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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}`.
| """README, Author - Anurag Kumar(mailto:anuragkumarak95@gmail.com)
Requirements:
- sklearn
- numpy
- matplotlib
Python:
- 3.5
Inputs:
- X , a 2D numpy array of features.
- k , number of clusters to create.
- initial_centroids , initial centroid values generated by utility function(mentioned
in usage).
- maxiter , maximum number of iterations to process.
- heterogeneity , empty list that will be filled with hetrogeneity values if passed
to kmeans func.
Usage:
1. define 'k' value, 'X' features array and 'hetrogeneity' empty list
2. create initial_centroids,
initial_centroids = get_initial_centroids(
X,
k,
seed=0 # seed value for initial centroid generation,
# None for randomness(default=None)
)
3. find centroids and clusters using kmeans function.
centroids, cluster_assignment = kmeans(
X,
k,
initial_centroids,
maxiter=400,
record_heterogeneity=heterogeneity,
verbose=True # whether to print logs in console or not.(default=False)
)
4. Plot the loss function, hetrogeneity values for every iteration saved in
hetrogeneity list.
plot_heterogeneity(
heterogeneity,
k
)
5. Transfers Dataframe into excel format it must have feature called
'Clust' with k means clustering numbers in it.
"""
import warnings
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances
warnings.filterwarnings("ignore")
TAG = "K-MEANS-CLUST/ "
def get_initial_centroids(data, k, seed=None):
"""Randomly choose k data points as initial centroids"""
if seed is not None: # useful for obtaining consistent results
np.random.seed(seed)
n = data.shape[0] # number of data points
# Pick K indices from range [0, N).
rand_indices = np.random.randint(0, n, k)
# Keep centroids as dense format, as many entries will be nonzero due to averaging.
# As long as at least one document in a cluster contains a word,
# it will carry a nonzero weight in the TF-IDF vector of the centroid.
centroids = data[rand_indices, :]
return centroids
def centroid_pairwise_dist(X, centroids):
return pairwise_distances(X, centroids, metric="euclidean")
def assign_clusters(data, centroids):
# Compute distances between each data point and the set of centroids:
# Fill in the blank (RHS only)
distances_from_centroids = centroid_pairwise_dist(data, centroids)
# Compute cluster assignments for each data point:
# Fill in the blank (RHS only)
cluster_assignment = np.argmin(distances_from_centroids, axis=1)
return cluster_assignment
def revise_centroids(data, k, cluster_assignment):
new_centroids = []
for i in range(k):
# Select all data points that belong to cluster i. Fill in the blank (RHS only)
member_data_points = data[cluster_assignment == i]
# Compute the mean of the data points. Fill in the blank (RHS only)
centroid = member_data_points.mean(axis=0)
new_centroids.append(centroid)
new_centroids = np.array(new_centroids)
return new_centroids
def compute_heterogeneity(data, k, centroids, cluster_assignment):
heterogeneity = 0.0
for i in range(k):
# Select all data points that belong to cluster i. Fill in the blank (RHS only)
member_data_points = data[cluster_assignment == i, :]
if member_data_points.shape[0] > 0: # check if i-th cluster is non-empty
# Compute distances from centroid to data points (RHS only)
distances = pairwise_distances(
member_data_points, [centroids[i]], metric="euclidean"
)
squared_distances = distances ** 2
heterogeneity += np.sum(squared_distances)
return heterogeneity
def plot_heterogeneity(heterogeneity, k):
plt.figure(figsize=(7, 4))
plt.plot(heterogeneity, linewidth=4)
plt.xlabel("# Iterations")
plt.ylabel("Heterogeneity")
plt.title(f"Heterogeneity of clustering over time, K={k:d}")
plt.rcParams.update({"font.size": 16})
plt.show()
def kmeans(
data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
):
"""This function runs k-means on given data and initial set of centroids.
maxiter: maximum number of iterations to run.(default=500)
record_heterogeneity: (optional) a list, to store the history of heterogeneity
as function of iterations
if None, do not store the history.
verbose: if True, print how many data points changed their cluster labels in
each iteration"""
centroids = initial_centroids[:]
prev_cluster_assignment = None
for itr in range(maxiter):
if verbose:
print(itr, end="")
# 1. Make cluster assignments using nearest centroids
cluster_assignment = assign_clusters(data, centroids)
# 2. Compute a new centroid for each of the k clusters, averaging all data
# points assigned to that cluster.
centroids = revise_centroids(data, k, cluster_assignment)
# Check for convergence: if none of the assignments changed, stop
if (
prev_cluster_assignment is not None
and (prev_cluster_assignment == cluster_assignment).all()
):
break
# Print number of new assignments
if prev_cluster_assignment is not None:
num_changed = np.sum(prev_cluster_assignment != cluster_assignment)
if verbose:
print(
" {:5d} elements changed their cluster assignment.".format(
num_changed
)
)
# Record heterogeneity convergence metric
if record_heterogeneity is not None:
# YOUR CODE HERE
score = compute_heterogeneity(data, k, centroids, cluster_assignment)
record_heterogeneity.append(score)
prev_cluster_assignment = cluster_assignment[:]
return centroids, cluster_assignment
# Mock test below
if False: # change to true to run this test case.
from sklearn import datasets as ds
dataset = ds.load_iris()
k = 3
heterogeneity = []
initial_centroids = get_initial_centroids(dataset["data"], k, seed=0)
centroids, cluster_assignment = kmeans(
dataset["data"],
k,
initial_centroids,
maxiter=400,
record_heterogeneity=heterogeneity,
verbose=True,
)
plot_heterogeneity(heterogeneity, k)
def ReportGenerator(
df: pd.DataFrame, ClusteringVariables: np.ndarray, FillMissingReport=None
) -> pd.DataFrame:
"""
Function generates easy-erading clustering report. It takes 2 arguments as an input:
DataFrame - dataframe with predicted cluester column;
FillMissingReport - dictionary of rules how we are going to fill missing
values of for final report generate (not included in modeling);
in order to run the function following libraries must be imported:
import pandas as pd
import numpy as np
>>> data = pd.DataFrame()
>>> data['numbers'] = [1, 2, 3]
>>> data['col1'] = [0.5, 2.5, 4.5]
>>> data['col2'] = [100, 200, 300]
>>> data['col3'] = [10, 20, 30]
>>> data['Cluster'] = [1, 1, 2]
>>> ReportGenerator(data, ['col1', 'col2'], 0)
Features Type Mark 1 2
0 # of Customers ClusterSize False 2.000000 1.000000
1 % of Customers ClusterProportion False 0.666667 0.333333
2 col1 mean_with_zeros True 1.500000 4.500000
3 col2 mean_with_zeros True 150.000000 300.000000
4 numbers mean_with_zeros False 1.500000 3.000000
.. ... ... ... ... ...
99 dummy 5% False 1.000000 1.000000
100 dummy 95% False 1.000000 1.000000
101 dummy stdev False 0.000000 NaN
102 dummy mode False 1.000000 1.000000
103 dummy median False 1.000000 1.000000
<BLANKLINE>
[104 rows x 5 columns]
"""
# Fill missing values with given rules
if FillMissingReport:
df.fillna(value=FillMissingReport, inplace=True)
df["dummy"] = 1
numeric_cols = df.select_dtypes(np.number).columns
report = (
df.groupby(["Cluster"])[ # construct report dataframe
numeric_cols
] # group by cluster number
.agg(
[
("sum", np.sum),
("mean_with_zeros", lambda x: np.mean(np.nan_to_num(x))),
("mean_without_zeros", lambda x: x.replace(0, np.NaN).mean()),
(
"mean_25-75",
lambda x: np.mean(
np.nan_to_num(
sorted(x)[
round(len(x) * 25 / 100) : round(len(x) * 75 / 100)
]
)
),
),
("mean_with_na", np.mean),
("min", lambda x: x.min()),
("5%", lambda x: x.quantile(0.05)),
("25%", lambda x: x.quantile(0.25)),
("50%", lambda x: x.quantile(0.50)),
("75%", lambda x: x.quantile(0.75)),
("95%", lambda x: x.quantile(0.95)),
("max", lambda x: x.max()),
("count", lambda x: x.count()),
("stdev", lambda x: x.std()),
("mode", lambda x: x.mode()[0]),
("median", lambda x: x.median()),
("# > 0", lambda x: (x > 0).sum()),
]
)
.T.reset_index()
.rename(index=str, columns={"level_0": "Features", "level_1": "Type"})
) # rename columns
# calculate the size of cluster(count of clientID's)
clustersize = report[
(report["Features"] == "dummy") & (report["Type"] == "count")
].copy() # avoid SettingWithCopyWarning
clustersize.Type = (
"ClusterSize" # rename created cluster df to match report column names
)
clustersize.Features = "# of Customers"
clusterproportion = pd.DataFrame(
clustersize.iloc[:, 2:].values
/ clustersize.iloc[:, 2:].values.sum() # calculating the proportion of cluster
)
clusterproportion[
"Type"
] = "% of Customers" # rename created cluster df to match report column names
clusterproportion["Features"] = "ClusterProportion"
cols = clusterproportion.columns.tolist()
cols = cols[-2:] + cols[:-2]
clusterproportion = clusterproportion[cols] # rearrange columns to match report
clusterproportion.columns = report.columns
a = pd.DataFrame(
abs(
report[report["Type"] == "count"].iloc[:, 2:].values
- clustersize.iloc[:, 2:].values
)
) # generating df with count of nan values
a["Features"] = 0
a["Type"] = "# of nan"
a.Features = report[
report["Type"] == "count"
].Features.tolist() # filling values in order to match report
cols = a.columns.tolist()
cols = cols[-2:] + cols[:-2]
a = a[cols] # rearrange columns to match report
a.columns = report.columns # rename columns to match report
report = report.drop(
report[report.Type == "count"].index
) # drop count values except cluster size
report = pd.concat(
[report, a, clustersize, clusterproportion], axis=0
) # concat report with clustert size and nan values
report["Mark"] = report["Features"].isin(ClusteringVariables)
cols = report.columns.tolist()
cols = cols[0:2] + cols[-1:] + cols[2:-1]
report = report[cols]
sorter1 = {
"ClusterSize": 9,
"ClusterProportion": 8,
"mean_with_zeros": 7,
"mean_with_na": 6,
"max": 5,
"50%": 4,
"min": 3,
"25%": 2,
"75%": 1,
"# of nan": 0,
"# > 0": -1,
"sum_with_na": -2,
}
report = (
report.assign(
Sorter1=lambda x: x.Type.map(sorter1),
Sorter2=lambda x: list(reversed(range(len(x)))),
)
.sort_values(["Sorter1", "Mark", "Sorter2"], ascending=False)
.drop(["Sorter1", "Sorter2"], axis=1)
)
report.columns.name = ""
report = report.reset_index()
report.drop(columns=["index"], inplace=True)
return report
if __name__ == "__main__":
import doctest
doctest.testmod()
| """README, Author - Anurag Kumar(mailto:anuragkumarak95@gmail.com)
Requirements:
- sklearn
- numpy
- matplotlib
Python:
- 3.5
Inputs:
- X , a 2D numpy array of features.
- k , number of clusters to create.
- initial_centroids , initial centroid values generated by utility function(mentioned
in usage).
- maxiter , maximum number of iterations to process.
- heterogeneity , empty list that will be filled with hetrogeneity values if passed
to kmeans func.
Usage:
1. define 'k' value, 'X' features array and 'hetrogeneity' empty list
2. create initial_centroids,
initial_centroids = get_initial_centroids(
X,
k,
seed=0 # seed value for initial centroid generation,
# None for randomness(default=None)
)
3. find centroids and clusters using kmeans function.
centroids, cluster_assignment = kmeans(
X,
k,
initial_centroids,
maxiter=400,
record_heterogeneity=heterogeneity,
verbose=True # whether to print logs in console or not.(default=False)
)
4. Plot the loss function, hetrogeneity values for every iteration saved in
hetrogeneity list.
plot_heterogeneity(
heterogeneity,
k
)
5. Transfers Dataframe into excel format it must have feature called
'Clust' with k means clustering numbers in it.
"""
import warnings
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances
warnings.filterwarnings("ignore")
TAG = "K-MEANS-CLUST/ "
def get_initial_centroids(data, k, seed=None):
"""Randomly choose k data points as initial centroids"""
if seed is not None: # useful for obtaining consistent results
np.random.seed(seed)
n = data.shape[0] # number of data points
# Pick K indices from range [0, N).
rand_indices = np.random.randint(0, n, k)
# Keep centroids as dense format, as many entries will be nonzero due to averaging.
# As long as at least one document in a cluster contains a word,
# it will carry a nonzero weight in the TF-IDF vector of the centroid.
centroids = data[rand_indices, :]
return centroids
def centroid_pairwise_dist(X, centroids):
return pairwise_distances(X, centroids, metric="euclidean")
def assign_clusters(data, centroids):
# Compute distances between each data point and the set of centroids:
# Fill in the blank (RHS only)
distances_from_centroids = centroid_pairwise_dist(data, centroids)
# Compute cluster assignments for each data point:
# Fill in the blank (RHS only)
cluster_assignment = np.argmin(distances_from_centroids, axis=1)
return cluster_assignment
def revise_centroids(data, k, cluster_assignment):
new_centroids = []
for i in range(k):
# Select all data points that belong to cluster i. Fill in the blank (RHS only)
member_data_points = data[cluster_assignment == i]
# Compute the mean of the data points. Fill in the blank (RHS only)
centroid = member_data_points.mean(axis=0)
new_centroids.append(centroid)
new_centroids = np.array(new_centroids)
return new_centroids
def compute_heterogeneity(data, k, centroids, cluster_assignment):
heterogeneity = 0.0
for i in range(k):
# Select all data points that belong to cluster i. Fill in the blank (RHS only)
member_data_points = data[cluster_assignment == i, :]
if member_data_points.shape[0] > 0: # check if i-th cluster is non-empty
# Compute distances from centroid to data points (RHS only)
distances = pairwise_distances(
member_data_points, [centroids[i]], metric="euclidean"
)
squared_distances = distances ** 2
heterogeneity += np.sum(squared_distances)
return heterogeneity
def plot_heterogeneity(heterogeneity, k):
plt.figure(figsize=(7, 4))
plt.plot(heterogeneity, linewidth=4)
plt.xlabel("# Iterations")
plt.ylabel("Heterogeneity")
plt.title(f"Heterogeneity of clustering over time, K={k:d}")
plt.rcParams.update({"font.size": 16})
plt.show()
def kmeans(
data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
):
"""This function runs k-means on given data and initial set of centroids.
maxiter: maximum number of iterations to run.(default=500)
record_heterogeneity: (optional) a list, to store the history of heterogeneity
as function of iterations
if None, do not store the history.
verbose: if True, print how many data points changed their cluster labels in
each iteration"""
centroids = initial_centroids[:]
prev_cluster_assignment = None
for itr in range(maxiter):
if verbose:
print(itr, end="")
# 1. Make cluster assignments using nearest centroids
cluster_assignment = assign_clusters(data, centroids)
# 2. Compute a new centroid for each of the k clusters, averaging all data
# points assigned to that cluster.
centroids = revise_centroids(data, k, cluster_assignment)
# Check for convergence: if none of the assignments changed, stop
if (
prev_cluster_assignment is not None
and (prev_cluster_assignment == cluster_assignment).all()
):
break
# Print number of new assignments
if prev_cluster_assignment is not None:
num_changed = np.sum(prev_cluster_assignment != cluster_assignment)
if verbose:
print(
" {:5d} elements changed their cluster assignment.".format(
num_changed
)
)
# Record heterogeneity convergence metric
if record_heterogeneity is not None:
# YOUR CODE HERE
score = compute_heterogeneity(data, k, centroids, cluster_assignment)
record_heterogeneity.append(score)
prev_cluster_assignment = cluster_assignment[:]
return centroids, cluster_assignment
# Mock test below
if False: # change to true to run this test case.
from sklearn import datasets as ds
dataset = ds.load_iris()
k = 3
heterogeneity = []
initial_centroids = get_initial_centroids(dataset["data"], k, seed=0)
centroids, cluster_assignment = kmeans(
dataset["data"],
k,
initial_centroids,
maxiter=400,
record_heterogeneity=heterogeneity,
verbose=True,
)
plot_heterogeneity(heterogeneity, k)
def ReportGenerator(
df: pd.DataFrame, ClusteringVariables: np.ndarray, FillMissingReport=None
) -> pd.DataFrame:
"""
Function generates easy-erading clustering report. It takes 2 arguments as an input:
DataFrame - dataframe with predicted cluester column;
FillMissingReport - dictionary of rules how we are going to fill missing
values of for final report generate (not included in modeling);
in order to run the function following libraries must be imported:
import pandas as pd
import numpy as np
>>> data = pd.DataFrame()
>>> data['numbers'] = [1, 2, 3]
>>> data['col1'] = [0.5, 2.5, 4.5]
>>> data['col2'] = [100, 200, 300]
>>> data['col3'] = [10, 20, 30]
>>> data['Cluster'] = [1, 1, 2]
>>> ReportGenerator(data, ['col1', 'col2'], 0)
Features Type Mark 1 2
0 # of Customers ClusterSize False 2.000000 1.000000
1 % of Customers ClusterProportion False 0.666667 0.333333
2 col1 mean_with_zeros True 1.500000 4.500000
3 col2 mean_with_zeros True 150.000000 300.000000
4 numbers mean_with_zeros False 1.500000 3.000000
.. ... ... ... ... ...
99 dummy 5% False 1.000000 1.000000
100 dummy 95% False 1.000000 1.000000
101 dummy stdev False 0.000000 NaN
102 dummy mode False 1.000000 1.000000
103 dummy median False 1.000000 1.000000
<BLANKLINE>
[104 rows x 5 columns]
"""
# Fill missing values with given rules
if FillMissingReport:
df.fillna(value=FillMissingReport, inplace=True)
df["dummy"] = 1
numeric_cols = df.select_dtypes(np.number).columns
report = (
df.groupby(["Cluster"])[ # construct report dataframe
numeric_cols
] # group by cluster number
.agg(
[
("sum", np.sum),
("mean_with_zeros", lambda x: np.mean(np.nan_to_num(x))),
("mean_without_zeros", lambda x: x.replace(0, np.NaN).mean()),
(
"mean_25-75",
lambda x: np.mean(
np.nan_to_num(
sorted(x)[
round(len(x) * 25 / 100) : round(len(x) * 75 / 100)
]
)
),
),
("mean_with_na", np.mean),
("min", lambda x: x.min()),
("5%", lambda x: x.quantile(0.05)),
("25%", lambda x: x.quantile(0.25)),
("50%", lambda x: x.quantile(0.50)),
("75%", lambda x: x.quantile(0.75)),
("95%", lambda x: x.quantile(0.95)),
("max", lambda x: x.max()),
("count", lambda x: x.count()),
("stdev", lambda x: x.std()),
("mode", lambda x: x.mode()[0]),
("median", lambda x: x.median()),
("# > 0", lambda x: (x > 0).sum()),
]
)
.T.reset_index()
.rename(index=str, columns={"level_0": "Features", "level_1": "Type"})
) # rename columns
# calculate the size of cluster(count of clientID's)
clustersize = report[
(report["Features"] == "dummy") & (report["Type"] == "count")
].copy() # avoid SettingWithCopyWarning
clustersize.Type = (
"ClusterSize" # rename created cluster df to match report column names
)
clustersize.Features = "# of Customers"
clusterproportion = pd.DataFrame(
clustersize.iloc[:, 2:].values
/ clustersize.iloc[:, 2:].values.sum() # calculating the proportion of cluster
)
clusterproportion[
"Type"
] = "% of Customers" # rename created cluster df to match report column names
clusterproportion["Features"] = "ClusterProportion"
cols = clusterproportion.columns.tolist()
cols = cols[-2:] + cols[:-2]
clusterproportion = clusterproportion[cols] # rearrange columns to match report
clusterproportion.columns = report.columns
a = pd.DataFrame(
abs(
report[report["Type"] == "count"].iloc[:, 2:].values
- clustersize.iloc[:, 2:].values
)
) # generating df with count of nan values
a["Features"] = 0
a["Type"] = "# of nan"
a.Features = report[
report["Type"] == "count"
].Features.tolist() # filling values in order to match report
cols = a.columns.tolist()
cols = cols[-2:] + cols[:-2]
a = a[cols] # rearrange columns to match report
a.columns = report.columns # rename columns to match report
report = report.drop(
report[report.Type == "count"].index
) # drop count values except cluster size
report = pd.concat(
[report, a, clustersize, clusterproportion], axis=0
) # concat report with clustert size and nan values
report["Mark"] = report["Features"].isin(ClusteringVariables)
cols = report.columns.tolist()
cols = cols[0:2] + cols[-1:] + cols[2:-1]
report = report[cols]
sorter1 = {
"ClusterSize": 9,
"ClusterProportion": 8,
"mean_with_zeros": 7,
"mean_with_na": 6,
"max": 5,
"50%": 4,
"min": 3,
"25%": 2,
"75%": 1,
"# of nan": 0,
"# > 0": -1,
"sum_with_na": -2,
}
report = (
report.assign(
Sorter1=lambda x: x.Type.map(sorter1),
Sorter2=lambda x: list(reversed(range(len(x)))),
)
.sort_values(["Sorter1", "Mark", "Sorter2"], ascending=False)
.drop(["Sorter1", "Sorter2"], axis=1)
)
report.columns.name = ""
report = report.reset_index()
report.drop(columns=["index"], inplace=True)
return report
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}`.
| """
Problem 16: https://projecteuler.net/problem=16
2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26.
What is the sum of the digits of the number 2^1000?
"""
def solution(power: int = 1000) -> int:
"""Returns the sum of the digits of the number 2^power.
>>> solution(1000)
1366
>>> solution(50)
76
>>> solution(20)
31
>>> solution(15)
26
"""
num = 2 ** power
string_num = str(num)
list_num = list(string_num)
sum_of_num = 0
for i in list_num:
sum_of_num += int(i)
return sum_of_num
if __name__ == "__main__":
power = int(input("Enter the power of 2: ").strip())
print("2 ^ ", power, " = ", 2 ** power)
result = solution(power)
print("Sum of the digits is: ", result)
| """
Problem 16: https://projecteuler.net/problem=16
2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26.
What is the sum of the digits of the number 2^1000?
"""
def solution(power: int = 1000) -> int:
"""Returns the sum of the digits of the number 2^power.
>>> solution(1000)
1366
>>> solution(50)
76
>>> solution(20)
31
>>> solution(15)
26
"""
num = 2 ** power
string_num = str(num)
list_num = list(string_num)
sum_of_num = 0
for i in list_num:
sum_of_num += int(i)
return sum_of_num
if __name__ == "__main__":
power = int(input("Enter the power of 2: ").strip())
print("2 ^ ", power, " = ", 2 ** power)
result = solution(power)
print("Sum of the digits is: ", result)
| -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
"""The following implementation assumes that the activities
are already sorted according to their finish time"""
"""Prints a maximum set of activities that can be done by a
single person, one at a time"""
# n --> Total number of activities
# start[]--> An array that contains start time of all activities
# finish[] --> An array that contains finish time of all activities
def printMaxActivities(start, finish):
"""
>>> start = [1, 3, 0, 5, 8, 5]
>>> finish = [2, 4, 6, 7, 9, 9]
>>> printMaxActivities(start, finish)
The following activities are selected:
0,1,3,4,
"""
n = len(finish)
print("The following activities are selected:")
# The first activity is always selected
i = 0
print(i, end=",")
# Consider rest of the activities
for j in range(n):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(j, end=",")
i = j
if __name__ == "__main__":
import doctest
doctest.testmod()
start = [1, 3, 0, 5, 8, 5]
finish = [2, 4, 6, 7, 9, 9]
printMaxActivities(start, finish)
| # flake8: noqa
"""The following implementation assumes that the activities
are already sorted according to their finish time"""
"""Prints a maximum set of activities that can be done by a
single person, one at a time"""
# n --> Total number of activities
# start[]--> An array that contains start time of all activities
# finish[] --> An array that contains finish time of all activities
def printMaxActivities(start, finish):
"""
>>> start = [1, 3, 0, 5, 8, 5]
>>> finish = [2, 4, 6, 7, 9, 9]
>>> printMaxActivities(start, finish)
The following activities are selected:
0,1,3,4,
"""
n = len(finish)
print("The following activities are selected:")
# The first activity is always selected
i = 0
print(i, end=",")
# Consider rest of the activities
for j in range(n):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(j, end=",")
i = j
if __name__ == "__main__":
import doctest
doctest.testmod()
start = [1, 3, 0, 5, 8, 5]
finish = [2, 4, 6, 7, 9, 9]
printMaxActivities(start, finish)
| -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}`.
| """
Counting Summations
Problem 76: https://projecteuler.net/problem=76
It is possible to write five as a sum in exactly six different ways:
4 + 1
3 + 2
3 + 1 + 1
2 + 2 + 1
2 + 1 + 1 + 1
1 + 1 + 1 + 1 + 1
How many different ways can one hundred be written as a sum of at least two
positive integers?
