in doc]), len(docs))
def inverse_document_frequency(df: int, N: int, smoothing=False) -> float:
"""
Return an integer denoting the importance
of a word. This measure of importance is
calculated by log10(N/df), where N is the
number of documents and df is
the Document Frequency.
@params : df, the Document Frequency, N,
the number of documents in the corpus and
smoothing, if True return the idf-smooth
@returns : log10(N/df) or 1+log10(N/1+df)
@examples :
>>> inverse_document_frequency(3, 0)
Traceback (most recent call last):
...
ValueError: log10(0) is undefined.
>>> inverse_document_frequency(1, 3)
0.477
>>> inverse_document_frequency(0, 3)
Traceback (most recent call last):
...
ZeroDivisionError: df must be > 0
>>> inverse_document_frequency(0, 3,True)
1.477
"""
if smoothing:
if N == 0:
raise ValueError("log10(0) is undefined.")
return round(1 + log10(N / (1 + df)), 3)
if df == 0:
raise ZeroDivisionError("df must be > 0")
elif N == 0:
raise ValueError("log10(0) is undefined.")
return round(log10(N / df), 3)
def tf_idf(tf: int, idf: int) -> float:
"""
Combine the term frequency
and inverse document frequency functions to
calculate the originality of a term. This
'originality' is calculated by multiplying
the term frequency and the inverse document
frequency : tf-idf = TF * IDF
@params : tf, the term frequency, and idf, the inverse document
frequency
@examples :
>>> tf_idf(2, 0.477)
0.954
"""
return round(tf * idf, 3)
| import string
from math import log10
"""
tf-idf Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf
tf-idf and other word frequency algorithms are often used
as a weighting factor in information retrieval and text
mining. 83% of text-based recommender systems use
tf-idf for term weighting. In Layman's terms, tf-idf
is a statistic intended to reflect how important a word
is to a document in a corpus (a collection of documents)
Here I've implemented several word frequency algorithms
that are commonly used in information retrieval: Term Frequency,
Document Frequency, and TF-IDF (Term-Frequency*Inverse-Document-Frequency)
are included.
Term Frequency is a statistical function that
returns a number representing how frequently
an expression occurs in a document. This
indicates how significant a particular term is in
a given document.
Document Frequency is a statistical function that returns
an integer representing the number of documents in a
corpus that a term occurs in (where the max number returned
would be the number of documents in the corpus).
Inverse Document Frequency is mathematically written as
log10(N/df), where N is the number of documents in your
corpus and df is the Document Frequency. If df is 0, a
ZeroDivisionError will be thrown.
Term-Frequency*Inverse-Document-Frequency is a measure
of the originality of a term. It is mathematically written
as tf*log10(N/df). It compares the number of times
a term appears in a document with the number of documents
the term appears in. If df is 0, a ZeroDivisionError will be thrown.
"""
def term_frequency(term: str, document: str) -> int:
"""
Return the number of times a term occurs within
a given document.
@params: term, the term to search a document for, and document,
the document to search within
@returns: an integer representing the number of times a term is
found within the document
@examples:
>>> term_frequency("to", "To be, or not to be")
2
"""
# strip all punctuation and newlines and replace it with ''
document_without_punctuation = document.translate(
str.maketrans("", "", string.punctuation)
).replace("\n", "")
tokenize_document = document_without_punctuation.split(" ") # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()])
def document_frequency(term: str, corpus: str) -> tuple[int, int]:
"""
Calculate the number of documents in a corpus that contain a
given term
@params : term, the term to search each document for, and corpus, a collection of
documents. Each document should be separated by a newline.
@returns : the number of documents in the corpus that contain the term you are
searching for and the number of documents in the corpus
@examples :
>>> document_frequency("first", "This is the first document in the corpus.\\nThIs\
is the second document in the corpus.\\nTHIS is \
the third document in the corpus.")
(1, 3)
"""
corpus_without_punctuation = corpus.lower().translate(
str.maketrans("", "", string.punctuation)
) # strip all punctuation and replace it with ''
docs = corpus_without_punctuation.split("\n")
term = term.lower()
return (len([doc for doc in docs if term in doc]), len(docs))
def inverse_document_frequency(df: int, N: int, smoothing=False) -> float:
"""
Return an integer denoting the importance
of a word. This measure of importance is
calculated by log10(N/df), where N is the
number of documents and df is
the Document Frequency.