"""
def solution(m: int = 100) -> int:
"""
Returns the number of different ways the number m can be written as a
sum of at least two positive integers.
>>> solution(100)
190569291
>>> solution(50)
204225
>>> solution(30)
5603
>>> solution(10)
41
>>> solution(5)
6
>>> solution(3)
2
>>> solution(2)
1
>>> solution(1)
0
"""
memo = [[0 for _ in range(m)] for _ in range(m + 1)]
for i in range(m + 1):
memo[i][0] = 1
for n in range(m + 1):
for k in range(1, m):
memo[n][k] += memo[n][k - 1]
if n > k:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1] - 1
if __name__ == "__main__":
print(solution(int(input("Enter a number: ").strip())))
| """
Counting Summations
Problem 76: https://projecteuler.net/problem=76
It is possible to write five as a sum in exactly six different ways:
4 + 1
3 + 2
3 + 1 + 1
2 + 2 + 1
2 + 1 + 1 + 1
1 + 1 + 1 + 1 + 1
How many different ways can one hundred be written as a sum of at least two
positive integers?
"""
def solution(m: int = 100) -> int:
"""
Returns the number of different ways the number m can be written as a
sum of at least two positive integers.
>>> solution(100)
190569291
>>> solution(50)
204225
>>> solution(30)
5603
>>> solution(10)
41
>>> solution(5)
6
>>> solution(3)
2
>>> solution(2)
1
>>> solution(1)
0
"""
memo = [[0 for _ in range(m)] for _ in range(m + 1)]
for i in range(m + 1):
memo[i][0] = 1
for n in range(m + 1):
for k in range(1, m):
memo[n][k] += memo[n][k - 1]
if n > k:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1] - 1
if __name__ == "__main__":
print(solution(int(input("Enter a number: ").strip())))
| -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 things:
def __init__(self, name, value, weight):
self.name = name
self.value = value
self.weight = weight
def __repr__(self):
return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"
def get_value(self):
return self.value
def get_name(self):
return self.name
def get_weight(self):
return self.weight
def value_Weight(self):
return self.value / self.weight
def build_menu(name, value, weight):
menu = []
for i in range(len(value)):
menu.append(things(name[i], value[i], weight[i]))
return menu
def greedy(item, maxCost, keyFunc):
itemsCopy = sorted(item, key=keyFunc, reverse=True)
result = []
totalValue, total_cost = 0.0, 0.0
for i in range(len(itemsCopy)):
if (total_cost + itemsCopy[i].get_weight()) <= maxCost:
result.append(itemsCopy[i])
total_cost += itemsCopy[i].get_weight()
totalValue += itemsCopy[i].get_value()
return (result, totalValue)
def test_greedy():
"""
>>> food = ["Burger", "Pizza", "Coca Cola", "Rice",
... "Sambhar", "Chicken", "Fries", "Milk"]
>>> value = [80, 100, 60, 70, 50, 110, 90, 60]
>>> weight = [40, 60, 40, 70, 100, 85, 55, 70]
>>> foods = build_menu(food, value, weight)
>>> foods # doctest: +NORMALIZE_WHITESPACE
[things(Burger, 80, 40), things(Pizza, 100, 60), things(Coca Cola, 60, 40),
things(Rice, 70, 70), things(Sambhar, 50, 100), things(Chicken, 110, 85),
things(Fries, 90, 55), things(Milk, 60, 70)]
>>> greedy(foods, 500, things.get_value) # doctest: +NORMALIZE_WHITESPACE
([things(Chicken, 110, 85), things(Pizza, 100, 60), things(Fries, 90, 55),
things(Burger, 80, 40), things(Rice, 70, 70), things(Coca Cola, 60, 40),
things(Milk, 60, 70)], 570.0)
"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| class things:
def __init__(self, name, value, weight):
self.name = name
self.value = value
self.weight = weight
def __repr__(self):
return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"
def get_value(self):
return self.value
def get_name(self):
return self.name
def get_weight(self):
return self.weight
def value_Weight(self):
return self.value / self.weight
def build_menu(name, value, weight):
menu = []
for i in range(len(value)):
menu.append(things(name[i], value[i], weight[i]))
return menu
def greedy(item, maxCost, keyFunc):
itemsCopy = sorted(item, key=keyFunc, reverse=True)
result = []
totalValue, total_cost = 0.0, 0.0
for i in range(len(itemsCopy)):
if (total_cost + itemsCopy[i].get_weight()) <= maxCost:
result.append(itemsCopy[i])
total_cost += itemsCopy[i].get_weight()
totalValue += itemsCopy[i].get_value()
return (result, totalValue)
def test_greedy():
"""
>>> food = ["Burger", "Pizza", "Coca Cola", "Rice",
... "Sambhar", "Chicken", "Fries", "Milk"]
>>> value = [80, 100, 60, 70, 50, 110, 90, 60]
>>> weight = [40, 60, 40, 70, 100, 85, 55, 70]
>>> foods = build_menu(food, value, weight)
>>> foods # doctest: +NORMALIZE_WHITESPACE
[things(Burger, 80, 40), things(Pizza, 100, 60), things(Coca Cola, 60, 40),
things(Rice, 70, 70), things(Sambhar, 50, 100), things(Chicken, 110, 85),
things(Fries, 90, 55), things(Milk, 60, 70)]
>>> greedy(foods, 500, things.get_value) # doctest: +NORMALIZE_WHITESPACE
([things(Chicken, 110, 85), things(Pizza, 100, 60), things(Fries, 90, 55),
things(Burger, 80, 40), things(Rice, 70, 70), things(Coca Cola, 60, 40),
things(Milk, 60, 70)], 570.0)
"""
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}`.
| """
author: Christian Bender
date: 21.12.2017
class: XORCipher
This class implements the XOR-cipher algorithm and provides
some useful methods for encrypting and decrypting strings and
files.
Overview about methods
- encrypt : list of char
- decrypt : list of char
- encrypt_string : str
- decrypt_string : str
- encrypt_file : boolean
- decrypt_file : boolean
"""
class XORCipher:
def __init__(self, key: int = 0):
"""
simple constructor that receives a key or uses
default key = 0
"""
# private field
self.__key = key
def encrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = []
for ch in content:
ans.append(chr(ord(ch) ^ key))
return ans
def decrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type list and 'key' of type int
output: decrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, list)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = []
for ch in content:
ans.append(chr(ord(ch) ^ key))
return ans
def encrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def decrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: decrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def encrypt_file(self, file: str, key: int = 0) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if encrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin:
with open("encrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(line, key))
except OSError:
return False
return True
def decrypt_file(self, file: str, key: int) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if decrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin:
with open("decrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(line, key))
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| """
author: Christian Bender
date: 21.12.2017
class: XORCipher
This class implements the XOR-cipher algorithm and provides
some useful methods for encrypting and decrypting strings and
files.
Overview about methods
- encrypt : list of char
- decrypt : list of char
- encrypt_string : str
- decrypt_string : str
- encrypt_file : boolean
- decrypt_file : boolean
"""
class XORCipher:
def __init__(self, key: int = 0):
"""
simple constructor that receives a key or uses
default key = 0
"""
# private field
self.__key = key
def encrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = []
for ch in content:
ans.append(chr(ord(ch) ^ key))
return ans
def decrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type list and 'key' of type int
output: decrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, list)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = []
for ch in content:
ans.append(chr(ord(ch) ^ key))
return ans
def encrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def decrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: decrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def encrypt_file(self, file: str, key: int = 0) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if encrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin:
with open("encrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(line, key))
except OSError:
return False
return True
def decrypt_file(self, file: str, key: int) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if decrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin:
with open("decrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(line, key))
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| -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 nth term of the sequence of triangle numbers is given by, tn = ½n(n+1); so
the first ten triangle numbers are:
1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...
By converting each letter in a word to a number corresponding to its
alphabetical position and adding these values we form a word value. For example,
the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a
triangle number then we shall call the word a triangle word.
Using words.txt (right click and 'Save Link/Target As...'), a 16K text file
containing nearly two-thousand common English words, how many are triangle
words?
"""
import os
# Precomputes a list of the 100 first triangular numbers
TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def solution():
"""
Finds the amount of triangular words in the words file.
>>> solution()
162
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
wordsFilePath = os.path.join(script_dir, "words.txt")
words = ""
with open(wordsFilePath) as f:
words = f.readline()
words = list(map(lambda word: word.strip('"'), words.strip("\r\n").split(",")))
words = list(
filter(
lambda word: word in TRIANGULAR_NUMBERS,
map(lambda word: sum(map(lambda x: ord(x) - 64, word)), words),
)
)
return len(words)
if __name__ == "__main__":
print(solution())
| """
The nth term of the sequence of triangle numbers is given by, tn = ½n(n+1); so
the first ten triangle numbers are:
1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...
By converting each letter in a word to a number corresponding to its
alphabetical position and adding these values we form a word value. For example,
the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a
triangle number then we shall call the word a triangle word.
Using words.txt (right click and 'Save Link/Target As...'), a 16K text file
containing nearly two-thousand common English words, how many are triangle
words?
"""
import os
# Precomputes a list of the 100 first triangular numbers
TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def solution():
"""
Finds the amount of triangular words in the words file.
>>> solution()
162
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
wordsFilePath = os.path.join(script_dir, "words.txt")
words = ""
with open(wordsFilePath) as f:
words = f.readline()
words = list(map(lambda word: word.strip('"'), words.strip("\r\n").split(",")))
words = list(
filter(
lambda word: word in TRIANGULAR_NUMBERS,
map(lambda word: sum(map(lambda x: ord(x) - 64, word)), words),
)
)
return len(words)
if __name__ == "__main__":
print(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}`.
| import math
from numpy import inf
from scipy.integrate import quad
def gamma(num: float) -> float:
"""
https://en.wikipedia.org/wiki/Gamma_function
In mathematics, the gamma function is one commonly
used extension of the factorial function to complex numbers.
The gamma function is defined for all complex numbers except the non-positive
integers
>>> gamma(-1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(0)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(9)
40320.0
>>> from math import gamma as math_gamma
>>> all(.99999999 < gamma(i) / math_gamma(i) <= 1.000000001
... for i in range(1, 50))
True
>>> from math import gamma as math_gamma
>>> gamma(-1)/math_gamma(-1) <= 1.000000001
Traceback (most recent call last):
...
ValueError: math domain error
>>> from math import gamma as math_gamma
>>> gamma(3.3) - math_gamma(3.3) <= 0.00000001
True
"""
if num <= 0:
raise ValueError("math domain error")
return quad(integrand, 0, inf, args=(num))[0]
def integrand(x: float, z: float) -> float:
return math.pow(x, z - 1) * math.exp(-x)
if __name__ == "__main__":
from doctest import testmod
testmod()
| import math
from numpy import inf
from scipy.integrate import quad
def gamma(num: float) -> float:
"""
https://en.wikipedia.org/wiki/Gamma_function
In mathematics, the gamma function is one commonly
used extension of the factorial function to complex numbers.
The gamma function is defined for all complex numbers except the non-positive
integers
>>> gamma(-1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(0)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(9)
40320.0
>>> from math import gamma as math_gamma
>>> all(.99999999 < gamma(i) / math_gamma(i) <= 1.000000001
... for i in range(1, 50))
True
>>> from math import gamma as math_gamma
>>> gamma(-1)/math_gamma(-1) <= 1.000000001
Traceback (most recent call last):
...
ValueError: math domain error
>>> from math import gamma as math_gamma
>>> gamma(3.3) - math_gamma(3.3) <= 0.00000001
True
"""
if num <= 0:
raise ValueError("math domain error")
return quad(integrand, 0, inf, args=(num))[0]
def integrand(x: float, z: float) -> float:
return math.pow(x, z - 1) * math.exp(-x)
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}`.
| -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}`.
| """
Each character on a computer is assigned a unique code and the preferred standard is
ASCII (American Standard Code for Information Interchange).
For example, uppercase A = 65, asterisk (*) = 42, and lowercase k = 107.
A modern encryption method is to take a text file, convert the bytes to ASCII, then
XOR each byte with a given value, taken from a secret key. The advantage with the
XOR function is that using the same encryption key on the cipher text, restores
the plain text; for example, 65 XOR 42 = 107, then 107 XOR 42 = 65.
For unbreakable encryption, the key is the same length as the plain text message, and
the key is made up of random bytes. The user would keep the encrypted message and the
encryption key in different locations, and without both "halves", it is impossible to
decrypt the message.
Unfortunately, this method is impractical for most users, so the modified method is
to use a password as a key. If the password is shorter than the message, which is
likely, the key is repeated cyclically throughout the message. The balance for this
method is using a sufficiently long password key for security, but short enough to
be memorable.
Your task has been made easy, as the encryption key consists of three lower case
characters. Using p059_cipher.txt (right click and 'Save Link/Target As...'), a
file containing the encrypted ASCII codes, and the knowledge that the plain text
must contain common English words, decrypt the message and find the sum of the ASCII
values in the original text.
"""
import string
from itertools import cycle, product
from pathlib import Path
from typing import List, Optional, Set, Tuple
VALID_CHARS: str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
LOWERCASE_INTS: List[int] = [ord(letter) for letter in string.ascii_lowercase]
VALID_INTS: Set[int] = {ord(char) for char in VALID_CHARS}
COMMON_WORDS: List[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def try_key(ciphertext: List[int], key: Tuple[int, ...]) -> Optional[str]:
"""
Given an encrypted message and a possible 3-character key, decrypt the message.
If the decrypted message contains a invalid character, i.e. not an ASCII letter,
a digit, punctuation or whitespace, then we know the key is incorrect, so return
None.
>>> try_key([0, 17, 20, 4, 27], (104, 116, 120))
'hello'
>>> try_key([68, 10, 300, 4, 27], (104, 116, 120)) is None
True
"""
decoded: str = ""
keychar: int
cipherchar: int
decodedchar: int
for keychar, cipherchar in zip(cycle(key), ciphertext):
decodedchar = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(decodedchar)
return decoded
def filter_valid_chars(ciphertext: List[int]) -> List[str]:
"""
Given an encrypted message, test all 3-character strings to try and find the
key. Return a list of the possible decrypted messages.
>>> from itertools import cycle
>>> text = "The enemy's gate is down"
>>> key = "end"
>>> encoded = [ord(k) ^ ord(c) for k,c in zip(cycle(key), text)]
>>> text in filter_valid_chars(encoded)
True
"""
possibles: List[str] = []
for key in product(LOWERCASE_INTS, repeat=3):
encoded = try_key(ciphertext, key)
if encoded is not None:
possibles.append(encoded)
return possibles
def filter_common_word(possibles: List[str], common_word: str) -> List[str]:
"""
Given a list of possible decoded messages, narrow down the possibilities
for checking for the presence of a specified common word. Only decoded messages
containing common_word will be returned.
>>> filter_common_word(['asfla adf', 'I am here', ' !?! #a'], 'am')
['I am here']
>>> filter_common_word(['athla amf', 'I am here', ' !?! #a'], 'am')
['athla amf', 'I am here']
"""
return [possible for possible in possibles if common_word in possible.lower()]
def solution(filename: str = "p059_cipher.txt") -> int:
"""
Test the ciphertext against all possible 3-character keys, then narrow down the
possibilities by filtering using common words until there's only one possible
decoded message.
>>> solution("test_cipher.txt")
3000
"""
ciphertext: List[int]
possibles: List[str]
common_word: str
decoded_text: str
data: str = Path(__file__).parent.joinpath(filename).read_text(encoding="utf-8")
ciphertext = [int(number) for number in data.strip().split(",")]
possibles = filter_valid_chars(ciphertext)
for common_word in COMMON_WORDS:
possibles = filter_common_word(possibles, common_word)
if len(possibles) == 1:
break
decoded_text = possibles[0]
return sum([ord(char) for char in decoded_text])
if __name__ == "__main__":
print(f"{solution() = }")
| """
Each character on a computer is assigned a unique code and the preferred standard is
ASCII (American Standard Code for Information Interchange).
For example, uppercase A = 65, asterisk (*) = 42, and lowercase k = 107.
A modern encryption method is to take a text file, convert the bytes to ASCII, then
XOR each byte with a given value, taken from a secret key. The advantage with the
XOR function is that using the same encryption key on the cipher text, restores
the plain text; for example, 65 XOR 42 = 107, then 107 XOR 42 = 65.
For unbreakable encryption, the key is the same length as the plain text message, and
the key is made up of random bytes. The user would keep the encrypted message and the
encryption key in different locations, and without both "halves", it is impossible to
decrypt the message.
Unfortunately, this method is impractical for most users, so the modified method is
to use a password as a key. If the password is shorter than the message, which is
likely, the key is repeated cyclically throughout the message. The balance for this
method is using a sufficiently long password key for security, but short enough to
be memorable.
Your task has been made easy, as the encryption key consists of three lower case
characters. Using p059_cipher.txt (right click and 'Save Link/Target As...'), a
file containing the encrypted ASCII codes, and the knowledge that the plain text
must contain common English words, decrypt the message and find the sum of the ASCII
values in the original text.
"""
import string
from itertools import cycle, product
from pathlib import Path
from typing import List, Optional, Set, Tuple
VALID_CHARS: str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
LOWERCASE_INTS: List[int] = [ord(letter) for letter in string.ascii_lowercase]
VALID_INTS: Set[int] = {ord(char) for char in VALID_CHARS}
COMMON_WORDS: List[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def try_key(ciphertext: List[int], key: Tuple[int, ...]) -> Optional[str]:
"""
Given an encrypted message and a possible 3-character key, decrypt the message.
If the decrypted message contains a invalid character, i.e. not an ASCII letter,
a digit, punctuation or whitespace, then we know the key is incorrect, so return
None.
>>> try_key([0, 17, 20, 4, 27], (104, 116, 120))
'hello'
>>> try_key([68, 10, 300, 4, 27], (104, 116, 120)) is None
True
"""
decoded: str = ""
keychar: int
cipherchar: int
decodedchar: int
for keychar, cipherchar in zip(cycle(key), ciphertext):
decodedchar = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(decodedchar)
return decoded
def filter_valid_chars(ciphertext: List[int]) -> List[str]:
"""
Given an encrypted message, test all 3-character strings to try and find the
key. Return a list of the possible decrypted messages.
>>> from itertools import cycle
>>> text = "The enemy's gate is down"
>>> key = "end"
>>> encoded = [ord(k) ^ ord(c) for k,c in zip(cycle(key), text)]
>>> text in filter_valid_chars(encoded)
True
"""
possibles: List[str] = []
for key in product(LOWERCASE_INTS, repeat=3):
encoded = try_key(ciphertext, key)
if encoded is not None:
possibles.append(encoded)
return possibles
def filter_common_word(possibles: List[str], common_word: str) -> List[str]:
"""
Given a list of possible decoded messages, narrow down the possibilities
for checking for the presence of a specified common word. Only decoded messages
containing common_word will be returned.
>>> filter_common_word(['asfla adf', 'I am here', ' !?! #a'], 'am')
['I am here']
>>> filter_common_word(['athla amf', 'I am here', ' !?! #a'], 'am')
['athla amf', 'I am here']
"""
return [possible for possible in possibles if common_word in possible.lower()]
def solution(filename: str = "p059_cipher.txt") -> int:
"""
Test the ciphertext against all possible 3-character keys, then narrow down the
possibilities by filtering using common words until there's only one possible
decoded message.
>>> solution("test_cipher.txt")
3000
"""
ciphertext: List[int]
possibles: List[str]
common_word: str
decoded_text: str
data: str = Path(__file__).parent.joinpath(filename).read_text(encoding="utf-8")
ciphertext = [int(number) for number in data.strip().split(",")]
possibles = filter_valid_chars(ciphertext)
for common_word in COMMON_WORDS:
possibles = filter_common_word(possibles, common_word)
if len(possibles) == 1:
break
decoded_text = possibles[0]
return sum([ord(char) for char in decoded_text])
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}`.
| # Print all subset combinations of n element in given set of r element.
def combination_util(arr, n, r, index, data, i):
"""
Current combination is ready to be printed, print it
arr[] ---> Input Array
data[] ---> Temporary array to store current combination
start & end ---> Staring and Ending indexes in arr[]
index ---> Current index in data[]
r ---> Size of a combination to be printed
"""
if index == r:
for j in range(r):
print(data[j], end=" ")
print(" ")
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
data[index] = arr[i]
combination_util(arr, n, r, index + 1, data, i + 1)
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(arr, n, r, index, data, i + 1)
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def print_combination(arr, n, r):
# A temporary array to store all combination one by one
data = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(arr, n, r, 0, data, 0)
# Driver function to check for above function
arr = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| # Print all subset combinations of n element in given set of r element.
def combination_util(arr, n, r, index, data, i):
"""
Current combination is ready to be printed, print it
arr[] ---> Input Array
data[] ---> Temporary array to store current combination
start & end ---> Staring and Ending indexes in arr[]
index ---> Current index in data[]
r ---> Size of a combination to be printed
"""
if index == r:
for j in range(r):
print(data[j], end=" ")
print(" ")
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
data[index] = arr[i]
combination_util(arr, n, r, index + 1, data, i + 1)
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(arr, n, r, index, data, i + 1)
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def print_combination(arr, n, r):
# A temporary array to store all combination one by one
data = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(arr, n, r, 0, data, 0)
# Driver function to check for above function
arr = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| -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.
"""
import math
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_of_squares = sum([i * i for i in range(1, n + 1)])
square_of_sum = int(math.pow(sum(range(1, n + 1)), 2))
return square_of_sum - sum_of_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.
"""
import math
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_of_squares = sum([i * i for i in range(1, n + 1)])
square_of_sum = int(math.pow(sum(range(1, n + 1)), 2))
return square_of_sum - sum_of_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}`.
| """
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 | 4,314 | 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 oct_to_decimal(oct_string: str) -> int:
"""
Convert a octal value to its decimal equivalent
>>> oct_to_decimal("12")
10
>>> oct_to_decimal(" 12 ")
10
>>> oct_to_decimal("-45")
-37
>>> oct_to_decimal("2-0Fm")
Traceback (most recent call last):
...
ValueError: Non-octal value was passed to the function
>>> oct_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> oct_to_decimal("19")
Traceback (most recent call last):
...
ValueError: Non-octal value was passed to the function
"""
oct_string = str(oct_string).strip()
if not oct_string:
raise ValueError("Empty string was passed to the function")
is_negative = oct_string[0] == "-"
if is_negative:
oct_string = oct_string[1:]
if not oct_string.isdigit() or not all(0 <= int(char) <= 7 for char in oct_string):
raise ValueError("Non-octal value was passed to the function")
decimal_number = 0
for char in oct_string:
decimal_number = 8 * decimal_number + int(char)
if is_negative:
decimal_number = -decimal_number
return decimal_number
if __name__ == "__main__":
from doctest import testmod
testmod()
| def oct_to_decimal(oct_string: str) -> int:
"""
Convert a octal value to its decimal equivalent
>>> oct_to_decimal("12")
10
>>> oct_to_decimal(" 12 ")
10
>>> oct_to_decimal("-45")
-37
>>> oct_to_decimal("2-0Fm")
Traceback (most recent call last):
...
ValueError: Non-octal value was passed to the function
>>> oct_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> oct_to_decimal("19")
Traceback (most recent call last):
...