@params : df, the Document Frequency, N,
the number of documents in the corpus and
smoothing, if True return the idf-smooth
@returns : log10(N/df) or 1+log10(N/1+df)
@examples :
>>> inverse_document_frequency(3, 0)
Traceback (most recent call last):
...
ValueError: log10(0) is undefined.
>>> inverse_document_frequency(1, 3)
0.477
>>> inverse_document_frequency(0, 3)
Traceback (most recent call last):
...
ZeroDivisionError: df must be > 0
>>> inverse_document_frequency(0, 3,True)
1.477
"""
if smoothing:
if N == 0:
raise ValueError("log10(0) is undefined.")
return round(1 + log10(N / (1 + df)), 3)
if df == 0:
raise ZeroDivisionError("df must be > 0")
elif N == 0:
raise ValueError("log10(0) is undefined.")
return round(log10(N / df), 3)
def tf_idf(tf: int, idf: int) -> float:
"""
Combine the term frequency
and inverse document frequency functions to
calculate the originality of a term. This
'originality' is calculated by multiplying
the term frequency and the inverse document
frequency : tf-idf = TF * IDF
@params : tf, the term frequency, and idf, the inverse document
frequency
@examples :
>>> tf_idf(2, 0.477)
0.954
"""
return round(tf * idf, 3)
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def gcd(a: int, b: int) -> int:
while a != 0:
a, b = b % a, a
return b
def find_mod_inverse(a: int, m: int) -> int:
if gcd(a, m) != 1:
raise ValueError(f"mod inverse of {a!r} and {m!r} does not exist")
u1, u2, u3 = 1, 0, a
v1, v2, v3 = 0, 1, m
while v3 != 0:
q = u3 // v3
v1, v2, v3, u1, u2, u3 = (u1 - q * v1), (u2 - q * v2), (u3 - q * v3), v1, v2, v3
return u1 % m
| def gcd(a: int, b: int) -> int:
while a != 0:
a, b = b % a, a
return b
def find_mod_inverse(a: int, m: int) -> int:
if gcd(a, m) != 1:
raise ValueError(f"mod inverse of {a!r} and {m!r} does not exist")
u1, u2, u3 = 1, 0, a
v1, v2, v3 = 0, 1, m
while v3 != 0:
q = u3 // v3
v1, v2, v3, u1, u2, u3 = (u1 - q * v1), (u2 - q * v2), (u3 - q * v3), v1, v2, v3
return u1 % m
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Created by susmith98
from collections import Counter
from timeit import timeit
# Problem Description:
# Check if characters of the given string can be rearranged to form a palindrome.
# Counter is faster for long strings and non-Counter is faster for short strings.
def can_string_be_rearranged_as_palindrome_counter(
input_str: str = "",
) -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome_counter("Momo")
True
>>> can_string_be_rearranged_as_palindrome_counter("Mother")
False
>>> can_string_be_rearranged_as_palindrome_counter("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
return sum(c % 2 for c in Counter(input_str.replace(" ", "").lower()).values()) < 2
def can_string_be_rearranged_as_palindrome(input_str: str = "") -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome("Momo")
True
>>> can_string_be_rearranged_as_palindrome("Mother")
False
>>> can_string_be_rearranged_as_palindrome("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
if len(input_str) == 0:
return True
lower_case_input_str = input_str.replace(" ", "").lower()
# character_freq_dict: Stores the frequency of every character in the input string
character_freq_dict = {}
for character in lower_case_input_str:
character_freq_dict[character] = character_freq_dict.get(character, 0) + 1
"""
Above line of code is equivalent to:
1) Getting the frequency of current character till previous index
>>> character_freq = character_freq_dict.get(character, 0)
2) Incrementing the frequency of current character by 1
>>> character_freq = character_freq + 1
3) Updating the frequency of current character
>>> character_freq_dict[character] = character_freq
"""
"""
OBSERVATIONS:
Even length palindrome
-> Every character appears even no.of times.
Odd length palindrome
-> Every character appears even no.of times except for one character.