ValueError: Non-octal value was passed to the function
"""
oct_string = str(oct_string).strip()
if not oct_string:
raise ValueError("Empty string was passed to the function")
is_negative = oct_string[0] == "-"
if is_negative:
oct_string = oct_string[1:]
if not oct_string.isdigit() or not all(0 <= int(char) <= 7 for char in oct_string):
raise ValueError("Non-octal value was passed to the function")
decimal_number = 0
for char in oct_string:
decimal_number = 8 * decimal_number + int(char)
if is_negative:
decimal_number = -decimal_number
return decimal_number
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}`.
| # https://www.tutorialspoint.com/python3/bitwise_operators_example.htm
def binary_xor(a: int, b: int) -> str:
"""
Take in 2 integers, convert them to binary,
return a binary number that is the
result of a binary xor operation on the integers provided.
>>> binary_xor(25, 32)
'0b111001'
>>> binary_xor(37, 50)
'0b010111'
>>> binary_xor(21, 30)
'0b01011'
>>> binary_xor(58, 73)
'0b1110011'
>>> binary_xor(0, 255)
'0b11111111'
>>> binary_xor(256, 256)
'0b000000000'
>>> binary_xor(0, -1)
Traceback (most recent call last):
...
ValueError: the value of both input must be positive
>>> binary_xor(0, 1.1)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> binary_xor("0", "1")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0 or b < 0:
raise ValueError("the value of both input must be positive")
a_binary = str(bin(a))[2:] # remove the leading "0b"
b_binary = str(bin(b))[2:] # remove the leading "0b"
max_len = max(len(a_binary), len(b_binary))
return "0b" + "".join(
str(int(char_a != char_b))
for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # https://www.tutorialspoint.com/python3/bitwise_operators_example.htm
def binary_xor(a: int, b: int) -> str:
"""
Take in 2 integers, convert them to binary,
return a binary number that is the
result of a binary xor operation on the integers provided.
>>> binary_xor(25, 32)
'0b111001'
>>> binary_xor(37, 50)
'0b010111'
>>> binary_xor(21, 30)
'0b01011'
>>> binary_xor(58, 73)
'0b1110011'
>>> binary_xor(0, 255)
'0b11111111'
>>> binary_xor(256, 256)
'0b000000000'
>>> binary_xor(0, -1)
Traceback (most recent call last):
...
ValueError: the value of both input must be positive
>>> binary_xor(0, 1.1)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> binary_xor("0", "1")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0 or b < 0:
raise ValueError("the value of both input must be positive")
a_binary = str(bin(a))[2:] # remove the leading "0b"
b_binary = str(bin(b))[2:] # remove the leading "0b"
max_len = max(len(a_binary), len(b_binary))
return "0b" + "".join(
str(int(char_a != char_b))
for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
)
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}`.
| """
https://en.wikipedia.org/wiki/Shellsort#Pseudocode
"""
def shell_sort(collection):
"""Pure implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> shell_sort([])
[]
>>> shell_sort([-2, -5, -45])
[-45, -5, -2]
"""
# Marcin Ciura's gap sequence
gaps = [701, 301, 132, 57, 23, 10, 4, 1]
for gap in gaps:
for i in range(gap, len(collection)):
insert_value = collection[i]
j = i
while j >= gap and collection[j - gap] > insert_value:
collection[j] = collection[j - gap]
j -= gap
if j != i:
collection[j] = insert_value
return collection
if __name__ == "__main__":
from doctest import testmod
testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(shell_sort(unsorted))
| """
https://en.wikipedia.org/wiki/Shellsort#Pseudocode
"""
def shell_sort(collection):
"""Pure implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> shell_sort([])
[]
>>> shell_sort([-2, -5, -45])
[-45, -5, -2]
"""
# Marcin Ciura's gap sequence
gaps = [701, 301, 132, 57, 23, 10, 4, 1]
for gap in gaps:
for i in range(gap, len(collection)):
insert_value = collection[i]
j = i
while j >= gap and collection[j - gap] > insert_value:
collection[j] = collection[j - gap]
j -= gap
if j != i:
collection[j] = insert_value
return collection
if __name__ == "__main__":
from doctest import testmod
testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(shell_sort(unsorted))
| -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 .stack import Stack
def balanced_parentheses(parentheses: str) -> bool:
"""Use a stack to check if a string of parentheses is balanced.
>>> balanced_parentheses("([]{})")
True
>>> balanced_parentheses("[()]{}{[()()]()}")
True
>>> balanced_parentheses("[(])")
False
>>> balanced_parentheses("1+2*3-4")
True
>>> balanced_parentheses("")
True
"""
stack = Stack()
bracket_pairs = {"(": ")", "[": "]", "{": "}"}
for bracket in parentheses:
if bracket in bracket_pairs:
stack.push(bracket)
elif bracket in (")", "]", "}"):
if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
return False
return stack.is_empty()
if __name__ == "__main__":
from doctest import testmod
testmod()
examples = ["((()))", "((())", "(()))"]
print("Balanced parentheses demonstration:\n")
for example in examples:
not_str = "" if balanced_parentheses(example) else "not "
print(f"{example} is {not_str}balanced")
| from .stack import Stack
def balanced_parentheses(parentheses: str) -> bool:
"""Use a stack to check if a string of parentheses is balanced.
>>> balanced_parentheses("([]{})")
True
>>> balanced_parentheses("[()]{}{[()()]()}")
True
>>> balanced_parentheses("[(])")
False
>>> balanced_parentheses("1+2*3-4")
True
>>> balanced_parentheses("")
True
"""
stack = Stack()
bracket_pairs = {"(": ")", "[": "]", "{": "}"}
for bracket in parentheses:
if bracket in bracket_pairs:
stack.push(bracket)
elif bracket in (")", "]", "}"):
if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
return False
return stack.is_empty()
if __name__ == "__main__":
from doctest import testmod
testmod()
examples = ["((()))", "((())", "(()))"]
print("Balanced parentheses demonstration:\n")
for example in examples:
not_str = "" if balanced_parentheses(example) else "not "
print(f"{example} is {not_str}balanced")
| -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}`.
| [report]
sort = Cover
omit =
.env/*
| [report]
sort = Cover
omit =
.env/*
| -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
def compare_string(string1: str, string2: str) -> str:
"""
>>> compare_string('0010','0110')
'0_10'
>>> compare_string('0110','1101')
'X'
"""
l1 = list(string1)
l2 = list(string2)
count = 0
for i in range(len(l1)):
if l1[i] != l2[i]:
count += 1
l1[i] = "_"
if count > 1:
return "X"
else:
return "".join(l1)
def check(binary: List[str]) -> List[str]:
"""
>>> check(['0.00.01.5'])
['0.00.01.5']
"""
pi = []
while 1:
check1 = ["$"] * len(binary)
temp = []
for i in range(len(binary)):
for j in range(i + 1, len(binary)):
k = compare_string(binary[i], binary[j])
if k != "X":
check1[i] = "*"
check1[j] = "*"
temp.append(k)
for i in range(len(binary)):
if check1[i] == "$":
pi.append(binary[i])
if len(temp) == 0:
return pi
binary = list(set(temp))
def decimal_to_binary(no_of_variable: int, minterms: List[float]) -> List[str]:
"""
>>> decimal_to_binary(3,[1.5])
['0.00.01.5']
"""
temp = []
s = ""
for m in minterms:
for i in range(no_of_variable):
s = str(m % 2) + s
m //= 2
temp.append(s)
s = ""
return temp
def is_for_table(string1: str, string2: str, count: int) -> bool:
"""
>>> is_for_table('__1','011',2)
True
>>> is_for_table('01_','001',1)
False
"""
l1 = list(string1)
l2 = list(string2)
count_n = 0
for i in range(len(l1)):
if l1[i] != l2[i]:
count_n += 1
if count_n == count:
return True
else:
return False
def selection(chart: List[List[int]], prime_implicants: List[str]) -> List[str]:
"""
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
"""
temp = []
select = [0] * len(chart)
for i in range(len(chart[0])):
count = 0
rem = -1
for j in range(len(chart)):
if chart[j][i] == 1:
count += 1
rem = j
if count == 1:
select[rem] = 1
for i in range(len(select)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(chart)):
chart[k][j] = 0
temp.append(prime_implicants[i])
while 1:
max_n = 0
rem = -1
count_n = 0
for i in range(len(chart)):
count_n = chart[i].count(1)
if count_n > max_n:
max_n = count_n
rem = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(chart)):
chart[j][i] = 0
def prime_implicant_chart(
prime_implicants: List[str], binary: List[str]
) -> List[List[int]]:
"""
>>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
[[1]]
"""
chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
for i in range(len(prime_implicants)):
count = prime_implicants[i].count("_")
for j in range(len(binary)):
if is_for_table(prime_implicants[i], binary[j], count):
chart[i][j] = 1
return chart
def main():
no_of_variable = int(input("Enter the no. of variables\n"))
minterms = [
int(x)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n"
).split()
]
binary = decimal_to_binary(no_of_variable, minterms)
prime_implicants = check(binary)
print("Prime Implicants are:")
print(prime_implicants)
chart = prime_implicant_chart(prime_implicants, binary)
essential_prime_implicants = selection(chart, prime_implicants)
print("Essential Prime Implicants are:")
print(essential_prime_implicants)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| from typing import List
def compare_string(string1: str, string2: str) -> str:
"""
>>> compare_string('0010','0110')
'0_10'
>>> compare_string('0110','1101')
'X'
"""
l1 = list(string1)
l2 = list(string2)
count = 0
for i in range(len(l1)):
if l1[i] != l2[i]:
count += 1
l1[i] = "_"
if count > 1:
return "X"
else:
return "".join(l1)
def check(binary: List[str]) -> List[str]:
"""
>>> check(['0.00.01.5'])
['0.00.01.5']
"""
pi = []
while 1:
check1 = ["$"] * len(binary)
temp = []
for i in range(len(binary)):
for j in range(i + 1, len(binary)):
k = compare_string(binary[i], binary[j])
if k != "X":
check1[i] = "*"
check1[j] = "*"
temp.append(k)
for i in range(len(binary)):
if check1[i] == "$":
pi.append(binary[i])
if len(temp) == 0:
return pi
binary = list(set(temp))
def decimal_to_binary(no_of_variable: int, minterms: List[float]) -> List[str]:
"""
>>> decimal_to_binary(3,[1.5])
['0.00.01.5']
"""
temp = []
s = ""
for m in minterms:
for i in range(no_of_variable):
s = str(m % 2) + s
m //= 2
temp.append(s)
s = ""
return temp
def is_for_table(string1: str, string2: str, count: int) -> bool:
"""
>>> is_for_table('__1','011',2)
True
>>> is_for_table('01_','001',1)
False
"""
l1 = list(string1)
l2 = list(string2)
count_n = 0
for i in range(len(l1)):
if l1[i] != l2[i]:
count_n += 1
if count_n == count:
return True
else:
return False
def selection(chart: List[List[int]], prime_implicants: List[str]) -> List[str]:
"""
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
"""
temp = []
select = [0] * len(chart)
for i in range(len(chart[0])):
count = 0
rem = -1
for j in range(len(chart)):
if chart[j][i] == 1:
count += 1
rem = j
if count == 1:
select[rem] = 1
for i in range(len(select)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(chart)):
chart[k][j] = 0
temp.append(prime_implicants[i])
while 1:
max_n = 0
rem = -1
count_n = 0
for i in range(len(chart)):
count_n = chart[i].count(1)
if count_n > max_n:
max_n = count_n
rem = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(chart)):
chart[j][i] = 0
def prime_implicant_chart(
prime_implicants: List[str], binary: List[str]
) -> List[List[int]]:
"""
>>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
[[1]]
"""
chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
for i in range(len(prime_implicants)):
count = prime_implicants[i].count("_")
for j in range(len(binary)):
if is_for_table(prime_implicants[i], binary[j], count):
chart[i][j] = 1
return chart
def main():
no_of_variable = int(input("Enter the no. of variables\n"))
minterms = [
int(x)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n"
).split()
]
binary = decimal_to_binary(no_of_variable, minterms)
prime_implicants = check(binary)
print("Prime Implicants are:")
print(prime_implicants)
chart = prime_implicant_chart(prime_implicants, binary)
essential_prime_implicants = selection(chart, prime_implicants)
print("Essential Prime Implicants are:")
print(essential_prime_implicants)
if __name__ == "__main__":
import doctest
doctest.testmod()
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}`.
| """
Name scores
Problem 22
Using names.txt (right click and 'Save Link/Target As...'), a 46K text file
containing over five-thousand first names, begin by sorting it into
alphabetical order. Then working out the alphabetical value for each name,
multiply this value by its alphabetical position in the list to obtain a name
score.
For example, when the list is sorted into alphabetical order, COLIN, which is
worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would
obtain a score of 938 × 53 = 49714.
What is the total of all the name scores in the file?
"""
import os
def solution():
"""Returns the total of all the name scores in the file.
>>> solution()
871198282
"""
total_sum = 0
temp_sum = 0
with open(os.path.dirname(__file__) + "/p022_names.txt") as file:
name = str(file.readlines()[0])
name = name.replace('"', "").split(",")
name.sort()
for i in range(len(name)):
for j in name[i]:
temp_sum += ord(j) - ord("A") + 1
total_sum += (i + 1) * temp_sum
temp_sum = 0
return total_sum
if __name__ == "__main__":
print(solution())
| """
Name scores
Problem 22
Using names.txt (right click and 'Save Link/Target As...'), a 46K text file
containing over five-thousand first names, begin by sorting it into
alphabetical order. Then working out the alphabetical value for each name,
multiply this value by its alphabetical position in the list to obtain a name
score.
For example, when the list is sorted into alphabetical order, COLIN, which is
worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would
obtain a score of 938 × 53 = 49714.
What is the total of all the name scores in the file?
"""
import os
def solution():
"""Returns the total of all the name scores in the file.
>>> solution()
871198282
"""
total_sum = 0
temp_sum = 0
with open(os.path.dirname(__file__) + "/p022_names.txt") as file:
name = str(file.readlines()[0])
name = name.replace('"', "").split(",")
name.sort()
for i in range(len(name)):
for j in name[i]:
temp_sum += ord(j) - ord("A") + 1
total_sum += (i + 1) * temp_sum
temp_sum = 0
return total_sum
if __name__ == "__main__":
print(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}`.
| #!/bin/sh
#
# An example hook script to prepare a packed repository for use over
# dumb transports.
#
# To enable this hook, rename this file to "post-update".
exec git update-server-info
| #!/bin/sh
#
# An example hook script to prepare a packed repository for use over
# dumb transports.
#
# To enable this hook, rename this file to "post-update".
exec git update-server-info
| -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 __future__ import annotations
def abs_max(x: list[int]) -> int:
"""
>>> abs_max([0,5,1,11])
11
>>> abs_max([3,-10,-2])
-10
"""
j = x[0]
for i in x:
if abs(i) > abs(j):
j = i
return j
def abs_max_sort(x):
"""
>>> abs_max_sort([0,5,1,11])
11
>>> abs_max_sort([3,-10,-2])
-10
"""
return sorted(x, key=abs)[-1]
def main():
a = [1, 2, -11]
assert abs_max(a) == -11
assert abs_max_sort(a) == -11
if __name__ == "__main__":
main()
| from __future__ import annotations
def abs_max(x: list[int]) -> int:
"""
>>> abs_max([0,5,1,11])
11
>>> abs_max([3,-10,-2])
-10
"""
j = x[0]
for i in x:
if abs(i) > abs(j):
j = i
return j
def abs_max_sort(x):
"""
>>> abs_max_sort([0,5,1,11])
11
>>> abs_max_sort([3,-10,-2])
-10
"""
return sorted(x, key=abs)[-1]
def main():
a = [1, 2, -11]
assert abs_max(a) == -11
assert abs_max_sort(a) == -11
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}`.
| 37107287533902102798797998220837590246510135740250
46376937677490009712648124896970078050417018260538
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20849603980134001723930671666823555245252804609722
53503534226472524250874054075591789781264330331690
| 37107287533902102798797998220837590246510135740250
46376937677490009712648124896970078050417018260538
74324986199524741059474233309513058123726617309629
91942213363574161572522430563301811072406154908250
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47451445736001306439091167216856844588711603153276
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92575867718337217661963751590579239728245598838407
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20849603980134001723930671666823555245252804609722
53503534226472524250874054075591789781264330331690
| -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}`.
| 536fb4bca48f69cb66cfbd03aeb02550def07977
| 536fb4bca48f69cb66cfbd03aeb02550def07977
| -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 27
https://projecteuler.net/problem=27
Problem Statement:
Euler discovered the remarkable quadratic formula:
n2 + n + 41
It turns out that the formula will produce 40 primes for the consecutive values
n = 0 to 39. However, when n = 40, 402 + 40 + 41 = 40(40 + 1) + 41 is divisible
by 41, and certainly when n = 41, 412 + 41 + 41 is clearly divisible by 41.
The incredible formula n2 − 79n + 1601 was discovered, which produces 80 primes
for the consecutive values n = 0 to 79. The product of the coefficients, −79 and
1601, is −126479.
Considering quadratics of the form:
n² + an + b, where |a| < 1000 and |b| < 1000
where |n| is the modulus/absolute value of ne.g. |11| = 11 and |−4| = 4
Find the product of the coefficients, a and b, for the quadratic expression that
produces the maximum number of primes for consecutive values of n, starting with
n = 0.
"""
import math
def is_prime(k: int) -> bool:
"""
Determine if a number is prime
>>> is_prime(10)
False
>>> is_prime(11)
True
"""
if k < 2 or k % 2 == 0:
return False
elif k == 2:
return True
else:
for x in range(3, int(math.sqrt(k) + 1), 2):
if k % x == 0:
return False
return True
def solution(a_limit: int = 1000, b_limit: int = 1000) -> int:
"""
>>> solution(1000, 1000)
-59231
>>> solution(200, 1000)
-59231
>>> solution(200, 200)
-4925
>>> solution(-1000, 1000)
0
>>> solution(-1000, -1000)
0
"""
longest = [0, 0, 0] # length, a, b
for a in range((a_limit * -1) + 1, a_limit):
for b in range(2, b_limit):
if is_prime(b):
count = 0
n = 0
while is_prime((n ** 2) + (a * n) + b):
count += 1
n += 1
if count > longest[0]:
longest = [count, a, b]
ans = longest[1] * longest[2]
return ans
if __name__ == "__main__":
print(solution(1000, 1000))
| """
Project Euler Problem 27
https://projecteuler.net/problem=27
Problem Statement:
Euler discovered the remarkable quadratic formula:
n2 + n + 41
It turns out that the formula will produce 40 primes for the consecutive values
n = 0 to 39. However, when n = 40, 402 + 40 + 41 = 40(40 + 1) + 41 is divisible
by 41, and certainly when n = 41, 412 + 41 + 41 is clearly divisible by 41.
The incredible formula n2 − 79n + 1601 was discovered, which produces 80 primes
for the consecutive values n = 0 to 79. The product of the coefficients, −79 and
1601, is −126479.
Considering quadratics of the form:
n² + an + b, where |a| < 1000 and |b| < 1000
where |n| is the modulus/absolute value of ne.g. |11| = 11 and |−4| = 4
Find the product of the coefficients, a and b, for the quadratic expression that
produces the maximum number of primes for consecutive values of n, starting with
n = 0.
"""
import math
def is_prime(k: int) -> bool:
"""
Determine if a number is prime
>>> is_prime(10)
False
>>> is_prime(11)
True
"""
if k < 2 or k % 2 == 0:
return False
elif k == 2:
return True
else:
for x in range(3, int(math.sqrt(k) + 1), 2):
if k % x == 0:
return False
return True
def solution(a_limit: int = 1000, b_limit: int = 1000) -> int:
"""
>>> solution(1000, 1000)
-59231
>>> solution(200, 1000)
-59231
>>> solution(200, 200)
-4925
>>> solution(-1000, 1000)
0
>>> solution(-1000, -1000)
0
"""
longest = [0, 0, 0] # length, a, b
for a in range((a_limit * -1) + 1, a_limit):
for b in range(2, b_limit):
if is_prime(b):
count = 0
n = 0
while is_prime((n ** 2) + (a * n) + b):
count += 1
n += 1
if count > longest[0]:
longest = [count, a, b]
ans = longest[1] * longest[2]
return ans
if __name__ == "__main__":
print(solution(1000, 1000))
| -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.tutorialspoint.com/python3/bitwise_operators_example.htm
def binary_or(a: int, b: int) -> str:
"""
Take in 2 integers, convert them to binary, and return a binary number that is the
result of a binary or operation on the integers provided.
>>> binary_or(25, 32)
'0b111001'
>>> binary_or(37, 50)
'0b110111'
>>> binary_or(21, 30)
'0b11111'
>>> binary_or(58, 73)
'0b1111011'
>>> binary_or(0, 255)
'0b11111111'
>>> binary_or(0, 256)
'0b100000000'
>>> binary_or(0, -1)
Traceback (most recent call last):
...
ValueError: the value of both input must be positive
>>> binary_or(0, 1.1)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> binary_or("0", "1")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0 or b < 0:
raise ValueError("the value of both input must be positive")
a_binary = str(bin(a))[2:] # remove the leading "0b"
b_binary = str(bin(b))[2:]
max_len = max(len(a_binary), len(b_binary))
return "0b" + "".join(
str(int("1" in (char_a, char_b)))
for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # https://www.tutorialspoint.com/python3/bitwise_operators_example.htm
def binary_or(a: int, b: int) -> str:
"""
Take in 2 integers, convert them to binary, and return a binary number that is the
result of a binary or operation on the integers provided.
>>> binary_or(25, 32)
'0b111001'
>>> binary_or(37, 50)
'0b110111'
>>> binary_or(21, 30)
'0b11111'
>>> binary_or(58, 73)
'0b1111011'
>>> binary_or(0, 255)
'0b11111111'
>>> binary_or(0, 256)
'0b100000000'
>>> binary_or(0, -1)
Traceback (most recent call last):
...
ValueError: the value of both input must be positive
>>> binary_or(0, 1.1)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> binary_or("0", "1")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0 or b < 0:
raise ValueError("the value of both input must be positive")
a_binary = str(bin(a))[2:] # remove the leading "0b"
b_binary = str(bin(b))[2:]
max_len = max(len(a_binary), len(b_binary))
return "0b" + "".join(
str(int("1" in (char_a, char_b)))
for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
)
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}`.
| def factors_of_a_number(num: int) -> list:
"""
>>> factors_of_a_number(1)
[1]
>>> factors_of_a_number(5)
[1, 5]
>>> factors_of_a_number(24)
[1, 2, 3, 4, 6, 8, 12, 24]
>>> factors_of_a_number(-24)
[]
"""
return [i for i in range(1, num + 1) if num % i == 0]
if __name__ == "__main__":
num = int(input("Enter a number to find its factors: "))
factors = factors_of_a_number(num)
print(f"{num} has {len(factors)} factors: {', '.join(str(f) for f in factors)}")
| def factors_of_a_number(num: int) -> list:
"""
>>> factors_of_a_number(1)
[1]
>>> factors_of_a_number(5)
[1, 5]
>>> factors_of_a_number(24)
[1, 2, 3, 4, 6, 8, 12, 24]
>>> factors_of_a_number(-24)
[]
"""
return [i for i in range(1, num + 1) if num % i == 0]
if __name__ == "__main__":
num = int(input("Enter a number to find its factors: "))
factors = factors_of_a_number(num)
print(f"{num} has {len(factors)} factors: {', '.join(str(f) for f in factors)}")
| -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 script demonstrates the implementation of the Softmax function.
Its a function that takes as input a vector of K real numbers, and normalizes
it into a probability distribution consisting of K probabilities proportional
to the exponentials of the input numbers. After softmax, the elements of the
vector always sum up to 1.
Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Softmax_function
"""
import numpy as np
def softmax(vector):
"""
Implements the softmax function
Parameters:
vector (np.array,list,tuple): A numpy array of shape (1,n)
consisting of real values or a similar list,tuple
Returns:
softmax_vec (np.array): The input numpy array after applying
softmax.
The softmax vector adds up to one. We need to ceil to mitigate for
precision
>>> np.ceil(np.sum(softmax([1,2,3,4])))
1.0
>>> vec = np.array([5,5])
>>> softmax(vec)
array([0.5, 0.5])
>>> softmax([0])
array([1.])
"""
# Calculate e^x for each x in your vector where e is Euler's
# number (approximately 2.718)
exponentVector = np.exp(vector)
# Add up the all the exponentials
sumOfExponents = np.sum(exponentVector)
# Divide every exponent by the sum of all exponents
softmax_vector = exponentVector / sumOfExponents
return softmax_vector
if __name__ == "__main__":
print(softmax((0,)))
| """
This script demonstrates the implementation of the Softmax function.
Its a function that takes as input a vector of K real numbers, and normalizes
it into a probability distribution consisting of K probabilities proportional
to the exponentials of the input numbers. After softmax, the elements of the
vector always sum up to 1.
Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Softmax_function
"""
import numpy as np
def softmax(vector):
"""
Implements the softmax function
Parameters:
vector (np.array,list,tuple): A numpy array of shape (1,n)
consisting of real values or a similar list,tuple
Returns:
softmax_vec (np.array): The input numpy array after applying
softmax.
The softmax vector adds up to one. We need to ceil to mitigate for
precision
>>> np.ceil(np.sum(softmax([1,2,3,4])))
1.0
>>> vec = np.array([5,5])
>>> softmax(vec)
array([0.5, 0.5])
>>> softmax([0])
array([1.])
"""
# Calculate e^x for each x in your vector where e is Euler's
# number (approximately 2.718)
exponentVector = np.exp(vector)
# Add up the all the exponentials
sumOfExponents = np.sum(exponentVector)
# Divide every exponent by the sum of all exponents
softmax_vector = exponentVector / sumOfExponents
return softmax_vector
if __name__ == "__main__":
print(softmax((0,)))
| -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 sys
from collections import defaultdict
def PrimsAlgorithm(l): # noqa: E741
nodePosition = []
def get_position(vertex):
return nodePosition[vertex]
def set_position(vertex, pos):
nodePosition[vertex] = pos
def top_to_bottom(heap, start, size, positions):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
m = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
m = 2 * start + 1
else:
m = 2 * start + 2
if heap[m] < heap[start]:
temp, temp1 = heap[m], positions[m]
heap[m], positions[m] = heap[start], positions[start]
heap[start], positions[start] = temp, temp1
temp = get_position(positions[m])
set_position(positions[m], get_position(positions[start]))
set_position(positions[start], temp)
top_to_bottom(heap, m, size, positions)
# Update function if value of any node in min-heap decreases
def bottom_to_top(val, index, heap, position):
temp = position[index]
while index != 0:
if index % 2 == 0:
parent = int((index - 2) / 2)
else:
parent = int((index - 1) / 2)
if val < heap[parent]:
heap[index] = heap[parent]
position[index] = position[parent]
set_position(position[parent], index)
else:
heap[index] = val
position[index] = temp
set_position(temp, index)
break
index = parent
else:
heap[0] = val
position[0] = temp
set_position(temp, 0)
def heapify(heap, positions):
start = len(heap) // 2 - 1
for i in range(start, -1, -1):
top_to_bottom(heap, i, len(heap), positions)
def deleteMinimum(heap, positions):
temp = positions[0]
heap[0] = sys.maxsize
top_to_bottom(heap, 0, len(heap), positions)
return temp
visited = [0 for i in range(len(l))]
Nbr_TV = [-1 for i in range(len(l))] # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
Distance_TV = [] # Heap of Distance of vertices from their neighboring vertex
Positions = []
for x in range(len(l)):
p = sys.maxsize
Distance_TV.append(p)
Positions.append(x)
nodePosition.append(x)
TreeEdges = []
visited[0] = 1
Distance_TV[0] = sys.maxsize
for x in l[0]:
Nbr_TV[x[0]] = 0
Distance_TV[x[0]] = x[1]
heapify(Distance_TV, Positions)
for i in range(1, len(l)):
vertex = deleteMinimum(Distance_TV, Positions)
if visited[vertex] == 0:
TreeEdges.append((Nbr_TV[vertex], vertex))
visited[vertex] = 1
for v in l[vertex]:
if visited[v[0]] == 0 and v[1] < Distance_TV[get_position(v[0])]:
Distance_TV[get_position(v[0])] = v[1]
bottom_to_top(v[1], get_position(v[0]), Distance_TV, Positions)
Nbr_TV[v[0]] = vertex
return TreeEdges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
n = int(input("Enter number of vertices: ").strip())
e = int(input("Enter number of edges: ").strip())
adjlist = defaultdict(list)
for x in range(e):
l = [int(x) for x in input().strip().split()] # noqa: E741
adjlist[l[0]].append([l[1], l[2]])
adjlist[l[1]].append([l[0], l[2]])
print(PrimsAlgorithm(adjlist))
| import sys
from collections import defaultdict
def PrimsAlgorithm(l): # noqa: E741
nodePosition = []
def get_position(vertex):
return nodePosition[vertex]
def set_position(vertex, pos):
nodePosition[vertex] = pos
def top_to_bottom(heap, start, size, positions):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
m = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
m = 2 * start + 1
else:
m = 2 * start + 2
if heap[m] < heap[start]:
temp, temp1 = heap[m], positions[m]
heap[m], positions[m] = heap[start], positions[start]
heap[start], positions[start] = temp, temp1
temp = get_position(positions[m])
set_position(positions[m], get_position(positions[start]))
set_position(positions[start], temp)
top_to_bottom(heap, m, size, positions)
# Update function if value of any node in min-heap decreases
def bottom_to_top(val, index, heap, position):
temp = position[index]
while index != 0:
if index % 2 == 0:
parent = int((index - 2) / 2)
else:
parent = int((index - 1) / 2)
if val < heap[parent]:
heap[index] = heap[parent]
position[index] = position[parent]
set_position(position[parent], index)
else:
heap[index] = val
position[index] = temp
set_position(temp, index)
break
index = parent
else:
heap[0] = val
position[0] = temp
set_position(temp, 0)
def heapify(heap, positions):
start = len(heap) // 2 - 1
for i in range(start, -1, -1):
top_to_bottom(heap, i, len(heap), positions)
def deleteMinimum(heap, positions):
temp = positions[0]
heap[0] = sys.maxsize
top_to_bottom(heap, 0, len(heap), positions)
return temp
visited = [0 for i in range(len(l))]
Nbr_TV = [-1 for i in range(len(l))] # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
Distance_TV = [] # Heap of Distance of vertices from their neighboring vertex
Positions = []
for x in range(len(l)):
p = sys.maxsize
Distance_TV.append(p)
Positions.append(x)
nodePosition.append(x)
TreeEdges = []
visited[0] = 1
Distance_TV[0] = sys.maxsize
for x in l[0]:
Nbr_TV[x[0]] = 0
Distance_TV[x[0]] = x[1]
heapify(Distance_TV, Positions)
for i in range(1, len(l)):
vertex = deleteMinimum(Distance_TV, Positions)
if visited[vertex] == 0:
TreeEdges.append((Nbr_TV[vertex], vertex))
visited[vertex] = 1
for v in l[vertex]:
if visited[v[0]] == 0 and v[1] < Distance_TV[get_position(v[0])]:
Distance_TV[get_position(v[0])] = v[1]
bottom_to_top(v[1], get_position(v[0]), Distance_TV, Positions)
Nbr_TV[v[0]] = vertex
return TreeEdges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
n = int(input("Enter number of vertices: ").strip())
e = int(input("Enter number of edges: ").strip())
adjlist = defaultdict(list)
for x in range(e):
l = [int(x) for x in input().strip().split()] # noqa: E741
adjlist[l[0]].append([l[1], l[2]])
adjlist[l[1]].append([l[0], l[2]])
print(PrimsAlgorithm(adjlist))
| -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 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
"""
from math import sqrt
def is_prime(num: int) -> bool:
"""
Determines whether the given number is prime or not
>>> is_prime(2)
True
>>> is_prime(15)
False
>>> is_prime(29)
True
>>> is_prime(0)
False
"""
if num == 2:
return True
elif num % 2 == 0:
return False
else:
sq = int(sqrt(num)) + 1
for i in range(3, sq, 2):
if num % i == 0:
return False
return True
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
"""
count = 0
number = 1
while count != nth and number < 3:
number += 1
if is_prime(number):
count += 1
while count != nth:
number += 2
if is_prime(number):
count += 1
return number
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
"""
from math import sqrt
def is_prime(num: int) -> bool:
"""
Determines whether the given number is prime or not
>>> is_prime(2)
True
>>> is_prime(15)
False
>>> is_prime(29)
True
>>> is_prime(0)
False
"""
if num == 2:
return True
elif num % 2 == 0:
return False
else:
sq = int(sqrt(num)) + 1
for i in range(3, sq, 2):
if num % i == 0:
return False
return True
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
"""
count = 0
number = 1
while count != nth and number < 3:
number += 1
if is_prime(number):
count += 1
while count != nth:
number += 2
if is_prime(number):
count += 1
return number
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}`.
| 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 | 4,314 | 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://en.wikipedia.org/wiki/Continuous_knapsack_problem
# https://www.guru99.com/fractional-knapsack-problem-greedy.html
# https://medium.com/walkinthecode/greedy-algorithm-fractional-knapsack-problem-9aba1daecc93
from __future__ import annotations
def fractional_knapsack(
value: list[int], weight: list[int], capacity: int
) -> tuple[int, list[int]]:
"""
>>> value = [1, 3, 5, 7, 9]
>>> weight = [0.9, 0.7, 0.5, 0.3, 0.1]
>>> fractional_knapsack(value, weight, 5)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack(value, weight, 15)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack(value, weight, 25)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack(value, weight, 26)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack(value, weight, -1)
(-90.0, [0, 0, 0, 0, -10.0])
>>> fractional_knapsack([1, 3, 5, 7], weight, 30)
(16, [1, 1, 1, 1])
>>> fractional_knapsack(value, [0.9, 0.7, 0.5, 0.3, 0.1], 30)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack([], [], 30)
(0, [])
"""
index = list(range(len(value)))
ratio = [v / w for v, w in zip(value, weight)]
index.sort(key=lambda i: ratio[i], reverse=True)
max_value = 0
fractions = [0] * len(value)
for i in index:
if weight[i] <= capacity:
fractions[i] = 1
max_value += value[i]
capacity -= weight[i]
else:
fractions[i] = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
n = int(input("Enter number of items: "))
value = input(f"Enter the values of the {n} item(s) in order: ").split()
value = [int(v) for v in value]
weight = input(f"Enter the positive weights of the {n} item(s) in order: ".split())
weight = [int(w) for w in weight]
capacity = int(input("Enter maximum weight: "))
max_value, fractions = fractional_knapsack(value, weight, capacity)
print("The maximum value of items that can be carried:", max_value)
print("The fractions in which the items should be taken:", fractions)
| # https://en.wikipedia.org/wiki/Continuous_knapsack_problem
# https://www.guru99.com/fractional-knapsack-problem-greedy.html
# https://medium.com/walkinthecode/greedy-algorithm-fractional-knapsack-problem-9aba1daecc93
from __future__ import annotations
def fractional_knapsack(
value: list[int], weight: list[int], capacity: int
) -> tuple[int, list[int]]:
"""
>>> value = [1, 3, 5, 7, 9]
>>> weight = [0.9, 0.7, 0.5, 0.3, 0.1]
>>> fractional_knapsack(value, weight, 5)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack(value, weight, 15)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack(value, weight, 25)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack(value, weight, 26)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack(value, weight, -1)
(-90.0, [0, 0, 0, 0, -10.0])
>>> fractional_knapsack([1, 3, 5, 7], weight, 30)
(16, [1, 1, 1, 1])
>>> fractional_knapsack(value, [0.9, 0.7, 0.5, 0.3, 0.1], 30)
(25, [1, 1, 1, 1, 1])
>>> fractional_knapsack([], [], 30)
(0, [])
"""
index = list(range(len(value)))
ratio = [v / w for v, w in zip(value, weight)]
index.sort(key=lambda i: ratio[i], reverse=True)
max_value = 0
fractions = [0] * len(value)
for i in index:
if weight[i] <= capacity:
fractions[i] = 1
max_value += value[i]
capacity -= weight[i]
else:
fractions[i] = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
n = int(input("Enter number of items: "))
value = input(f"Enter the values of the {n} item(s) in order: ").split()
value = [int(v) for v in value]
weight = input(f"Enter the positive weights of the {n} item(s) in order: ".split())
weight = [int(w) for w in weight]
capacity = int(input("Enter maximum weight: "))
max_value, fractions = fractional_knapsack(value, weight, capacity)
print("The maximum value of items that can be carried:", max_value)
print("The fractions in which the items should be taken:", fractions)
| -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}`.
| import math
from timeit import timeit
def num_digits(n: int) -> int:
"""
Find the number of digits in a number.
>>> num_digits(12345)
5
>>> num_digits(123)
3
>>> num_digits(0)
1
>>> num_digits(-1)
1
>>> num_digits(-123456)
6
"""
digits = 0
n = abs(n)
while True:
n = n // 10
digits += 1
if n == 0:
break
return digits
def num_digits_fast(n: int) -> int:
"""
Find the number of digits in a number.
abs() is used as logarithm for negative numbers is not defined.
>>> num_digits_fast(12345)
5
>>> num_digits_fast(123)
3
>>> num_digits_fast(0)
1
>>> num_digits_fast(-1)
1
>>> num_digits_fast(-123456)
6
"""
return 1 if n == 0 else math.floor(math.log(abs(n), 10) + 1)
def num_digits_faster(n: int) -> int:
"""
Find the number of digits in a number.
abs() is used for negative numbers
>>> num_digits_faster(12345)
5
>>> num_digits_faster(123)
3
>>> num_digits_faster(0)
1
>>> num_digits_faster(-1)
1
>>> num_digits_faster(-123456)
6
"""
return len(str(abs(n)))
def benchmark() -> None:
"""
Benchmark code for comparing 3 functions,
with 3 different length int values.
"""
print("\nFor small_num = ", small_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(small_num),
"\ttime =",
timeit("z.num_digits(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(small_num),
"\ttime =",
timeit("z.num_digits_fast(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(small_num),
"\ttime =",
timeit("z.num_digits_faster(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor medium_num = ", medium_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(medium_num),
"\ttime =",
timeit("z.num_digits(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(medium_num),
"\ttime =",
timeit("z.num_digits_fast(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(medium_num),
"\ttime =",
timeit("z.num_digits_faster(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor large_num = ", large_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(large_num),
"\ttime =",
timeit("z.num_digits(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(large_num),
"\ttime =",
timeit("z.num_digits_fast(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(large_num),
"\ttime =",
timeit("z.num_digits_faster(z.large_num)", setup="import __main__ as z"),
"seconds",
)
if __name__ == "__main__":
small_num = 262144
medium_num = 1125899906842624
large_num = 1267650600228229401496703205376
benchmark()
import doctest
doctest.testmod()
| import math
from timeit import timeit
def num_digits(n: int) -> int:
"""
Find the number of digits in a number.
>>> num_digits(12345)
5
>>> num_digits(123)
3
>>> num_digits(0)
1
>>> num_digits(-1)
1
>>> num_digits(-123456)
6
"""
digits = 0
n = abs(n)
while True:
n = n // 10
digits += 1
if n == 0:
break
return digits
def num_digits_fast(n: int) -> int:
"""
Find the number of digits in a number.
abs() is used as logarithm for negative numbers is not defined.
>>> num_digits_fast(12345)
5
>>> num_digits_fast(123)
3
>>> num_digits_fast(0)
1
>>> num_digits_fast(-1)
1
>>> num_digits_fast(-123456)
6
"""
return 1 if n == 0 else math.floor(math.log(abs(n), 10) + 1)
def num_digits_faster(n: int) -> int:
"""
Find the number of digits in a number.
abs() is used for negative numbers
>>> num_digits_faster(12345)
5
>>> num_digits_faster(123)
3
>>> num_digits_faster(0)
1
>>> num_digits_faster(-1)
1
>>> num_digits_faster(-123456)
6
"""
return len(str(abs(n)))
def benchmark() -> None:
"""
Benchmark code for comparing 3 functions,
with 3 different length int values.
"""
print("\nFor small_num = ", small_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(small_num),
"\ttime =",
timeit("z.num_digits(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(small_num),
"\ttime =",
timeit("z.num_digits_fast(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(small_num),
"\ttime =",
timeit("z.num_digits_faster(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor medium_num = ", medium_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(medium_num),
"\ttime =",
timeit("z.num_digits(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(medium_num),
"\ttime =",
timeit("z.num_digits_fast(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(medium_num),
"\ttime =",
timeit("z.num_digits_faster(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor large_num = ", large_num, ":")
print(
"> num_digits()",
"\t\tans =",
num_digits(large_num),
"\ttime =",
timeit("z.num_digits(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_fast()",
"\tans =",
num_digits_fast(large_num),
"\ttime =",
timeit("z.num_digits_fast(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> num_digits_faster()",
"\tans =",
num_digits_faster(large_num),
"\ttime =",
timeit("z.num_digits_faster(z.large_num)", setup="import __main__ as z"),
"seconds",
)
if __name__ == "__main__":
small_num = 262144
medium_num = 1125899906842624
large_num = 1267650600228229401496703205376
benchmark()
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 '(ciphers|conversions|data_structures|digital_image_processing|dynamic_programming|graphs|hashes|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 '(ciphers|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: ${{ success() }}
run: scripts/build_directory_md.py 2>&1 | tee DIRECTORY.md
| 1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| """
Adler-32 is a checksum algorithm which was invented by Mark Adler in 1995.
Compared to a cyclic redundancy check of the same length, it trades reliability for
speed (preferring the latter).
Adler-32 is more reliable than Fletcher-16, and slightly less reliable than
Fletcher-32.[2]
source: https://en.wikipedia.org/wiki/Adler-32
"""
def adler32(plain_text: str) -> str:
"""
Function implements adler-32 hash.
Itterates and evaluates new value for each character
>>> adler32('Algorithms')
363791387
>>> adler32('go adler em all')
708642122
"""
MOD_ADLER = 65521
a = 1
b = 0
for plain_chr in plain_text:
a = (a + ord(plain_chr)) % MOD_ADLER
b = (b + a) % MOD_ADLER
return (b << 16) | a
| """
Adler-32 is a checksum algorithm which was invented by Mark Adler in 1995.
Compared to a cyclic redundancy check of the same length, it trades reliability for
speed (preferring the latter).
Adler-32 is more reliable than Fletcher-16, and slightly less reliable than
Fletcher-32.[2]
source: https://en.wikipedia.org/wiki/Adler-32
"""
def adler32(plain_text: str) -> int:
"""
Function implements adler-32 hash.
Iterates and evaluates a new value for each character
>>> adler32('Algorithms')
363791387
>>> adler32('go adler em all')
708642122
"""
MOD_ADLER = 65521
a = 1
b = 0
for plain_chr in plain_text:
a = (a + ord(plain_chr)) % MOD_ADLER
b = (b + a) % MOD_ADLER
return (b << 16) | a
| 1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| """example of simple chaos machine"""
# Chaos Machine (K, t, m)
K = [0.33, 0.44, 0.55, 0.44, 0.33]
t = 3
m = 5
# Buffer Space (with Parameters Space)
buffer_space, params_space = [], []
# Machine Time
machine_time = 0
def push(seed):
global buffer_space, params_space, machine_time, K, m, t
# Choosing Dynamical Systems (All)
for key, value in enumerate(buffer_space):
# Evolution Parameter
e = float(seed / value)
# Control Theory: Orbit Change
value = (buffer_space[(key + 1) % m] + e) % 1
# Control Theory: Trajectory Change
r = (params_space[key] + e) % 1 + 3
# Modification (Transition Function) - Jumps
buffer_space[key] = round(float(r * value * (1 - value)), 10)
params_space[key] = r # Saving to Parameters Space
# Logistic Map
assert max(buffer_space) < 1
assert max(params_space) < 4
# Machine Time
machine_time += 1
def pull():
global buffer_space, params_space, machine_time, K, m, t
# PRNG (Xorshift by George Marsaglia)
def xorshift(X, Y):
X ^= Y >> 13
Y ^= X << 17
X ^= Y >> 5
return X
# Choosing Dynamical Systems (Increment)
key = machine_time % m
# Evolution (Time Length)
for i in range(0, t):
# Variables (Position + Parameters)
r = params_space[key]
value = buffer_space[key]
# Modification (Transition Function) - Flow
buffer_space[key] = round(float(r * value * (1 - value)), 10)
params_space[key] = (machine_time * 0.01 + r * 1.01) % 1 + 3
# Choosing Chaotic Data
X = int(buffer_space[(key + 2) % m] * (10 ** 10))
Y = int(buffer_space[(key - 2) % m] * (10 ** 10))
# Machine Time
machine_time += 1
return xorshift(X, Y) % 0xFFFFFFFF
def reset():
global buffer_space, params_space, machine_time, K, m, t
buffer_space = K
params_space = [0] * m
machine_time = 0
if __name__ == "__main__":
# Initialization
reset()
# Pushing Data (Input)
import random
message = random.sample(range(0xFFFFFFFF), 100)
for chunk in message:
push(chunk)
# for controlling
inp = ""
# Pulling Data (Output)
while inp in ("e", "E"):
print("%s" % format(pull(), "#04x"))
print(buffer_space)
print(params_space)
inp = input("(e)exit? ").strip()
| """example of simple chaos machine"""
# Chaos Machine (K, t, m)
K = [0.33, 0.44, 0.55, 0.44, 0.33]
t = 3
m = 5
# Buffer Space (with Parameters Space)
buffer_space: list[float] = []
params_space: list[float] = []
# Machine Time
machine_time = 0
def push(seed):
global buffer_space, params_space, machine_time, K, m, t
# Choosing Dynamical Systems (All)
for key, value in enumerate(buffer_space):
# Evolution Parameter
e = float(seed / value)
# Control Theory: Orbit Change
value = (buffer_space[(key + 1) % m] + e) % 1
# Control Theory: Trajectory Change
r = (params_space[key] + e) % 1 + 3
# Modification (Transition Function) - Jumps
buffer_space[key] = round(float(r * value * (1 - value)), 10)
params_space[key] = r # Saving to Parameters Space
# Logistic Map
assert max(buffer_space) < 1
assert max(params_space) < 4
# Machine Time
machine_time += 1
def pull():
global buffer_space, params_space, machine_time, K, m, t
# PRNG (Xorshift by George Marsaglia)
def xorshift(X, Y):
X ^= Y >> 13
Y ^= X << 17
X ^= Y >> 5
return X
# Choosing Dynamical Systems (Increment)
key = machine_time % m
# Evolution (Time Length)
for i in range(0, t):
# Variables (Position + Parameters)
r = params_space[key]
value = buffer_space[key]
# Modification (Transition Function) - Flow
buffer_space[key] = round(float(r * value * (1 - value)), 10)
params_space[key] = (machine_time * 0.01 + r * 1.01) % 1 + 3
# Choosing Chaotic Data
X = int(buffer_space[(key + 2) % m] * (10 ** 10))
Y = int(buffer_space[(key - 2) % m] * (10 ** 10))
# Machine Time
machine_time += 1
return xorshift(X, Y) % 0xFFFFFFFF
def reset():
global buffer_space, params_space, machine_time, K, m, t
buffer_space = K
params_space = [0] * m
machine_time = 0
if __name__ == "__main__":
# Initialization
reset()
# Pushing Data (Input)
import random
message = random.sample(range(0xFFFFFFFF), 100)
for chunk in message:
push(chunk)
# for controlling
inp = ""
# Pulling Data (Output)
while inp in ("e", "E"):
print("%s" % format(pull(), "#04x"))
print(buffer_space)
print(params_space)
inp = input("(e)exit? ").strip()
| 1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| alphabets = [chr(i) for i in range(32, 126)]
gear_one = [i for i in range(len(alphabets))]
gear_two = [i for i in range(len(alphabets))]
gear_three = [i for i in range(len(alphabets))]
reflector = [i for i in reversed(range(len(alphabets)))]
code = []
gear_one_pos = gear_two_pos = gear_three_pos = 0
def rotator():
global gear_one_pos
global gear_two_pos
global gear_three_pos
i = gear_one[0]
gear_one.append(i)
del gear_one[0]
gear_one_pos += 1
if gear_one_pos % int(len(alphabets)) == 0:
i = gear_two[0]
gear_two.append(i)
del gear_two[0]
gear_two_pos += 1
if gear_two_pos % int(len(alphabets)) == 0:
i = gear_three[0]
gear_three.append(i)
del gear_three[0]
gear_three_pos += 1
def engine(input_character):
target = alphabets.index(input_character)
target = gear_one[target]
target = gear_two[target]
target = gear_three[target]
target = reflector[target]
target = gear_three.index(target)
target = gear_two.index(target)
target = gear_one.index(target)
code.append(alphabets[target])
rotator()
if __name__ == "__main__":
decode = input("Type your message:\n")
decode = list(decode)
while True:
try:
token = int(input("Please set token:(must be only digits)\n"))
break
except Exception as error:
print(error)
for i in range(token):
rotator()
for i in decode:
engine(i)
print("\n" + "".join(code))
print(
f"\nYour Token is {token} please write it down.\nIf you want to decode "
f"this message again you should input same digits as token!"