LOGIC:
Step 1: We'll count number of characters that appear odd number of times i.e oddChar
Step 2:If we find more than 1 character that appears odd number of times,
It is not possible to rearrange as a palindrome
"""
oddChar = 0
for character_count in character_freq_dict.values():
if character_count % 2:
oddChar += 1
if oddChar > 1:
return False
return True
def benchmark(input_str: str = "") -> None:
"""
Benchmark code for comparing above 2 functions
"""
print("\nFor string = ", input_str, ":")
print(
"> can_string_be_rearranged_as_palindrome_counter()",
"\tans =",
can_string_be_rearranged_as_palindrome_counter(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
print(
"> can_string_be_rearranged_as_palindrome()",
"\tans =",
can_string_be_rearranged_as_palindrome(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
if __name__ == "__main__":
check_str = input(
"Enter string to determine if it can be rearranged as a palindrome or not: "
).strip()
benchmark(check_str)
status = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")
| # Created by susmith98
from collections import Counter
from timeit import timeit
# Problem Description:
# Check if characters of the given string can be rearranged to form a palindrome.
# Counter is faster for long strings and non-Counter is faster for short strings.
def can_string_be_rearranged_as_palindrome_counter(
input_str: str = "",
) -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome_counter("Momo")
True
>>> can_string_be_rearranged_as_palindrome_counter("Mother")
False
>>> can_string_be_rearranged_as_palindrome_counter("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
return sum(c % 2 for c in Counter(input_str.replace(" ", "").lower()).values()) < 2
def can_string_be_rearranged_as_palindrome(input_str: str = "") -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
>>> can_string_be_rearranged_as_palindrome("Momo")
True
>>> can_string_be_rearranged_as_palindrome("Mother")
False
>>> can_string_be_rearranged_as_palindrome("Father")
False
>>> can_string_be_rearranged_as_palindrome_counter("A man a plan a canal Panama")
True
"""
if len(input_str) == 0:
return True
lower_case_input_str = input_str.replace(" ", "").lower()
# character_freq_dict: Stores the frequency of every character in the input string
character_freq_dict = {}
for character in lower_case_input_str:
character_freq_dict[character] = character_freq_dict.get(character, 0) + 1
"""
Above line of code is equivalent to:
1) Getting the frequency of current character till previous index
>>> character_freq = character_freq_dict.get(character, 0)
2) Incrementing the frequency of current character by 1
>>> character_freq = character_freq + 1
3) Updating the frequency of current character
>>> character_freq_dict[character] = character_freq
"""
"""
OBSERVATIONS:
Even length palindrome
-> Every character appears even no.of times.
Odd length palindrome
-> Every character appears even no.of times except for one character.
LOGIC:
Step 1: We'll count number of characters that appear odd number of times i.e oddChar
Step 2:If we find more than 1 character that appears odd number of times,
It is not possible to rearrange as a palindrome
"""
oddChar = 0
for character_count in character_freq_dict.values():
if character_count % 2:
oddChar += 1
if oddChar > 1:
return False
return True
def benchmark(input_str: str = "") -> None:
"""
Benchmark code for comparing above 2 functions
"""
print("\nFor string = ", input_str, ":")
print(
"> can_string_be_rearranged_as_palindrome_counter()",
"\tans =",
can_string_be_rearranged_as_palindrome_counter(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
print(
"> can_string_be_rearranged_as_palindrome()",
"\tans =",
can_string_be_rearranged_as_palindrome(input_str),
"\ttime =",
timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)",
setup="import __main__ as z",
),
"seconds",
)
if __name__ == "__main__":
check_str = input(
"Enter string to determine if it can be rearranged as a palindrome or not: "
).strip()
benchmark(check_str)
status = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")
| -1 |
TheAlgorithms/Python | 4,314 | fix(mypy): type annotations for conversions algorithms | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| dhruvmanila | "2021-04-04T13:03:20Z" | "2021-04-04T13:25:49Z" | 536fb4bca48f69cb66cfbd03aeb02550def07977 | 20c7518028efbb6e8ae46a42b28c3f2e27acb2a2 | fix(mypy): type annotations for conversions algorithms. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
Subsets and Splits