)
| alphabets = [chr(i) for i in range(32, 126)]
gear_one = [i for i in range(len(alphabets))]
gear_two = [i for i in range(len(alphabets))]
gear_three = [i for i in range(len(alphabets))]
reflector = [i for i in reversed(range(len(alphabets)))]
code = []
gear_one_pos = gear_two_pos = gear_three_pos = 0
def rotator():
global gear_one_pos
global gear_two_pos
global gear_three_pos
i = gear_one[0]
gear_one.append(i)
del gear_one[0]
gear_one_pos += 1
if gear_one_pos % int(len(alphabets)) == 0:
i = gear_two[0]
gear_two.append(i)
del gear_two[0]
gear_two_pos += 1
if gear_two_pos % int(len(alphabets)) == 0:
i = gear_three[0]
gear_three.append(i)
del gear_three[0]
gear_three_pos += 1
def engine(input_character):
target = alphabets.index(input_character)
target = gear_one[target]
target = gear_two[target]
target = gear_three[target]
target = reflector[target]
target = gear_three.index(target)
target = gear_two.index(target)
target = gear_one.index(target)
code.append(alphabets[target])
rotator()
if __name__ == "__main__":
decode = list(input("Type your message:\n"))
while True:
try:
token = int(input("Please set token:(must be only digits)\n"))
break
except Exception as error:
print(error)
for i in range(token):
rotator()
for j in decode:
engine(j)
print("\n" + "".join(code))
print(
f"\nYour Token is {token} please write it down.\nIf you want to decode "
f"this message again you should input same digits as token!"
)
| 1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 was created for sdbm (a public-domain reimplementation of ndbm)
database library.
It was found to do well in scrambling bits, causing better distribution of the keys
and fewer splits.
It also happens to be a good general hashing function with good distribution.
The actual function (pseudo code) is:
for i in i..len(str):
hash(i) = hash(i - 1) * 65599 + str[i];
What is included below is the faster version used in gawk. [there is even a faster,
duff-device version]
The magic constant 65599 was picked out of thin air while experimenting with
different constants.
It turns out to be a prime.
This is one of the algorithms used in berkeley db (see sleepycat) and elsewhere.
source: http://www.cse.yorku.ca/~oz/hash.html
"""
def sdbm(plain_text: str) -> str:
"""
Function implements sdbm hash, easy to use, great for bits scrambling.
iterates over each character in the given string and applies function to each of
them.
>>> sdbm('Algorithms')
1462174910723540325254304520539387479031000036
>>> sdbm('scramble bits')
730247649148944819640658295400555317318720608290373040936089
"""
hash = 0
for plain_chr in plain_text:
hash = ord(plain_chr) + (hash << 6) + (hash << 16) - hash
return hash
| """
This algorithm was created for sdbm (a public-domain reimplementation of ndbm)
database library.
It was found to do well in scrambling bits, causing better distribution of the keys
and fewer splits.
It also happens to be a good general hashing function with good distribution.
The actual function (pseudo code) is:
for i in i..len(str):
hash(i) = hash(i - 1) * 65599 + str[i];
What is included below is the faster version used in gawk. [there is even a faster,
duff-device version]
The magic constant 65599 was picked out of thin air while experimenting with
different constants.
It turns out to be a prime.
This is one of the algorithms used in berkeley db (see sleepycat) and elsewhere.
source: http://www.cse.yorku.ca/~oz/hash.html
"""
def sdbm(plain_text: str) -> int:
"""
Function implements sdbm hash, easy to use, great for bits scrambling.
iterates over each character in the given string and applies function to each of
them.
>>> sdbm('Algorithms')
1462174910723540325254304520539387479031000036
>>> sdbm('scramble bits')
730247649148944819640658295400555317318720608290373040936089
"""
hash = 0
for plain_chr in plain_text:
hash = ord(plain_chr) + (hash << 6) + (hash << 16) - hash
return hash
| 1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| def reverse_words(input_str: str) -> str:
"""
Reverses words in a given string
>>> reverse_words("I love Python")
'Python love I'
>>> reverse_words("I Love Python")
'Python Love I'
"""
return " ".join(input_str.split()[::-1])
if __name__ == "__main__":
import doctest
doctest.testmod()
| def reverse_words(input_str: str) -> str:
"""
Reverses words in a given string
>>> reverse_words("I love Python")
'Python love I'
>>> reverse_words("I Love Python")
'Python Love I'
"""
return " ".join(input_str.split()[::-1])
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| def perfect_cube(n: int) -> bool:
"""
Check if a number is a perfect cube or not.
>>> perfect_cube(27)
True
>>> perfect_cube(4)
False
"""
val = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| def perfect_cube(n: int) -> bool:
"""
Check if a number is a perfect cube or not.
>>> perfect_cube(27)
True
>>> perfect_cube(4)
False
"""
val = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| hex_table = {hex(i)[2:]: i for i in range(16)} # Use [:2] to strip off the leading '0x'
def hex_to_decimal(hex_string: str) -> int:
"""
Convert a hexadecimal value to its decimal equivalent
#https://www.programiz.com/python-programming/methods/built-in/hex
>>> hex_to_decimal("a")
10
>>> hex_to_decimal("12f")
303
>>> hex_to_decimal(" 12f ")
303
>>> hex_to_decimal("FfFf")
65535
>>> hex_to_decimal("-Ff")
-255
>>> hex_to_decimal("F-f")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
>>> hex_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> hex_to_decimal("12m")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
"""
hex_string = hex_string.strip().lower()
if not hex_string:
raise ValueError("Empty string was passed to the function")
is_negative = hex_string[0] == "-"
if is_negative:
hex_string = hex_string[1:]
if not all(char in hex_table for char in hex_string):
raise ValueError("Non-hexadecimal value was passed to the function")
decimal_number = 0
for char in hex_string:
decimal_number = 16 * decimal_number + hex_table[char]
return -decimal_number if is_negative else decimal_number
if __name__ == "__main__":
from doctest import testmod
testmod()
| hex_table = {hex(i)[2:]: i for i in range(16)} # Use [:2] to strip off the leading '0x'
def hex_to_decimal(hex_string: str) -> int:
"""
Convert a hexadecimal value to its decimal equivalent
#https://www.programiz.com/python-programming/methods/built-in/hex
>>> hex_to_decimal("a")
10
>>> hex_to_decimal("12f")
303
>>> hex_to_decimal(" 12f ")
303
>>> hex_to_decimal("FfFf")
65535
>>> hex_to_decimal("-Ff")
-255
>>> hex_to_decimal("F-f")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
>>> hex_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> hex_to_decimal("12m")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
"""
hex_string = hex_string.strip().lower()
if not hex_string:
raise ValueError("Empty string was passed to the function")
is_negative = hex_string[0] == "-"
if is_negative:
hex_string = hex_string[1:]
if not all(char in hex_table for char in hex_string):
raise ValueError("Non-hexadecimal value was passed to the function")
decimal_number = 0
for char in hex_string:
decimal_number = 16 * decimal_number + hex_table[char]
return -decimal_number if is_negative else decimal_number
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def main():
message = input("Enter message: ")
key = input("Enter key [alphanumeric]: ")
mode = input("Encrypt/Decrypt [e/d]: ")
if mode.lower().startswith("e"):
mode = "encrypt"
translated = encryptMessage(key, message)
elif mode.lower().startswith("d"):
mode = "decrypt"
translated = decryptMessage(key, message)
print("\n%sed message:" % mode.title())
print(translated)
def encryptMessage(key: str, message: str) -> str:
"""
>>> encryptMessage('HDarji', 'This is Harshil Darji from Dharmaj.')
'Akij ra Odrjqqs Gaisq muod Mphumrs.'
"""
return translateMessage(key, message, "encrypt")
def decryptMessage(key: str, message: str) -> str:
"""
>>> decryptMessage('HDarji', 'Akij ra Odrjqqs Gaisq muod Mphumrs.')
'This is Harshil Darji from Dharmaj.'
"""
return translateMessage(key, message, "decrypt")
def translateMessage(key: str, message: str, mode: str) -> str:
translated = []
keyIndex = 0
key = key.upper()
for symbol in message:
num = LETTERS.find(symbol.upper())
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[keyIndex])
elif mode == "decrypt":
num -= LETTERS.find(key[keyIndex])
num %= len(LETTERS)
if symbol.isupper():
translated.append(LETTERS[num])
elif symbol.islower():
translated.append(LETTERS[num].lower())
keyIndex += 1
if keyIndex == len(key):
keyIndex = 0
else:
translated.append(symbol)
return "".join(translated)
if __name__ == "__main__":
main()
| LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def main():
message = input("Enter message: ")
key = input("Enter key [alphanumeric]: ")
mode = input("Encrypt/Decrypt [e/d]: ")
if mode.lower().startswith("e"):
mode = "encrypt"
translated = encryptMessage(key, message)
elif mode.lower().startswith("d"):
mode = "decrypt"
translated = decryptMessage(key, message)
print("\n%sed message:" % mode.title())
print(translated)
def encryptMessage(key: str, message: str) -> str:
"""
>>> encryptMessage('HDarji', 'This is Harshil Darji from Dharmaj.')
'Akij ra Odrjqqs Gaisq muod Mphumrs.'
"""
return translateMessage(key, message, "encrypt")
def decryptMessage(key: str, message: str) -> str:
"""
>>> decryptMessage('HDarji', 'Akij ra Odrjqqs Gaisq muod Mphumrs.')
'This is Harshil Darji from Dharmaj.'
"""
return translateMessage(key, message, "decrypt")
def translateMessage(key: str, message: str, mode: str) -> str:
translated = []
keyIndex = 0
key = key.upper()
for symbol in message:
num = LETTERS.find(symbol.upper())
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[keyIndex])
elif mode == "decrypt":
num -= LETTERS.find(key[keyIndex])
num %= len(LETTERS)
if symbol.isupper():
translated.append(LETTERS[num])
elif symbol.islower():
translated.append(LETTERS[num].lower())
keyIndex += 1
if keyIndex == len(key):
keyIndex = 0
else:
translated.append(symbol)
return "".join(translated)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 on Fri Oct 16 09:31:07 2020
@author: Dr. Tobias Schröder
@license: MIT-license
This file contains the test-suite for the knapsack problem.
"""
import unittest
from knapsack import knapsack as k
class Test(unittest.TestCase):
def test_base_case(self):
"""
test for the base case
"""
cap = 0
val = [0]
w = [0]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 0)
val = [60]
w = [10]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 0)
def test_easy_case(self):
"""
test for the base case
"""
cap = 3
val = [1, 2, 3]
w = [3, 2, 1]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 5)
def test_knapsack(self):
"""
test for the knapsack
"""
cap = 50
val = [60, 100, 120]
w = [10, 20, 30]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 220)
if __name__ == "__main__":
unittest.main()
| """
Created on Fri Oct 16 09:31:07 2020
@author: Dr. Tobias Schröder
@license: MIT-license
This file contains the test-suite for the knapsack problem.
"""
import unittest
from knapsack import knapsack as k
class Test(unittest.TestCase):
def test_base_case(self):
"""
test for the base case
"""
cap = 0
val = [0]
w = [0]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 0)
val = [60]
w = [10]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 0)
def test_easy_case(self):
"""
test for the base case
"""
cap = 3
val = [1, 2, 3]
w = [3, 2, 1]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 5)
def test_knapsack(self):
"""
test for the knapsack
"""
cap = 50
val = [60, 100, 120]
w = [10, 20, 30]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 220)
if __name__ == "__main__":
unittest.main()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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
"""Provide the functionality to manipulate a single bit."""
def set_bit(number: int, position: int) -> int:
"""
Set the bit at position to 1.
Details: perform bitwise or for given number and X.
Where X is a number with all the bits – zeroes and bit on given
position – one.
>>> set_bit(0b1101, 1) # 0b1111
15
>>> set_bit(0b0, 5) # 0b100000
32
>>> set_bit(0b1111, 1) # 0b1111
15
"""
return number | (1 << position)
def clear_bit(number: int, position: int) -> int:
"""
Set the bit at position to 0.
Details: perform bitwise and for given number and X.
Where X is a number with all the bits – ones and bit on given
position – zero.
>>> clear_bit(0b10010, 1) # 0b10000
16
>>> clear_bit(0b0, 5) # 0b0
0
"""
return number & ~(1 << position)
def flip_bit(number: int, position: int) -> int:
"""
Flip the bit at position.
Details: perform bitwise xor for given number and X.
Where X is a number with all the bits – zeroes and bit on given
position – one.
>>> flip_bit(0b101, 1) # 0b111
7
>>> flip_bit(0b101, 0) # 0b100
4
"""
return number ^ (1 << position)
def is_bit_set(number: int, position: int) -> bool:
"""
Is the bit at position set?
Details: Shift the bit at position to be the first (smallest) bit.
Then check if the first bit is set by anding the shifted number with 1.
>>> is_bit_set(0b1010, 0)
False
>>> is_bit_set(0b1010, 1)
True
>>> is_bit_set(0b1010, 2)
False
>>> is_bit_set(0b1010, 3)
True
>>> is_bit_set(0b0, 17)
False
"""
return ((number >> position) & 1) == 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| #!/usr/bin/env python3
"""Provide the functionality to manipulate a single bit."""
def set_bit(number: int, position: int) -> int:
"""
Set the bit at position to 1.
Details: perform bitwise or for given number and X.
Where X is a number with all the bits – zeroes and bit on given
position – one.
>>> set_bit(0b1101, 1) # 0b1111
15
>>> set_bit(0b0, 5) # 0b100000
32
>>> set_bit(0b1111, 1) # 0b1111
15
"""
return number | (1 << position)
def clear_bit(number: int, position: int) -> int:
"""
Set the bit at position to 0.
Details: perform bitwise and for given number and X.
Where X is a number with all the bits – ones and bit on given
position – zero.
>>> clear_bit(0b10010, 1) # 0b10000
16
>>> clear_bit(0b0, 5) # 0b0
0
"""
return number & ~(1 << position)
def flip_bit(number: int, position: int) -> int:
"""
Flip the bit at position.
Details: perform bitwise xor for given number and X.
Where X is a number with all the bits – zeroes and bit on given
position – one.
>>> flip_bit(0b101, 1) # 0b111
7
>>> flip_bit(0b101, 0) # 0b100
4
"""
return number ^ (1 << position)
def is_bit_set(number: int, position: int) -> bool:
"""
Is the bit at position set?
Details: Shift the bit at position to be the first (smallest) bit.
Then check if the first bit is set by anding the shifted number with 1.
>>> is_bit_set(0b1010, 0)
False
>>> is_bit_set(0b1010, 1)
True
>>> is_bit_set(0b1010, 2)
False
>>> is_bit_set(0b1010, 3)
True
>>> is_bit_set(0b0, 17)
False
"""
return ((number >> position) & 1) == 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 9: https://projecteuler.net/problem=9
Special Pythagorean triplet
A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,
a^2 + b^2 = c^2
For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.
There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product a*b*c.
References:
- https://en.wikipedia.org/wiki/Pythagorean_triple
"""
def solution() -> int:
"""
Returns the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = 1000
# The code below has been commented due to slow execution affecting Travis.
# >>> solution()
# 31875000
"""
for a in range(300):
for b in range(400):
for c in range(500):
if a < b < c:
if (a ** 2) + (b ** 2) == (c ** 2):
if (a + b + c) == 1000:
return a * b * c
def solution_fast() -> int:
"""
Returns the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = 1000
# The code below has been commented due to slow execution affecting Travis.
# >>> solution_fast()
# 31875000
"""
for a in range(300):
for b in range(400):
c = 1000 - a - b
if a < b < c and (a ** 2) + (b ** 2) == (c ** 2):
return a * b * c
def benchmark() -> None:
"""
Benchmark code comparing two different version function.
"""
import timeit
print(
timeit.timeit("solution()", setup="from __main__ import solution", number=1000)
)
print(
timeit.timeit(
"solution_fast()", setup="from __main__ import solution_fast", number=1000
)
)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 9: https://projecteuler.net/problem=9
Special Pythagorean triplet
A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,
a^2 + b^2 = c^2
For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.
There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product a*b*c.
References:
- https://en.wikipedia.org/wiki/Pythagorean_triple
"""
def solution() -> int:
"""
Returns the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = 1000
# The code below has been commented due to slow execution affecting Travis.
# >>> solution()
# 31875000
"""
for a in range(300):
for b in range(400):
for c in range(500):
if a < b < c:
if (a ** 2) + (b ** 2) == (c ** 2):
if (a + b + c) == 1000:
return a * b * c
def solution_fast() -> int:
"""
Returns the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = 1000
# The code below has been commented due to slow execution affecting Travis.
# >>> solution_fast()
# 31875000
"""
for a in range(300):
for b in range(400):
c = 1000 - a - b
if a < b < c and (a ** 2) + (b ** 2) == (c ** 2):
return a * b * c
def benchmark() -> None:
"""
Benchmark code comparing two different version function.
"""
import timeit
print(
timeit.timeit("solution()", setup="from __main__ import solution", number=1000)
)
print(
timeit.timeit(
"solution_fast()", setup="from __main__ import solution_fast", number=1000
)
)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| """
Testing here assumes that numpy and linalg is ALWAYS correct!!!!
If running from PyCharm you can place the following line in "Additional Arguments" for
the pytest run configuration
-vv -m mat_ops -p no:cacheprovider
"""
import logging
# standard libraries
import sys
import numpy as np
import pytest # type: ignore
# Custom/local libraries
from matrix import matrix_operation as matop
mat_a = [[12, 10], [3, 9]]
mat_b = [[3, 4], [7, 4]]
mat_c = [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
mat_d = [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
mat_e = [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
mat_f = [1]
mat_h = [2]
logger = logging.getLogger()
logger.level = logging.DEBUG
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@pytest.mark.mat_ops
@pytest.mark.parametrize(
("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]
)
def test_addition(mat1, mat2):
if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2):
with pytest.raises(TypeError):
logger.info(f"\n\t{test_addition.__name__} returned integer")
matop.add(mat1, mat2)
elif (np.array(mat1)).shape == (np.array(mat2)).shape:
logger.info(f"\n\t{test_addition.__name__} with same matrix dims")
act = (np.array(mat1) + np.array(mat2)).tolist()
theo = matop.add(mat1, mat2)
assert theo == act
else:
with pytest.raises(ValueError):
logger.info(f"\n\t{test_addition.__name__} with different matrix dims")
matop.add(mat1, mat2)
@pytest.mark.mat_ops
@pytest.mark.parametrize(
("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]
)
def test_subtraction(mat1, mat2):
if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2):
with pytest.raises(TypeError):
logger.info(f"\n\t{test_subtraction.__name__} returned integer")
matop.subtract(mat1, mat2)
elif (np.array(mat1)).shape == (np.array(mat2)).shape:
logger.info(f"\n\t{test_subtraction.__name__} with same matrix dims")
act = (np.array(mat1) - np.array(mat2)).tolist()
theo = matop.subtract(mat1, mat2)
assert theo == act
else:
with pytest.raises(ValueError):
logger.info(f"\n\t{test_subtraction.__name__} with different matrix dims")
assert matop.subtract(mat1, mat2)
@pytest.mark.mat_ops
@pytest.mark.parametrize(
("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]
)
def test_multiplication(mat1, mat2):
if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2):
logger.info(f"\n\t{test_multiplication.__name__} returned integer")
with pytest.raises(TypeError):
matop.add(mat1, mat2)
elif (np.array(mat1)).shape == (np.array(mat2)).shape:
logger.info(f"\n\t{test_multiplication.__name__} meets dim requirements")
act = (np.matmul(mat1, mat2)).tolist()
theo = matop.multiply(mat1, mat2)
assert theo == act
else:
with pytest.raises(ValueError):
logger.info(
f"\n\t{test_multiplication.__name__} does not meet dim requirements"
)
assert matop.subtract(mat1, mat2)
@pytest.mark.mat_ops
def test_scalar_multiply():
act = (3.5 * np.array(mat_a)).tolist()
theo = matop.scalar_multiply(mat_a, 3.5)
assert theo == act
@pytest.mark.mat_ops
def test_identity():
act = (np.identity(5)).tolist()
theo = matop.identity(5)
assert theo == act
@pytest.mark.mat_ops
@pytest.mark.parametrize("mat", [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f])
def test_transpose(mat):
if (np.array(mat)).shape < (2, 2):
with pytest.raises(TypeError):
logger.info(f"\n\t{test_transpose.__name__} returned integer")
matop.transpose(mat)
else:
act = (np.transpose(mat)).tolist()
theo = matop.transpose(mat, return_map=False)
assert theo == act
| """
Testing here assumes that numpy and linalg is ALWAYS correct!!!!
If running from PyCharm you can place the following line in "Additional Arguments" for
the pytest run configuration
-vv -m mat_ops -p no:cacheprovider
"""
import logging
# standard libraries
import sys
import numpy as np
import pytest # type: ignore
# Custom/local libraries
from matrix import matrix_operation as matop
mat_a = [[12, 10], [3, 9]]
mat_b = [[3, 4], [7, 4]]
mat_c = [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
mat_d = [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
mat_e = [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
mat_f = [1]
mat_h = [2]
logger = logging.getLogger()
logger.level = logging.DEBUG
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@pytest.mark.mat_ops
@pytest.mark.parametrize(
("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]
)
def test_addition(mat1, mat2):
if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2):
with pytest.raises(TypeError):
logger.info(f"\n\t{test_addition.__name__} returned integer")
matop.add(mat1, mat2)
elif (np.array(mat1)).shape == (np.array(mat2)).shape:
logger.info(f"\n\t{test_addition.__name__} with same matrix dims")
act = (np.array(mat1) + np.array(mat2)).tolist()
theo = matop.add(mat1, mat2)
assert theo == act
else:
with pytest.raises(ValueError):
logger.info(f"\n\t{test_addition.__name__} with different matrix dims")
matop.add(mat1, mat2)
@pytest.mark.mat_ops
@pytest.mark.parametrize(
("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]
)
def test_subtraction(mat1, mat2):
if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2):
with pytest.raises(TypeError):
logger.info(f"\n\t{test_subtraction.__name__} returned integer")
matop.subtract(mat1, mat2)
elif (np.array(mat1)).shape == (np.array(mat2)).shape:
logger.info(f"\n\t{test_subtraction.__name__} with same matrix dims")
act = (np.array(mat1) - np.array(mat2)).tolist()
theo = matop.subtract(mat1, mat2)
assert theo == act
else:
with pytest.raises(ValueError):
logger.info(f"\n\t{test_subtraction.__name__} with different matrix dims")
assert matop.subtract(mat1, mat2)
@pytest.mark.mat_ops
@pytest.mark.parametrize(
("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]
)
def test_multiplication(mat1, mat2):
if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2):
logger.info(f"\n\t{test_multiplication.__name__} returned integer")
with pytest.raises(TypeError):
matop.add(mat1, mat2)
elif (np.array(mat1)).shape == (np.array(mat2)).shape:
logger.info(f"\n\t{test_multiplication.__name__} meets dim requirements")
act = (np.matmul(mat1, mat2)).tolist()
theo = matop.multiply(mat1, mat2)
assert theo == act
else:
with pytest.raises(ValueError):
logger.info(
f"\n\t{test_multiplication.__name__} does not meet dim requirements"
)
assert matop.subtract(mat1, mat2)
@pytest.mark.mat_ops
def test_scalar_multiply():
act = (3.5 * np.array(mat_a)).tolist()
theo = matop.scalar_multiply(mat_a, 3.5)
assert theo == act
@pytest.mark.mat_ops
def test_identity():
act = (np.identity(5)).tolist()
theo = matop.identity(5)
assert theo == act
@pytest.mark.mat_ops
@pytest.mark.parametrize("mat", [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f])
def test_transpose(mat):
if (np.array(mat)).shape < (2, 2):
with pytest.raises(TypeError):
logger.info(f"\n\t{test_transpose.__name__} returned integer")
matop.transpose(mat)
else:
act = (np.transpose(mat)).tolist()
theo = matop.transpose(mat, return_map=False)
assert theo == act
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| """
Description
The Koch snowflake is a fractal curve and one of the earliest fractals to
have been described. The Koch snowflake can be built up iteratively, in a
sequence of stages. The first stage is an equilateral triangle, and each
successive stage is formed by adding outward bends to each side of the
previous stage, making smaller equilateral triangles.
This can be achieved through the following steps for each line:
1. divide the line segment into three segments of equal length.
2. draw an equilateral triangle that has the middle segment from step 1
as its base and points outward.
3. remove the line segment that is the base of the triangle from step 2.
(description adapted from https://en.wikipedia.org/wiki/Koch_snowflake )
(for a more detailed explanation and an implementation in the
Processing language, see https://natureofcode.com/book/chapter-8-fractals/
#84-the-koch-curve-and-the-arraylist-technique )
Requirements (pip):
- matplotlib
- numpy
"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
VECTOR_1 = numpy.array([0, 0])
VECTOR_2 = numpy.array([0.5, 0.8660254])
VECTOR_3 = numpy.array([1, 0])
INITIAL_VECTORS = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
# uncomment for simple Koch curve instead of Koch snowflake
# INITIAL_VECTORS = [VECTOR_1, VECTOR_3]
def iterate(initial_vectors: list[numpy.ndarray], steps: int) -> list[numpy.ndarray]:
"""
Go through the number of iterations determined by the argument "steps".
Be careful with high values (above 5) since the time to calculate increases
exponentially.
>>> iterate([numpy.array([0, 0]), numpy.array([1, 0])], 1)
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
vectors = initial_vectors
for i in range(steps):
vectors = iteration_step(vectors)
return vectors
def iteration_step(vectors: list[numpy.ndarray]) -> list[numpy.ndarray]:
"""
Loops through each pair of adjacent vectors. Each line between two adjacent
vectors is divided into 4 segments by adding 3 additional vectors in-between
the original two vectors. The vector in the middle is constructed through a
60 degree rotation so it is bent outwards.
>>> iteration_step([numpy.array([0, 0]), numpy.array([1, 0])])
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
new_vectors = []
for i, start_vector in enumerate(vectors[:-1]):
end_vector = vectors[i + 1]
new_vectors.append(start_vector)
difference_vector = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3)
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3, 60)
)
new_vectors.append(start_vector + difference_vector * 2 / 3)
new_vectors.append(vectors[-1])
return new_vectors
def rotate(vector: numpy.ndarray, angle_in_degrees: float) -> numpy.ndarray:
"""
Standard rotation of a 2D vector with a rotation matrix
(see https://en.wikipedia.org/wiki/Rotation_matrix )
>>> rotate(numpy.array([1, 0]), 60)
array([0.5 , 0.8660254])
>>> rotate(numpy.array([1, 0]), 90)
array([6.123234e-17, 1.000000e+00])
"""
theta = numpy.radians(angle_in_degrees)
c, s = numpy.cos(theta), numpy.sin(theta)
rotation_matrix = numpy.array(((c, -s), (s, c)))
return numpy.dot(rotation_matrix, vector)
def plot(vectors: list[numpy.ndarray]) -> None:
"""
Utility function to plot the vectors using matplotlib.pyplot
No doctest was implemented since this function does not have a return value
"""
# avoid stretched display of graph
axes = plt.gca()
axes.set_aspect("equal")
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
x_coordinates, y_coordinates = zip(*vectors)
plt.plot(x_coordinates, y_coordinates)
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
processed_vectors = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| """
Description
The Koch snowflake is a fractal curve and one of the earliest fractals to
have been described. The Koch snowflake can be built up iteratively, in a
sequence of stages. The first stage is an equilateral triangle, and each
successive stage is formed by adding outward bends to each side of the
previous stage, making smaller equilateral triangles.
This can be achieved through the following steps for each line:
1. divide the line segment into three segments of equal length.
2. draw an equilateral triangle that has the middle segment from step 1
as its base and points outward.
3. remove the line segment that is the base of the triangle from step 2.
(description adapted from https://en.wikipedia.org/wiki/Koch_snowflake )
(for a more detailed explanation and an implementation in the
Processing language, see https://natureofcode.com/book/chapter-8-fractals/
#84-the-koch-curve-and-the-arraylist-technique )
Requirements (pip):
- matplotlib
- numpy
"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
VECTOR_1 = numpy.array([0, 0])
VECTOR_2 = numpy.array([0.5, 0.8660254])
VECTOR_3 = numpy.array([1, 0])
INITIAL_VECTORS = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
# uncomment for simple Koch curve instead of Koch snowflake
# INITIAL_VECTORS = [VECTOR_1, VECTOR_3]
def iterate(initial_vectors: list[numpy.ndarray], steps: int) -> list[numpy.ndarray]:
"""
Go through the number of iterations determined by the argument "steps".
Be careful with high values (above 5) since the time to calculate increases
exponentially.
>>> iterate([numpy.array([0, 0]), numpy.array([1, 0])], 1)
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
vectors = initial_vectors
for i in range(steps):
vectors = iteration_step(vectors)
return vectors
def iteration_step(vectors: list[numpy.ndarray]) -> list[numpy.ndarray]:
"""
Loops through each pair of adjacent vectors. Each line between two adjacent
vectors is divided into 4 segments by adding 3 additional vectors in-between
the original two vectors. The vector in the middle is constructed through a
60 degree rotation so it is bent outwards.
>>> iteration_step([numpy.array([0, 0]), numpy.array([1, 0])])
[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
"""
new_vectors = []
for i, start_vector in enumerate(vectors[:-1]):
end_vector = vectors[i + 1]
new_vectors.append(start_vector)
difference_vector = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3)
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3, 60)
)
new_vectors.append(start_vector + difference_vector * 2 / 3)
new_vectors.append(vectors[-1])
return new_vectors
def rotate(vector: numpy.ndarray, angle_in_degrees: float) -> numpy.ndarray:
"""
Standard rotation of a 2D vector with a rotation matrix
(see https://en.wikipedia.org/wiki/Rotation_matrix )
>>> rotate(numpy.array([1, 0]), 60)
array([0.5 , 0.8660254])
>>> rotate(numpy.array([1, 0]), 90)
array([6.123234e-17, 1.000000e+00])
"""
theta = numpy.radians(angle_in_degrees)
c, s = numpy.cos(theta), numpy.sin(theta)
rotation_matrix = numpy.array(((c, -s), (s, c)))
return numpy.dot(rotation_matrix, vector)
def plot(vectors: list[numpy.ndarray]) -> None:
"""
Utility function to plot the vectors using matplotlib.pyplot
No doctest was implemented since this function does not have a return value
"""
# avoid stretched display of graph
axes = plt.gca()
axes.set_aspect("equal")
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
x_coordinates, y_coordinates = zip(*vectors)
plt.plot(x_coordinates, y_coordinates)
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
processed_vectors = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 Basic Math in Python."""
import math
def prime_factors(n: int) -> list:
"""Find Prime Factors.
>>> prime_factors(100)
[2, 2, 5, 5]
"""
pf = []
while n % 2 == 0:
pf.append(2)
n = int(n / 2)
for i in range(3, int(math.sqrt(n)) + 1, 2):
while n % i == 0:
pf.append(i)
n = int(n / i)
if n > 2:
pf.append(n)
return pf
def number_of_divisors(n: int) -> int:
"""Calculate Number of Divisors of an Integer.
>>> number_of_divisors(100)
9
"""
div = 1
temp = 1
while n % 2 == 0:
temp += 1
n = int(n / 2)
div *= temp
for i in range(3, int(math.sqrt(n)) + 1, 2):
temp = 1
while n % i == 0:
temp += 1
n = int(n / i)
div *= temp
return div
def sum_of_divisors(n: int) -> int:
"""Calculate Sum of Divisors.
>>> sum_of_divisors(100)
217
"""
s = 1
temp = 1
while n % 2 == 0:
temp += 1
n = int(n / 2)
if temp > 1:
s *= (2 ** temp - 1) / (2 - 1)
for i in range(3, int(math.sqrt(n)) + 1, 2):
temp = 1
while n % i == 0:
temp += 1
n = int(n / i)
if temp > 1:
s *= (i ** temp - 1) / (i - 1)
return int(s)
def euler_phi(n: int) -> int:
"""Calculate Euler's Phi Function.
>>> euler_phi(100)
40
"""
s = n
for x in set(prime_factors(n)):
s *= (x - 1) / x
return int(s)
if __name__ == "__main__":
print(prime_factors(100))
print(number_of_divisors(100))
print(sum_of_divisors(100))
print(euler_phi(100))
| """Implementation of Basic Math in Python."""
import math
def prime_factors(n: int) -> list:
"""Find Prime Factors.
>>> prime_factors(100)
[2, 2, 5, 5]
"""
pf = []
while n % 2 == 0:
pf.append(2)
n = int(n / 2)
for i in range(3, int(math.sqrt(n)) + 1, 2):
while n % i == 0:
pf.append(i)
n = int(n / i)
if n > 2:
pf.append(n)
return pf
def number_of_divisors(n: int) -> int:
"""Calculate Number of Divisors of an Integer.
>>> number_of_divisors(100)
9
"""
div = 1
temp = 1
while n % 2 == 0:
temp += 1
n = int(n / 2)
div *= temp
for i in range(3, int(math.sqrt(n)) + 1, 2):
temp = 1
while n % i == 0:
temp += 1
n = int(n / i)
div *= temp
return div
def sum_of_divisors(n: int) -> int:
"""Calculate Sum of Divisors.
>>> sum_of_divisors(100)
217
"""
s = 1
temp = 1
while n % 2 == 0:
temp += 1
n = int(n / 2)
if temp > 1:
s *= (2 ** temp - 1) / (2 - 1)
for i in range(3, int(math.sqrt(n)) + 1, 2):
temp = 1
while n % i == 0:
temp += 1
n = int(n / i)
if temp > 1:
s *= (i ** temp - 1) / (i - 1)
return int(s)
def euler_phi(n: int) -> int:
"""Calculate Euler's Phi Function.
>>> euler_phi(100)
40
"""
s = n
for x in set(prime_factors(n)):
s *= (x - 1) / x
return int(s)
if __name__ == "__main__":
print(prime_factors(100))
print(number_of_divisors(100))
print(sum_of_divisors(100))
print(euler_phi(100))
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
min_primitive_root = 3
# I have written my code naively same as definition of primitive root
# however every time I run this program, memory exceeded...
# so I used 4.80 Algorithm in
# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
# and it seems to run nicely!
def primitive_root(p_val: int) -> int:
print("Generating primitive root of p")
while True:
g = random.randrange(3, p_val)
if pow(g, 2, p_val) == 1:
continue
if pow(g, p_val, p_val) == 1:
continue
return g
def generate_key(key_size: int) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabin_miller.generateLargePrime(key_size) # select large prime number.
e_1 = primitive_root(p) # one primitive root on modulo p.
d = random.randrange(3, p) # private_key -> have to be greater than 2 for safety.
e_2 = cryptomath.find_mod_inverse(pow(e_1, d, p), p)
public_key = (key_size, e_1, e_2, p)
private_key = (key_size, d)
return public_key, private_key
def make_key_files(name: str, keySize: int) -> None:
if os.path.exists("%s_pubkey.txt" % name) or os.path.exists(
"%s_privkey.txt" % name
):
print("\nWARNING:")
print(
'"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
% (name, name)
)
sys.exit()
publicKey, privateKey = generate_key(keySize)
print("\nWriting public key to file %s_pubkey.txt..." % name)
with open("%s_pubkey.txt" % name, "w") as fo:
fo.write(
"%d,%d,%d,%d" % (publicKey[0], publicKey[1], publicKey[2], publicKey[3])
)
print("Writing private key to file %s_privkey.txt..." % name)
with open("%s_privkey.txt" % name, "w") as fo:
fo.write("%d,%d" % (privateKey[0], privateKey[1]))
def main() -> None:
print("Making key files...")
make_key_files("elgamal", 2048)
print("Key files generation successful")
if __name__ == "__main__":
main()
| import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
min_primitive_root = 3
# I have written my code naively same as definition of primitive root
# however every time I run this program, memory exceeded...
# so I used 4.80 Algorithm in
# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
# and it seems to run nicely!
def primitive_root(p_val: int) -> int:
print("Generating primitive root of p")
while True:
g = random.randrange(3, p_val)
if pow(g, 2, p_val) == 1:
continue
if pow(g, p_val, p_val) == 1:
continue
return g
def generate_key(key_size: int) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabin_miller.generateLargePrime(key_size) # select large prime number.
e_1 = primitive_root(p) # one primitive root on modulo p.
d = random.randrange(3, p) # private_key -> have to be greater than 2 for safety.
e_2 = cryptomath.find_mod_inverse(pow(e_1, d, p), p)
public_key = (key_size, e_1, e_2, p)
private_key = (key_size, d)
return public_key, private_key
def make_key_files(name: str, keySize: int) -> None:
if os.path.exists("%s_pubkey.txt" % name) or os.path.exists(
"%s_privkey.txt" % name
):
print("\nWARNING:")
print(
'"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
% (name, name)
)
sys.exit()
publicKey, privateKey = generate_key(keySize)
print("\nWriting public key to file %s_pubkey.txt..." % name)
with open("%s_pubkey.txt" % name, "w") as fo:
fo.write(
"%d,%d,%d,%d" % (publicKey[0], publicKey[1], publicKey[2], publicKey[3])
)
print("Writing private key to file %s_privkey.txt..." % name)
with open("%s_privkey.txt" % name, "w") as fo:
fo.write("%d,%d" % (privateKey[0], privateKey[1]))
def main() -> None:
print("Making key files...")
make_key_files("elgamal", 2048)
print("Key files generation successful")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 Reverse Polish Nation also known as Polish postfix notation
or simply postfix notation.
https://en.wikipedia.org/wiki/Reverse_Polish_notation
Classic examples of simple stack implementations
Valid operators are +, -, *, /.
Each operand may be an integer or another expression.
"""
def evaluate_postfix(postfix_notation: list) -> int:
"""
>>> evaluate_postfix(["2", "1", "+", "3", "*"])
9
>>> evaluate_postfix(["4", "13", "5", "/", "+"])
6
>>> evaluate_postfix([])
0
"""
if not postfix_notation:
return 0
operations = {"+", "-", "*", "/"}
stack = []
for token in postfix_notation:
if token in operations:
b, a = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b)
elif token == "-":
stack.append(a - b)
elif token == "*":
stack.append(a * b)
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1)
else:
stack.append(a // b)
else:
stack.append(int(token))
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
The Reverse Polish Nation also known as Polish postfix notation
or simply postfix notation.
https://en.wikipedia.org/wiki/Reverse_Polish_notation
Classic examples of simple stack implementations
Valid operators are +, -, *, /.
Each operand may be an integer or another expression.
"""
def evaluate_postfix(postfix_notation: list) -> int:
"""
>>> evaluate_postfix(["2", "1", "+", "3", "*"])
9
>>> evaluate_postfix(["4", "13", "5", "/", "+"])
6
>>> evaluate_postfix([])
0
"""
if not postfix_notation:
return 0
operations = {"+", "-", "*", "/"}
stack = []
for token in postfix_notation:
if token in operations:
b, a = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b)
elif token == "-":
stack.append(a - b)
elif token == "*":
stack.append(a * b)
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1)
else:
stack.append(a // b)
else:
stack.append(int(token))
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 re
def natural_sort(input_list: list[str]) -> list[str]:
"""
Sort the given list of strings in the way that humans expect.
The normal Python sort algorithm sorts lexicographically,
so you might not get the results that you expect...
>>> example1 = ['2 ft 7 in', '1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '7 ft 6 in']
>>> sorted(example1)
['1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '2 ft 7 in', '7 ft 6 in']
>>> # The natural sort algorithm sort based on meaning and not computer code point.
>>> natural_sort(example1)
['1 ft 5 in', '2 ft 7 in', '2 ft 11 in', '7 ft 6 in', '10 ft 2 in']
>>> example2 = ['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
>>> sorted(example2)
['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
>>> natural_sort(example2)
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
"""
def alphanum_key(key):
return [int(s) if s.isdigit() else s.lower() for s in re.split("([0-9]+)", key)]
return sorted(input_list, key=alphanum_key)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
import re
def natural_sort(input_list: list[str]) -> list[str]:
"""
Sort the given list of strings in the way that humans expect.
The normal Python sort algorithm sorts lexicographically,
so you might not get the results that you expect...
>>> example1 = ['2 ft 7 in', '1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '7 ft 6 in']
>>> sorted(example1)
['1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '2 ft 7 in', '7 ft 6 in']
>>> # The natural sort algorithm sort based on meaning and not computer code point.
>>> natural_sort(example1)
['1 ft 5 in', '2 ft 7 in', '2 ft 11 in', '7 ft 6 in', '10 ft 2 in']
>>> example2 = ['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
>>> sorted(example2)
['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
>>> natural_sort(example2)
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
"""
def alphanum_key(key):
return [int(s) if s.isdigit() else s.lower() for s in re.split("([0-9]+)", key)]
return sorted(input_list, key=alphanum_key)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 57: https://projecteuler.net/problem=57
It is possible to show that the square root of two can be expressed as an infinite
continued fraction.
sqrt(2) = 1 + 1 / (2 + 1 / (2 + 1 / (2 + ...)))
By expanding this for the first four iterations, we get:
1 + 1 / 2 = 3 / 2 = 1.5
1 + 1 / (2 + 1 / 2} = 7 / 5 = 1.4
1 + 1 / (2 + 1 / (2 + 1 / 2)) = 17 / 12 = 1.41666...
1 + 1 / (2 + 1 / (2 + 1 / (2 + 1 / 2))) = 41/ 29 = 1.41379...
The next three expansions are 99/70, 239/169, and 577/408, but the eighth expansion,
1393/985, is the first example where the number of digits in the numerator exceeds
the number of digits in the denominator.
In the first one-thousand expansions, how many fractions contain a numerator with
more digits than the denominator?
"""
def solution(n: int = 1000) -> int:
"""
returns number of fractions containing a numerator with more digits than
the denominator in the first n expansions.
>>> solution(14)
2
>>> solution(100)
15
>>> solution(10000)
1508
"""
prev_numerator, prev_denominator = 1, 1
result = []
for i in range(1, n + 1):
numerator = prev_numerator + 2 * prev_denominator
denominator = prev_numerator + prev_denominator
if len(str(numerator)) > len(str(denominator)):
result.append(i)
prev_numerator = numerator
prev_denominator = denominator
return len(result)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 57: https://projecteuler.net/problem=57
It is possible to show that the square root of two can be expressed as an infinite
continued fraction.
sqrt(2) = 1 + 1 / (2 + 1 / (2 + 1 / (2 + ...)))
By expanding this for the first four iterations, we get:
1 + 1 / 2 = 3 / 2 = 1.5
1 + 1 / (2 + 1 / 2} = 7 / 5 = 1.4
1 + 1 / (2 + 1 / (2 + 1 / 2)) = 17 / 12 = 1.41666...
1 + 1 / (2 + 1 / (2 + 1 / (2 + 1 / 2))) = 41/ 29 = 1.41379...
The next three expansions are 99/70, 239/169, and 577/408, but the eighth expansion,
1393/985, is the first example where the number of digits in the numerator exceeds
the number of digits in the denominator.
In the first one-thousand expansions, how many fractions contain a numerator with
more digits than the denominator?
"""
def solution(n: int = 1000) -> int:
"""
returns number of fractions containing a numerator with more digits than
the denominator in the first n expansions.
>>> solution(14)
2
>>> solution(100)
15
>>> solution(10000)
1508
"""
prev_numerator, prev_denominator = 1, 1
result = []
for i in range(1, n + 1):
numerator = prev_numerator + 2 * prev_denominator
denominator = prev_numerator + prev_denominator
if len(str(numerator)) > len(str(denominator)):
result.append(i)
prev_numerator = numerator
prev_denominator = denominator
return len(result)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| """
pseudo-code
DIJKSTRA(graph G, start vertex s, destination vertex d):
//all nodes initially unexplored
1 - let H = min heap data structure, initialized with 0 and s [here 0 indicates
the distance from start vertex s]
2 - while H is non-empty:
3 - remove the first node and cost of H, call it U and cost
4 - if U has been previously explored:
5 - go to the while loop, line 2 //Once a node is explored there is no need
to make it again
6 - mark U as explored
7 - if U is d:
8 - return cost // total cost from start to destination vertex
9 - for each edge(U, V): c=cost of edge(U,V) // for V in graph[U]
10 - if V explored:
11 - go to next V in line 9
12 - total_cost = cost + c
13 - add (total_cost,V) to H
You can think at cost as a distance where Dijkstra finds the shortest distance
between vertices s and v in a graph G. The use of a min heap as H guarantees
that if a vertex has already been explored there will be no other path with
shortest distance, that happens because heapq.heappop will always return the
next vertex with the shortest distance, considering that the heap stores not
only the distance between previous vertex and current vertex but the entire
distance between each vertex that makes up the path from start vertex to target
vertex.
"""
import heapq
def dijkstra(graph, start, end):
"""Return the cost of the shortest path between vertices start and end.
>>> dijkstra(G, "E", "C")
6
>>> dijkstra(G2, "E", "F")
3
>>> dijkstra(G3, "E", "F")
3
"""
heap = [(0, start)] # cost from start node,end node
visited = set()
while heap:
(cost, u) = heapq.heappop(heap)
if u in visited:
continue
visited.add(u)
if u == end:
return cost
for v, c in graph[u]:
if v in visited:
continue
next = cost + c
heapq.heappush(heap, (next, v))
return -1
G = {
"A": [["B", 2], ["C", 5]],
"B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
"C": [["A", 5], ["F", 3]],
"D": [["B", 3]],
"E": [["B", 4], ["F", 3]],
"F": [["C", 3], ["E", 3]],
}
r"""
Layout of G2:
E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F
\ /\
\ ||
----------------- 3 --------------------
"""
G2 = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["F", 3]],
"F": [],
}
r"""
Layout of G3:
E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F
\ /\
\ ||
-------- 2 ---------> G ------- 1 ------
"""
G3 = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["G", 2]],
"F": [],
"G": [["F", 1]],
}
shortDistance = dijkstra(G, "E", "C")
print(shortDistance) # E -- 3 --> F -- 3 --> C == 6
shortDistance = dijkstra(G2, "E", "F")
print(shortDistance) # E -- 3 --> F == 3
shortDistance = dijkstra(G3, "E", "F")
print(shortDistance) # E -- 2 --> G -- 1 --> F == 3
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
pseudo-code
DIJKSTRA(graph G, start vertex s, destination vertex d):
//all nodes initially unexplored
1 - let H = min heap data structure, initialized with 0 and s [here 0 indicates
the distance from start vertex s]
2 - while H is non-empty:
3 - remove the first node and cost of H, call it U and cost
4 - if U has been previously explored:
5 - go to the while loop, line 2 //Once a node is explored there is no need
to make it again
6 - mark U as explored
7 - if U is d:
8 - return cost // total cost from start to destination vertex
9 - for each edge(U, V): c=cost of edge(U,V) // for V in graph[U]
10 - if V explored:
11 - go to next V in line 9
12 - total_cost = cost + c
13 - add (total_cost,V) to H
You can think at cost as a distance where Dijkstra finds the shortest distance
between vertices s and v in a graph G. The use of a min heap as H guarantees
that if a vertex has already been explored there will be no other path with
shortest distance, that happens because heapq.heappop will always return the
next vertex with the shortest distance, considering that the heap stores not
only the distance between previous vertex and current vertex but the entire
distance between each vertex that makes up the path from start vertex to target
vertex.
"""
import heapq
def dijkstra(graph, start, end):
"""Return the cost of the shortest path between vertices start and end.
>>> dijkstra(G, "E", "C")
6
>>> dijkstra(G2, "E", "F")
3
>>> dijkstra(G3, "E", "F")
3
"""
heap = [(0, start)] # cost from start node,end node
visited = set()
while heap:
(cost, u) = heapq.heappop(heap)
if u in visited:
continue
visited.add(u)
if u == end:
return cost
for v, c in graph[u]:
if v in visited:
continue
next = cost + c
heapq.heappush(heap, (next, v))
return -1
G = {
"A": [["B", 2], ["C", 5]],
"B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
"C": [["A", 5], ["F", 3]],
"D": [["B", 3]],
"E": [["B", 4], ["F", 3]],
"F": [["C", 3], ["E", 3]],
}
r"""
Layout of G2:
E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F
\ /\
\ ||
----------------- 3 --------------------
"""
G2 = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["F", 3]],
"F": [],
}
r"""
Layout of G3:
E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F
\ /\
\ ||
-------- 2 ---------> G ------- 1 ------
"""
G3 = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["G", 2]],
"F": [],
"G": [["F", 1]],
}
shortDistance = dijkstra(G, "E", "C")
print(shortDistance) # E -- 3 --> F -- 3 --> C == 6
shortDistance = dijkstra(G2, "E", "F")
print(shortDistance) # E -- 3 --> F == 3
shortDistance = dijkstra(G3, "E", "F")
print(shortDistance) # E -- 2 --> G -- 1 --> F == 3
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| """
Lychrel numbers
Problem 55: https://projecteuler.net/problem=55
If we take 47, reverse and add, 47 + 74 = 121, which is palindromic.
Not all numbers produce palindromes so quickly. For example,
349 + 943 = 1292,
1292 + 2921 = 4213
4213 + 3124 = 7337
That is, 349 took three iterations to arrive at a palindrome.
Although no one has proved it yet, it is thought that some numbers, like 196,
never produce a palindrome. A number that never forms a palindrome through the
reverse and add process is called a Lychrel number. Due to the theoretical nature
of these numbers, and for the purpose of this problem, we shall assume that a number
is Lychrel until proven otherwise. In addition you are given that for every number
below ten-thousand, it will either (i) become a palindrome in less than fifty
iterations, or, (ii) no one, with all the computing power that exists, has managed
so far to map it to a palindrome. In fact, 10677 is the first number to be shown
to require over fifty iterations before producing a palindrome:
4668731596684224866951378664 (53 iterations, 28-digits).
Surprisingly, there are palindromic numbers that are themselves Lychrel numbers;
the first example is 4994.
How many Lychrel numbers are there below ten-thousand?
"""
def is_palindrome(n: int) -> bool:
"""
Returns True if a number is palindrome.
>>> is_palindrome(12567321)
False
>>> is_palindrome(1221)
True
>>> is_palindrome(9876789)
True
"""
return str(n) == str(n)[::-1]
def sum_reverse(n: int) -> int:
"""
Returns the sum of n and reverse of n.
>>> sum_reverse(123)
444
>>> sum_reverse(3478)
12221
>>> sum_reverse(12)
33
"""
return int(n) + int(str(n)[::-1])
def solution(limit: int = 10000) -> int:
"""
Returns the count of all lychrel numbers below limit.
>>> solution(10000)
249
>>> solution(5000)
76
>>> solution(1000)
13
"""
lychrel_nums = []
for num in range(1, limit):
iterations = 0
a = num
while iterations < 50:
num = sum_reverse(num)
iterations += 1
if is_palindrome(num):
break
else:
lychrel_nums.append(a)
return len(lychrel_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Lychrel numbers
Problem 55: https://projecteuler.net/problem=55
If we take 47, reverse and add, 47 + 74 = 121, which is palindromic.
Not all numbers produce palindromes so quickly. For example,
349 + 943 = 1292,
1292 + 2921 = 4213
4213 + 3124 = 7337
That is, 349 took three iterations to arrive at a palindrome.
Although no one has proved it yet, it is thought that some numbers, like 196,
never produce a palindrome. A number that never forms a palindrome through the
reverse and add process is called a Lychrel number. Due to the theoretical nature
of these numbers, and for the purpose of this problem, we shall assume that a number
is Lychrel until proven otherwise. In addition you are given that for every number
below ten-thousand, it will either (i) become a palindrome in less than fifty
iterations, or, (ii) no one, with all the computing power that exists, has managed
so far to map it to a palindrome. In fact, 10677 is the first number to be shown
to require over fifty iterations before producing a palindrome:
4668731596684224866951378664 (53 iterations, 28-digits).
Surprisingly, there are palindromic numbers that are themselves Lychrel numbers;
the first example is 4994.
How many Lychrel numbers are there below ten-thousand?
"""
def is_palindrome(n: int) -> bool:
"""
Returns True if a number is palindrome.
>>> is_palindrome(12567321)
False
>>> is_palindrome(1221)
True
>>> is_palindrome(9876789)
True
"""
return str(n) == str(n)[::-1]
def sum_reverse(n: int) -> int:
"""
Returns the sum of n and reverse of n.
>>> sum_reverse(123)
444
>>> sum_reverse(3478)
12221
>>> sum_reverse(12)
33
"""
return int(n) + int(str(n)[::-1])
def solution(limit: int = 10000) -> int:
"""
Returns the count of all lychrel numbers below limit.
>>> solution(10000)
249
>>> solution(5000)
76
>>> solution(1000)
13
"""
lychrel_nums = []
for num in range(1, limit):
iterations = 0
a = num
while iterations < 50:
num = sum_reverse(num)
iterations += 1
if is_palindrome(num):
break
else:
lychrel_nums.append(a)
return len(lychrel_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| """
Implemented an algorithm using opencv to tone an image with sepia technique
"""
from cv2 import destroyAllWindows, imread, imshow, waitKey
def make_sepia(img, factor: int):
"""
Function create sepia tone.
Source: https://en.wikipedia.org/wiki/Sepia_(color)
"""
pixel_h, pixel_v = img.shape[0], img.shape[1]
def to_grayscale(blue, green, red):
"""
Helper function to create pixel's greyscale representation
Src: https://pl.wikipedia.org/wiki/YUV
"""
return 0.2126 * red + 0.587 * green + 0.114 * blue
def normalize(value):
""" Helper function to normalize R/G/B value -> return 255 if value > 255"""
return min(value, 255)
for i in range(pixel_h):
for j in range(pixel_v):
greyscale = int(to_grayscale(*img[i][j]))
img[i][j] = [
normalize(greyscale),
normalize(greyscale + factor),
normalize(greyscale + 2 * factor),
]
return img
if __name__ == "__main__":
# read original image
images = {
percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
}
for percentage, img in images.items():
make_sepia(img, percentage)
for percentage, img in images.items():
imshow(f"Original image with sepia (factor: {percentage})", img)
waitKey(0)
destroyAllWindows()
| """
Implemented an algorithm using opencv to tone an image with sepia technique
"""
from cv2 import destroyAllWindows, imread, imshow, waitKey
def make_sepia(img, factor: int):
"""
Function create sepia tone.
Source: https://en.wikipedia.org/wiki/Sepia_(color)
"""
pixel_h, pixel_v = img.shape[0], img.shape[1]
def to_grayscale(blue, green, red):
"""
Helper function to create pixel's greyscale representation
Src: https://pl.wikipedia.org/wiki/YUV
"""
return 0.2126 * red + 0.587 * green + 0.114 * blue
def normalize(value):
""" Helper function to normalize R/G/B value -> return 255 if value > 255"""
return min(value, 255)
for i in range(pixel_h):
for j in range(pixel_v):
greyscale = int(to_grayscale(*img[i][j]))
img[i][j] = [
normalize(greyscale),
normalize(greyscale + factor),
normalize(greyscale + 2 * factor),
]
return img
if __name__ == "__main__":
# read original image
images = {
percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
}
for percentage, img in images.items():
make_sepia(img, percentage)
for percentage, img in images.items():
imshow(f"Original image with sepia (factor: {percentage})", img)
waitKey(0)
destroyAllWindows()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 Node:
def __init__(self, data):
self.data = data
self.next = None
def __repr__(self):
return f"Node({self.data})"
class LinkedList:
def __init__(self):
self.head = None
def __iter__(self):
node = self.head
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
Return length of linked list i.e. number of nodes
>>> linked_list = LinkedList()
>>> len(linked_list)
0
>>> linked_list.insert_tail("head")
>>> len(linked_list)
1
>>> linked_list.insert_head("head")
>>> len(linked_list)
2
>>> _ = linked_list.delete_tail()
>>> len(linked_list)
1
>>> _ = linked_list.delete_head()
>>> len(linked_list)
0
"""
return len(tuple(iter(self)))
def __repr__(self):
"""
String representation/visualization of a Linked Lists
"""
return "->".join([str(item) for item in self])
def __getitem__(self, index):
"""
Indexing Support. Used to get a node at particular position
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> all(str(linked_list[i]) == str(i) for i in range(0, 10))
True
>>> linked_list[-10]
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)]
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
for i, node in enumerate(self):
if i == index:
return node
# Used to change the data of a particular node
def __setitem__(self, index, data):
"""
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> linked_list[0] = 666
>>> linked_list[0]
666
>>> linked_list[5] = -666
>>> linked_list[5]
-666
>>> linked_list[-10] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
current = self.head
for i in range(index):
current = current.next
current.data = data
def insert_tail(self, data) -> None:
self.insert_nth(len(self), data)
def insert_head(self, data) -> None:
self.insert_nth(0, data)
def insert_nth(self, index: int, data) -> None:
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = new_node
elif index == 0:
new_node.next = self.head # link new_node to head
self.head = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
def print_list(self) -> None: # print every node data
print(self)
def delete_head(self):
return self.delete_nth(0)
def delete_tail(self): # delete from tail
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0):
if not 0 <= index <= len(self) - 1: # test if index is valid
raise IndexError("list index out of range")
delete_node = self.head # default first node
if index == 0:
self.head = self.head.next
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
delete_node = temp.next
temp.next = temp.next.next
return delete_node.data
def is_empty(self) -> bool:
return self.head is None
def reverse(self):
prev = None
current = self.head
while current:
# Store the current node's next node.
next_node = current.next
# Make the current node's next point backwards
current.next = prev
# Make the previous node be the current node
prev = current
# Make the current node the next node (to progress iteration)
current = next_node
# Return prev in order to put the head at the end
self.head = prev
def test_singly_linked_list() -> None:
"""
>>> test_singly_linked_list()
"""
linked_list = LinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_head(0)
linked_list.insert_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
assert all(linked_list[i] == i + 1 for i in range(0, 9)) is True
for i in range(0, 9):
linked_list[i] = -i
assert all(linked_list[i] == -i for i in range(0, 9)) is True
def main():
from doctest import testmod
testmod()
linked_list = LinkedList()
linked_list.insert_head(input("Inserting 1st at head ").strip())
linked_list.insert_head(input("Inserting 2nd at head ").strip())
print("\nPrint list:")
linked_list.print_list()
linked_list.insert_tail(input("\nInserting 1st at tail ").strip())
linked_list.insert_tail(input("Inserting 2nd at tail ").strip())
print("\nPrint list:")
linked_list.print_list()
print("\nDelete head")
linked_list.delete_head()
print("Delete tail")
linked_list.delete_tail()
print("\nPrint list:")
linked_list.print_list()
print("\nReverse linked list")
linked_list.reverse()
print("\nPrint list:")
linked_list.print_list()
print("\nString representation of linked list:")
print(linked_list)
print("\nReading/changing Node data using indexing:")
print(f"Element at Position 1: {linked_list[1]}")
linked_list[1] = input("Enter New Value: ").strip()
print("New list:")
print(linked_list)
print(f"length of linked_list is : {len(linked_list)}")
if __name__ == "__main__":
main()
| class Node:
def __init__(self, data):
self.data = data
self.next = None
def __repr__(self):
return f"Node({self.data})"
class LinkedList:
def __init__(self):
self.head = None
def __iter__(self):
node = self.head
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
Return length of linked list i.e. number of nodes
>>> linked_list = LinkedList()
>>> len(linked_list)
0
>>> linked_list.insert_tail("head")
>>> len(linked_list)
1
>>> linked_list.insert_head("head")
>>> len(linked_list)
2
>>> _ = linked_list.delete_tail()
>>> len(linked_list)
1
>>> _ = linked_list.delete_head()
>>> len(linked_list)
0
"""
return len(tuple(iter(self)))
def __repr__(self):
"""
String representation/visualization of a Linked Lists
"""
return "->".join([str(item) for item in self])
def __getitem__(self, index):
"""
Indexing Support. Used to get a node at particular position
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> all(str(linked_list[i]) == str(i) for i in range(0, 10))
True
>>> linked_list[-10]
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)]
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
for i, node in enumerate(self):
if i == index:
return node
# Used to change the data of a particular node
def __setitem__(self, index, data):
"""
>>> linked_list = LinkedList()
>>> for i in range(0, 10):
... linked_list.insert_nth(i, i)
>>> linked_list[0] = 666
>>> linked_list[0]
666
>>> linked_list[5] = -666
>>> linked_list[5]
-666
>>> linked_list[-10] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
>>> linked_list[len(linked_list)] = 666
Traceback (most recent call last):
...
ValueError: list index out of range.
"""
if not 0 <= index < len(self):
raise ValueError("list index out of range.")
current = self.head
for i in range(index):
current = current.next
current.data = data
def insert_tail(self, data) -> None:
self.insert_nth(len(self), data)
def insert_head(self, data) -> None:
self.insert_nth(0, data)
def insert_nth(self, index: int, data) -> None:
if not 0 <= index <= len(self):
raise IndexError("list index out of range")
new_node = Node(data)
if self.head is None:
self.head = new_node
elif index == 0:
new_node.next = self.head # link new_node to head
self.head = new_node
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
new_node.next = temp.next
temp.next = new_node
def print_list(self) -> None: # print every node data
print(self)
def delete_head(self):
return self.delete_nth(0)
def delete_tail(self): # delete from tail
return self.delete_nth(len(self) - 1)
def delete_nth(self, index: int = 0):
if not 0 <= index <= len(self) - 1: # test if index is valid
raise IndexError("list index out of range")
delete_node = self.head # default first node
if index == 0:
self.head = self.head.next
else:
temp = self.head
for _ in range(index - 1):
temp = temp.next
delete_node = temp.next
temp.next = temp.next.next
return delete_node.data
def is_empty(self) -> bool:
return self.head is None
def reverse(self):
prev = None
current = self.head
while current:
# Store the current node's next node.
next_node = current.next
# Make the current node's next point backwards
current.next = prev
# Make the previous node be the current node
prev = current
# Make the current node the next node (to progress iteration)
current = next_node
# Return prev in order to put the head at the end
self.head = prev
def test_singly_linked_list() -> None:
"""
>>> test_singly_linked_list()
"""
linked_list = LinkedList()
assert linked_list.is_empty() is True
assert str(linked_list) == ""
try:
linked_list.delete_head()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
assert False # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(linked_list) == i
linked_list.insert_nth(i, i + 1)
assert str(linked_list) == "->".join(str(i) for i in range(1, 11))
linked_list.insert_head(0)
linked_list.insert_tail(11)
assert str(linked_list) == "->".join(str(i) for i in range(0, 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(linked_list) == 9
assert str(linked_list) == "->".join(str(i) for i in range(1, 10))
assert all(linked_list[i] == i + 1 for i in range(0, 9)) is True
for i in range(0, 9):
linked_list[i] = -i
assert all(linked_list[i] == -i for i in range(0, 9)) is True
def main():
from doctest import testmod
testmod()
linked_list = LinkedList()
linked_list.insert_head(input("Inserting 1st at head ").strip())
linked_list.insert_head(input("Inserting 2nd at head ").strip())
print("\nPrint list:")
linked_list.print_list()
linked_list.insert_tail(input("\nInserting 1st at tail ").strip())
linked_list.insert_tail(input("Inserting 2nd at tail ").strip())
print("\nPrint list:")
linked_list.print_list()
print("\nDelete head")
linked_list.delete_head()
print("Delete tail")
linked_list.delete_tail()
print("\nPrint list:")
linked_list.print_list()
print("\nReverse linked list")
linked_list.reverse()
print("\nPrint list:")
linked_list.print_list()
print("\nString representation of linked list:")
print(linked_list)
print("\nReading/changing Node data using indexing:")
print(f"Element at Position 1: {linked_list[1]}")
linked_list[1] = input("Enter New Value: ").strip()
print("New list:")
print(linked_list)
print(f"length of linked_list is : {len(linked_list)}")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 129: https://projecteuler.net/problem=129
A number consisting entirely of ones is called a repunit. We shall define R(k) to be
a repunit of length k; for example, R(6) = 111111.
Given that n is a positive integer and GCD(n, 10) = 1, it can be shown that there
always exists a value, k, for which R(k) is divisible by n, and let A(n) be the least
such value of k; for example, A(7) = 6 and A(41) = 5.
The least value of n for which A(n) first exceeds ten is 17.
Find the least value of n for which A(n) first exceeds one-million.
"""
def least_divisible_repunit(divisor: int) -> int:
"""
Return the least value k such that the Repunit of length k is divisible by divisor.
>>> least_divisible_repunit(7)
6
>>> least_divisible_repunit(41)
5
>>> least_divisible_repunit(1234567)
34020
"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
repunit = 1
repunit_index = 1
while repunit:
repunit = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def solution(limit: int = 1000000) -> int:
"""
Return the least value of n for which least_divisible_repunit(n)
first exceeds limit.
>>> solution(10)
17
>>> solution(100)
109
>>> solution(1000)
1017
"""
divisor = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(divisor) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 129: https://projecteuler.net/problem=129
A number consisting entirely of ones is called a repunit. We shall define R(k) to be
a repunit of length k; for example, R(6) = 111111.
Given that n is a positive integer and GCD(n, 10) = 1, it can be shown that there
always exists a value, k, for which R(k) is divisible by n, and let A(n) be the least
such value of k; for example, A(7) = 6 and A(41) = 5.
The least value of n for which A(n) first exceeds ten is 17.
Find the least value of n for which A(n) first exceeds one-million.
"""
def least_divisible_repunit(divisor: int) -> int:
"""
Return the least value k such that the Repunit of length k is divisible by divisor.
>>> least_divisible_repunit(7)
6
>>> least_divisible_repunit(41)
5
>>> least_divisible_repunit(1234567)
34020
"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
repunit = 1
repunit_index = 1
while repunit:
repunit = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def solution(limit: int = 1000000) -> int:
"""
Return the least value of n for which least_divisible_repunit(n)
first exceeds limit.
>>> solution(10)
17
>>> solution(100)
109
>>> solution(1000)
1017
"""
divisor = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(divisor) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| # Numbers of alphabet which we call base
alphabet_size = 256
# Modulus to hash a string
modulus = 1000003
def rabin_karp(pattern: str, text: str) -> bool:
"""
The Rabin-Karp Algorithm for finding a pattern within a piece of text
with complexity O(nm), most efficient when it is used with multiple patterns
as it is able to check if any of a set of patterns match a section of text in o(1)
given the precomputed hashes.
This will be the simple version which only assumes one pattern is being searched
for but it's not hard to modify
1) Calculate pattern hash
2) Step through the text one character at a time passing a window with the same
length as the pattern
calculating the hash of the text within the window compare it with the hash
of the pattern. Only testing equality if the hashes match
"""
p_len = len(pattern)
t_len = len(text)
if p_len > t_len:
return False
p_hash = 0
text_hash = 0
modulus_power = 1
# Calculating the hash of pattern and substring of text
for i in range(p_len):
p_hash = (ord(pattern[i]) + p_hash * alphabet_size) % modulus
text_hash = (ord(text[i]) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
modulus_power = (modulus_power * alphabet_size) % modulus
for i in range(0, t_len - p_len + 1):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
text_hash = (
(text_hash - ord(text[i]) * modulus_power) * alphabet_size
+ ord(text[i + p_len])
) % modulus
return False
def test_rabin_karp() -> None:
"""
>>> test_rabin_karp()
Success.
"""
# Test 1)
pattern = "abc1abc12"
text1 = "alskfjaldsabc1abc1abc12k23adsfabcabc"
text2 = "alskfjaldsk23adsfabcabc"
assert rabin_karp(pattern, text1) and not rabin_karp(pattern, text2)
# Test 2)
pattern = "ABABX"
text = "ABABZABABYABABX"
assert rabin_karp(pattern, text)
# Test 3)
pattern = "AAAB"
text = "ABAAAAAB"
assert rabin_karp(pattern, text)
# Test 4)
pattern = "abcdabcy"
text = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(pattern, text)
# Test 5)
pattern = "Lü"
text = "Lüsai"
assert rabin_karp(pattern, text)
pattern = "Lue"
assert not rabin_karp(pattern, text)
print("Success.")
if __name__ == "__main__":
test_rabin_karp()
| # Numbers of alphabet which we call base
alphabet_size = 256
# Modulus to hash a string
modulus = 1000003
def rabin_karp(pattern: str, text: str) -> bool:
"""
The Rabin-Karp Algorithm for finding a pattern within a piece of text
with complexity O(nm), most efficient when it is used with multiple patterns
as it is able to check if any of a set of patterns match a section of text in o(1)
given the precomputed hashes.
This will be the simple version which only assumes one pattern is being searched
for but it's not hard to modify
1) Calculate pattern hash
2) Step through the text one character at a time passing a window with the same
length as the pattern
calculating the hash of the text within the window compare it with the hash
of the pattern. Only testing equality if the hashes match
"""
p_len = len(pattern)
t_len = len(text)
if p_len > t_len:
return False
p_hash = 0
text_hash = 0
modulus_power = 1
# Calculating the hash of pattern and substring of text
for i in range(p_len):
p_hash = (ord(pattern[i]) + p_hash * alphabet_size) % modulus
text_hash = (ord(text[i]) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
modulus_power = (modulus_power * alphabet_size) % modulus
for i in range(0, t_len - p_len + 1):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
text_hash = (
(text_hash - ord(text[i]) * modulus_power) * alphabet_size
+ ord(text[i + p_len])
) % modulus
return False
def test_rabin_karp() -> None:
"""
>>> test_rabin_karp()
Success.
"""
# Test 1)
pattern = "abc1abc12"
text1 = "alskfjaldsabc1abc1abc12k23adsfabcabc"
text2 = "alskfjaldsk23adsfabcabc"
assert rabin_karp(pattern, text1) and not rabin_karp(pattern, text2)
# Test 2)
pattern = "ABABX"
text = "ABABZABABYABABX"
assert rabin_karp(pattern, text)
# Test 3)
pattern = "AAAB"
text = "ABAAAAAB"
assert rabin_karp(pattern, text)
# Test 4)
pattern = "abcdabcy"
text = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(pattern, text)
# Test 5)
pattern = "Lü"
text = "Lüsai"
assert rabin_karp(pattern, text)
pattern = "Lue"
assert not rabin_karp(pattern, text)
print("Success.")
if __name__ == "__main__":
test_rabin_karp()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
- https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
"""
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n using Sieve of Eratosthenes:
The sieve of Eratosthenes is one of the most efficient ways to find all primes
smaller than n when n is smaller than 10 million. Only for positive numbers.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
>>> solution(7.1) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> solution(-7) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
IndexError: list assignment index out of range
>>> solution("seven") # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: can only concatenate str (not "int") to str
"""
primality_list = [0 for i in range(n + 1)]
primality_list[0] = 1
primality_list[1] = 1
for i in range(2, int(n ** 0.5) + 1):
if primality_list[i] == 0:
for j in range(i * i, n + 1, i):
primality_list[j] = 1
sum_of_primes = 0
for i in range(n):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
- https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes
"""
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n using Sieve of Eratosthenes:
The sieve of Eratosthenes is one of the most efficient ways to find all primes
smaller than n when n is smaller than 10 million. Only for positive numbers.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
>>> solution(7.1) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> solution(-7) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
IndexError: list assignment index out of range
>>> solution("seven") # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: can only concatenate str (not "int") to str
"""
primality_list = [0 for i in range(n + 1)]
primality_list[0] = 1
primality_list[1] = 1
for i in range(2, int(n ** 0.5) + 1):
if primality_list[i] == 0:
for j in range(i * i, n + 1, i):
primality_list[j] = 1
sum_of_primes = 0
for i in range(n):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 Harmonic Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)
For doctests run following command:
python -m doctest -v harmonic_series.py
or
python3 -m doctest -v harmonic_series.py
For manual testing run:
python3 harmonic_series.py
"""
def harmonic_series(n_term: str) -> list:
"""Pure Python implementation of Harmonic Series algorithm
:param n_term: The last (nth) term of Harmonic Series
:return: The Harmonic Series starting from 1 to last (nth) term
Examples:
>>> harmonic_series(5)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.0)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.1)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(-5)
[]
>>> harmonic_series(0)
[]
>>> harmonic_series(1)
['1']
"""
if n_term == "":
return n_term
series = []
for temp in range(int(n_term)):
series.append(f"1/{temp + 1}" if series else "1")
return series
if __name__ == "__main__":
nth_term = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| """
This is a pure Python implementation of the Harmonic Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)
For doctests run following command:
python -m doctest -v harmonic_series.py
or
python3 -m doctest -v harmonic_series.py
For manual testing run:
python3 harmonic_series.py
"""
def harmonic_series(n_term: str) -> list:
"""Pure Python implementation of Harmonic Series algorithm
:param n_term: The last (nth) term of Harmonic Series
:return: The Harmonic Series starting from 1 to last (nth) term
Examples:
>>> harmonic_series(5)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.0)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.1)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(-5)
[]
>>> harmonic_series(0)
[]
>>> harmonic_series(1)
['1']
"""
if n_term == "":
return n_term
series = []
for temp in range(int(n_term)):
series.append(f"1/{temp + 1}" if series else "1")
return series
if __name__ == "__main__":
nth_term = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| {
"001": "233168",
"002": "4613732",
"003": "6857",
"004": "906609",
"005": "232792560",
"006": "25164150",
"007": "104743",
"008": "23514624000",
"009": "31875000",
"010": "142913828922",
"011": "70600674",
"012": "76576500",
"013": "5537376230",
"014": "837799",
"015": "137846528820",
"016": "1366",
"017": "21124",
"018": "1074",
"019": "171",
"020": "648",
"021": "31626",
"022": "871198282",
"023": "4179871",
"024": "2783915460",
"025": "4782",
"026": "983",
"027": "-59231",
"028": "669171001",
"029": "9183",
"030": "443839",
"031": "73682",
"032": "45228",
"033": "100",
"034": "40730",
"035": "55",
"036": "872187",
"037": "748317",
"038": "932718654",
"039": "840",
"040": "210",
"041": "7652413",
"042": "162",
"043": "16695334890",
"044": "5482660",
"045": "1533776805",
"046": "5777",
"047": "134043",
"048": "9110846700",
"049": "296962999629",
"050": "997651",
"051": "121313",
"052": "142857",
"053": "4075",
"054": "376",
"055": "249",
"056": "972",
"057": "153",
"058": "26241",
"059": "129448",
"060": "26033",
"061": "28684",
"062": "127035954683",
"063": "49",
"064": "1322",
"065": "272",
"066": "661",
"067": "7273",
"068": "6531031914842725",
"069": "510510",
"070": "8319823",
"071": "428570",
"072": "303963552391",
"073": "7295372",
"074": "402",
"075": "161667",
"076": "190569291",
"077": "71",
"078": "55374",
"079": "73162890",
"080": "40886",
"081": "427337",
"082": "260324",
"083": "425185",
"084": "101524",
"085": "2772",
"086": "1818",
"087": "1097343",
"088": "7587457",
"089": "743",
"090": "1217",
"091": "14234",
"092": "8581146",
"093": "1258",
"094": "518408346",
"095": "14316",
"096": "24702",
"097": "8739992577",
"098": "18769",
"099": "709",
"100": "756872327473",
"101": "37076114526",
"102": "228",
"103": "20313839404245",
"104": "329468",
"105": "73702",
"106": "21384",
"107": "259679",
"108": "180180",
"109": "38182",
"110": "9350130049860600",
"111": "612407567715",
"112": "1587000",
"113": "51161058134250",
"114": "16475640049",
"115": "168",
"116": "20492570929",
"117": "100808458960497",
"118": "44680",
"119": "248155780267521",
"120": "333082500",
"121": "2269",
"122": "1582",
"123": "21035",
"124": "21417",
"125": "2906969179",
"126": "18522",
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| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 random
class Onepad:
def encrypt(self, text: str) -> ([str], [int]):
"""Function to encrypt text using pseudo-random numbers"""
plain = [ord(i) for i in text]
key = []
cipher = []
for i in plain:
k = random.randint(1, 300)
c = (i + k) * k
cipher.append(c)
key.append(k)
return cipher, key
def decrypt(self, cipher: [str], key: [int]) -> str:
"""Function to decrypt text using pseudo-random numbers."""
plain = []
for i in range(len(key)):
p = int((cipher[i] - (key[i]) ** 2) / key[i])
plain.append(chr(p))
plain = "".join([i for i in plain])
return plain
if __name__ == "__main__":
c, k = Onepad().encrypt("Hello")
print(c, k)
print(Onepad().decrypt(c, k))
| import random
class Onepad:
def encrypt(self, text: str) -> ([str], [int]):
"""Function to encrypt text using pseudo-random numbers"""
plain = [ord(i) for i in text]
key = []
cipher = []
for i in plain:
k = random.randint(1, 300)
c = (i + k) * k
cipher.append(c)
key.append(k)
return cipher, key
def decrypt(self, cipher: [str], key: [int]) -> str:
"""Function to decrypt text using pseudo-random numbers."""
plain = []
for i in range(len(key)):
p = int((cipher[i] - (key[i]) ** 2) / key[i])
plain.append(chr(p))
plain = "".join([i for i in plain])
return plain
if __name__ == "__main__":
c, k = Onepad().encrypt("Hello")
print(c, k)
print(Onepad().decrypt(c, k))
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 pure Python implementation of Tabu search algorithm for a Travelling Salesman
Problem, that the distances between the cities are symmetric (the distance between city
'a' and city 'b' is the same between city 'b' and city 'a').
The TSP can be represented into a graph. The cities are represented by nodes and the
distance between them is represented by the weight of the ark between the nodes.
The .txt file with the graph has the form:
node1 node2 distance_between_node1_and_node2
node1 node3 distance_between_node1_and_node3
...
Be careful node1, node2 and the distance between them, must exist only once. This means
in the .txt file should not exist:
node1 node2 distance_between_node1_and_node2
node2 node1 distance_between_node2_and_node1
For pytests run following command:
pytest
For manual testing run:
python tabu_search.py -f your_file_name.txt -number_of_iterations_of_tabu_search \
-s size_of_tabu_search
e.g. python tabu_search.py -f tabudata2.txt -i 4 -s 3
"""
import argparse
import copy
def generate_neighbours(path):
"""
Pure implementation of generating a dictionary of neighbors and the cost with each
neighbor, given a path file that includes a graph.
:param path: The path to the .txt file that includes the graph (e.g.tabudata2.txt)
:return dict_of_neighbours: Dictionary with key each node and value a list of lists
with the neighbors of the node and the cost (distance) for each neighbor.
Example of dict_of_neighbours:
>>) dict_of_neighbours[a]
[[b,20],[c,18],[d,22],[e,26]]
This indicates the neighbors of node (city) 'a', which has neighbor the node 'b'
with distance 20, the node 'c' with distance 18, the node 'd' with distance 22 and
the node 'e' with distance 26.
"""
dict_of_neighbours = {}
with open(path) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_list = list()
_list.append([line.split()[1], line.split()[2]])
dict_of_neighbours[line.split()[0]] = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]]
)
if line.split()[1] not in dict_of_neighbours:
_list = list()
_list.append([line.split()[0], line.split()[2]])
dict_of_neighbours[line.split()[1]] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]]
)
return dict_of_neighbours
def generate_first_solution(path, dict_of_neighbours):
"""
Pure implementation of generating the first solution for the Tabu search to start,
with the redundant resolution strategy. That means that we start from the starting
node (e.g. node 'a'), then we go to the city nearest (lowest distance) to this node
(let's assume is node 'c'), then we go to the nearest city of the node 'c', etc.
till we have visited all cities and return to the starting node.
:param path: The path to the .txt file that includes the graph (e.g.tabudata2.txt)
:param dict_of_neighbours: Dictionary with key each node and value a list of lists
with the neighbors of the node and the cost (distance) for each neighbor.
:return first_solution: The solution for the first iteration of Tabu search using
the redundant resolution strategy in a list.
:return distance_of_first_solution: The total distance that Travelling Salesman
will travel, if he follows the path in first_solution.
"""
with open(path) as f:
start_node = f.read(1)
end_node = start_node
first_solution = []
visiting = start_node
distance_of_first_solution = 0
while visiting not in first_solution:
minim = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1]) < int(minim) and k[0] not in first_solution:
minim = k[1]
best_node = k[0]
first_solution.append(visiting)
distance_of_first_solution = distance_of_first_solution + int(minim)
visiting = best_node
first_solution.append(end_node)
position = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
distance_of_first_solution = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1])
- 10000
)
return first_solution, distance_of_first_solution
def find_neighborhood(solution, dict_of_neighbours):
"""
Pure implementation of generating the neighborhood (sorted by total distance of
each solution from lowest to highest) of a solution with 1-1 exchange method, that
means we exchange each node in a solution with each other node and generating a
number of solution named neighborhood.
:param solution: The solution in which we want to find the neighborhood.
:param dict_of_neighbours: Dictionary with key each node and value a list of lists
with the neighbors of the node and the cost (distance) for each neighbor.
:return neighborhood_of_solution: A list that includes the solutions and the total
distance of each solution (in form of list) that are produced with 1-1 exchange
from the solution that the method took as an input
Example:
>>> find_neighborhood(['a', 'c', 'b', 'd', 'e', 'a'],
... {'a': [['b', '20'], ['c', '18'], ['d', '22'], ['e', '26']],
... 'c': [['a', '18'], ['b', '10'], ['d', '23'], ['e', '24']],
... 'b': [['a', '20'], ['c', '10'], ['d', '11'], ['e', '12']],
... 'e': [['a', '26'], ['b', '12'], ['c', '24'], ['d', '40']],
... 'd': [['a', '22'], ['b', '11'], ['c', '23'], ['e', '40']]}
... ) # doctest: +NORMALIZE_WHITESPACE
[['a', 'e', 'b', 'd', 'c', 'a', 90],
['a', 'c', 'd', 'b', 'e', 'a', 90],
['a', 'd', 'b', 'c', 'e', 'a', 93],
['a', 'c', 'b', 'e', 'd', 'a', 102],
['a', 'c', 'e', 'd', 'b', 'a', 113],
['a', 'b', 'c', 'd', 'e', 'a', 119]]
"""
neighborhood_of_solution = []
for n in solution[1:-1]:
idx1 = solution.index(n)
for kn in solution[1:-1]:
idx2 = solution.index(kn)
if n == kn:
continue
_tmp = copy.deepcopy(solution)
_tmp[idx1] = kn
_tmp[idx2] = n
distance = 0
for k in _tmp[:-1]:
next_node = _tmp[_tmp.index(k) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
distance = distance + int(i[1])
_tmp.append(distance)
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp)
indexOfLastItemInTheList = len(neighborhood_of_solution[0]) - 1
neighborhood_of_solution.sort(key=lambda x: x[indexOfLastItemInTheList])
return neighborhood_of_solution
def tabu_search(
first_solution, distance_of_first_solution, dict_of_neighbours, iters, size
):
"""
Pure implementation of Tabu search algorithm for a Travelling Salesman Problem in
Python.
:param first_solution: The solution for the first iteration of Tabu search using
the redundant resolution strategy in a list.
:param distance_of_first_solution: The total distance that Travelling Salesman will
travel, if he follows the path in first_solution.
:param dict_of_neighbours: Dictionary with key each node and value a list of lists
with the neighbors of the node and the cost (distance) for each neighbor.
:param iters: The number of iterations that Tabu search will execute.
:param size: The size of Tabu List.
:return best_solution_ever: The solution with the lowest distance that occurred
during the execution of Tabu search.
:return best_cost: The total distance that Travelling Salesman will travel, if he
follows the path in best_solution ever.
"""
count = 1
solution = first_solution
tabu_list = list()
best_cost = distance_of_first_solution
best_solution_ever = solution
while count <= iters:
neighborhood = find_neighborhood(solution, dict_of_neighbours)
index_of_best_solution = 0
best_solution = neighborhood[index_of_best_solution]
best_cost_index = len(best_solution) - 1
found = False
while not found:
i = 0
while i < len(best_solution):
if best_solution[i] != solution[i]:
first_exchange_node = best_solution[i]
second_exchange_node = solution[i]
break
i = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node])
found = True
solution = best_solution[:-1]
cost = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
best_cost = cost
best_solution_ever = solution
else:
index_of_best_solution = index_of_best_solution + 1
best_solution = neighborhood[index_of_best_solution]
if len(tabu_list) >= size:
tabu_list.pop(0)
count = count + 1
return best_solution_ever, best_cost
def main(args=None):
dict_of_neighbours = generate_neighbours(args.File)
first_solution, distance_of_first_solution = generate_first_solution(
args.File, dict_of_neighbours
)
best_sol, best_cost = tabu_search(
first_solution,
distance_of_first_solution,
dict_of_neighbours,
args.Iterations,
args.Size,
)
print(f"Best solution: {best_sol}, with total distance: {best_cost}.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| """
This is pure Python implementation of Tabu search algorithm for a Travelling Salesman
Problem, that the distances between the cities are symmetric (the distance between city
'a' and city 'b' is the same between city 'b' and city 'a').
The TSP can be represented into a graph. The cities are represented by nodes and the
distance between them is represented by the weight of the ark between the nodes.
The .txt file with the graph has the form:
node1 node2 distance_between_node1_and_node2
node1 node3 distance_between_node1_and_node3
...
Be careful node1, node2 and the distance between them, must exist only once. This means
in the .txt file should not exist:
node1 node2 distance_between_node1_and_node2
node2 node1 distance_between_node2_and_node1
For pytests run following command:
pytest
For manual testing run:
python tabu_search.py -f your_file_name.txt -number_of_iterations_of_tabu_search \
-s size_of_tabu_search
e.g. python tabu_search.py -f tabudata2.txt -i 4 -s 3
"""
import argparse
import copy
def generate_neighbours(path):
"""
Pure implementation of generating a dictionary of neighbors and the cost with each
neighbor, given a path file that includes a graph.
:param path: The path to the .txt file that includes the graph (e.g.tabudata2.txt)
:return dict_of_neighbours: Dictionary with key each node and value a list of lists
with the neighbors of the node and the cost (distance) for each neighbor.
Example of dict_of_neighbours:
>>) dict_of_neighbours[a]
[[b,20],[c,18],[d,22],[e,26]]
This indicates the neighbors of node (city) 'a', which has neighbor the node 'b'
with distance 20, the node 'c' with distance 18, the node 'd' with distance 22 and
the node 'e' with distance 26.
"""
dict_of_neighbours = {}
with open(path) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_list = list()
_list.append([line.split()[1], line.split()[2]])
dict_of_neighbours[line.split()[0]] = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]]
)
if line.split()[1] not in dict_of_neighbours:
_list = list()
_list.append([line.split()[0], line.split()[2]])
dict_of_neighbours[line.split()[1]] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]]
)
return dict_of_neighbours
def generate_first_solution(path, dict_of_neighbours):
"""
Pure implementation of generating the first solution for the Tabu search to start,
with the redundant resolution strategy. That means that we start from the starting
node (e.g. node 'a'), then we go to the city nearest (lowest distance) to this node
(let's assume is node 'c'), then we go to the nearest city of the node 'c', etc.
till we have visited all cities and return to the starting node.
:param path: The path to the .txt file that includes the graph (e.g.tabudata2.txt)
:param dict_of_neighbours: Dictionary with key each node and value a list of lists
with the neighbors of the node and the cost (distance) for each neighbor.
:return first_solution: The solution for the first iteration of Tabu search using
the redundant resolution strategy in a list.
:return distance_of_first_solution: The total distance that Travelling Salesman
will travel, if he follows the path in first_solution.
"""
with open(path) as f:
start_node = f.read(1)
end_node = start_node
first_solution = []
visiting = start_node
distance_of_first_solution = 0
while visiting not in first_solution:
minim = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1]) < int(minim) and k[0] not in first_solution:
minim = k[1]
best_node = k[0]
first_solution.append(visiting)
distance_of_first_solution = distance_of_first_solution + int(minim)
visiting = best_node
first_solution.append(end_node)
position = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
distance_of_first_solution = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1])
- 10000
)
return first_solution, distance_of_first_solution
def find_neighborhood(solution, dict_of_neighbours):
"""
Pure implementation of generating the neighborhood (sorted by total distance of
each solution from lowest to highest) of a solution with 1-1 exchange method, that
means we exchange each node in a solution with each other node and generating a
number of solution named neighborhood.
:param solution: The solution in which we want to find the neighborhood.
:param dict_of_neighbours: Dictionary with key each node and value a list of lists
with the neighbors of the node and the cost (distance) for each neighbor.
:return neighborhood_of_solution: A list that includes the solutions and the total
distance of each solution (in form of list) that are produced with 1-1 exchange
from the solution that the method took as an input
Example:
>>> find_neighborhood(['a', 'c', 'b', 'd', 'e', 'a'],
... {'a': [['b', '20'], ['c', '18'], ['d', '22'], ['e', '26']],
... 'c': [['a', '18'], ['b', '10'], ['d', '23'], ['e', '24']],
... 'b': [['a', '20'], ['c', '10'], ['d', '11'], ['e', '12']],
... 'e': [['a', '26'], ['b', '12'], ['c', '24'], ['d', '40']],
... 'd': [['a', '22'], ['b', '11'], ['c', '23'], ['e', '40']]}
... ) # doctest: +NORMALIZE_WHITESPACE
[['a', 'e', 'b', 'd', 'c', 'a', 90],
['a', 'c', 'd', 'b', 'e', 'a', 90],
['a', 'd', 'b', 'c', 'e', 'a', 93],
['a', 'c', 'b', 'e', 'd', 'a', 102],
['a', 'c', 'e', 'd', 'b', 'a', 113],
['a', 'b', 'c', 'd', 'e', 'a', 119]]
"""
neighborhood_of_solution = []
for n in solution[1:-1]:
idx1 = solution.index(n)
for kn in solution[1:-1]:
idx2 = solution.index(kn)
if n == kn:
continue
_tmp = copy.deepcopy(solution)
_tmp[idx1] = kn
_tmp[idx2] = n
distance = 0
for k in _tmp[:-1]:
next_node = _tmp[_tmp.index(k) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
distance = distance + int(i[1])
_tmp.append(distance)
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp)
indexOfLastItemInTheList = len(neighborhood_of_solution[0]) - 1
neighborhood_of_solution.sort(key=lambda x: x[indexOfLastItemInTheList])
return neighborhood_of_solution
def tabu_search(
first_solution, distance_of_first_solution, dict_of_neighbours, iters, size
):
"""
Pure implementation of Tabu search algorithm for a Travelling Salesman Problem in
Python.
:param first_solution: The solution for the first iteration of Tabu search using
the redundant resolution strategy in a list.
:param distance_of_first_solution: The total distance that Travelling Salesman will
travel, if he follows the path in first_solution.
:param dict_of_neighbours: Dictionary with key each node and value a list of lists
with the neighbors of the node and the cost (distance) for each neighbor.
:param iters: The number of iterations that Tabu search will execute.
:param size: The size of Tabu List.
:return best_solution_ever: The solution with the lowest distance that occurred
during the execution of Tabu search.
:return best_cost: The total distance that Travelling Salesman will travel, if he
follows the path in best_solution ever.
"""
count = 1
solution = first_solution
tabu_list = list()
best_cost = distance_of_first_solution
best_solution_ever = solution
while count <= iters:
neighborhood = find_neighborhood(solution, dict_of_neighbours)
index_of_best_solution = 0
best_solution = neighborhood[index_of_best_solution]
best_cost_index = len(best_solution) - 1
found = False
while not found:
i = 0
while i < len(best_solution):
if best_solution[i] != solution[i]:
first_exchange_node = best_solution[i]
second_exchange_node = solution[i]
break
i = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node])
found = True
solution = best_solution[:-1]
cost = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
best_cost = cost
best_solution_ever = solution
else:
index_of_best_solution = index_of_best_solution + 1
best_solution = neighborhood[index_of_best_solution]
if len(tabu_list) >= size:
tabu_list.pop(0)
count = count + 1
return best_solution_ever, best_cost
def main(args=None):
dict_of_neighbours = generate_neighbours(args.File)
first_solution, distance_of_first_solution = generate_first_solution(
args.File, dict_of_neighbours
)
best_sol, best_cost = tabu_search(
first_solution,
distance_of_first_solution,
dict_of_neighbours,
args.Iterations,
args.Size,
)
print(f"Best solution: {best_sol}, with total distance: {best_cost}.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 120 Square remainders: https://projecteuler.net/problem=120
Description:
Let r be the remainder when (a−1)^n + (a+1)^n is divided by a^2.
For example, if a = 7 and n = 3, then r = 42: 6^3 + 8^3 = 728 ≡ 42 mod 49.
And as n varies, so too will r, but for a = 7 it turns out that r_max = 42.
For 3 ≤ a ≤ 1000, find ∑ r_max.
Solution:
On expanding the terms, we get 2 if n is even and 2an if n is odd.
For maximizing the value, 2an < a*a => n <= (a - 1)/2 (integer division)
"""
def solution(n: int = 1000) -> int:
"""
Returns ∑ r_max for 3 <= a <= n as explained above
>>> solution(10)
300
>>> solution(100)
330750
>>> solution(1000)
333082500
"""
return sum(2 * a * ((a - 1) // 2) for a in range(3, n + 1))
if __name__ == "__main__":
print(solution())
| """
Problem 120 Square remainders: https://projecteuler.net/problem=120
Description:
Let r be the remainder when (a−1)^n + (a+1)^n is divided by a^2.
For example, if a = 7 and n = 3, then r = 42: 6^3 + 8^3 = 728 ≡ 42 mod 49.
And as n varies, so too will r, but for a = 7 it turns out that r_max = 42.
For 3 ≤ a ≤ 1000, find ∑ r_max.
Solution:
On expanding the terms, we get 2 if n is even and 2an if n is odd.
For maximizing the value, 2an < a*a => n <= (a - 1)/2 (integer division)
"""
def solution(n: int = 1000) -> int:
"""
Returns ∑ r_max for 3 <= a <= n as explained above
>>> solution(10)
300
>>> solution(100)
330750
>>> solution(1000)
333082500
"""
return sum(2 * a * ((a - 1) // 2) for a in range(3, n + 1))
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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
"""
This is pure Python implementation of tree traversal algorithms
"""
from __future__ import annotations
import queue
class TreeNode:
def __init__(self, data):
self.data = data
self.right = None
self.left = None
def build_tree():
print("\n********Press N to stop entering at any point of time********\n")
check = input("Enter the value of the root node: ").strip().lower() or "n"
if check == "n":
return None
q: queue.Queue = queue.Queue()
tree_node = TreeNode(int(check))
q.put(tree_node)
while not q.empty():
node_found = q.get()
msg = "Enter the left node of %s: " % node_found.data
check = input(msg).strip().lower() or "n"
if check == "n":
return tree_node
left_node = TreeNode(int(check))
node_found.left = left_node
q.put(left_node)
msg = "Enter the right node of %s: " % node_found.data
check = input(msg).strip().lower() or "n"
if check == "n":
return tree_node
right_node = TreeNode(int(check))
node_found.right = right_node
q.put(right_node)
def pre_order(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> pre_order(root)
1,2,4,5,3,6,7,
"""
if not isinstance(node, TreeNode) or not node:
return
print(node.data, end=",")
pre_order(node.left)
pre_order(node.right)
def in_order(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> in_order(root)
4,2,5,1,6,3,7,
"""
if not isinstance(node, TreeNode) or not node:
return
in_order(node.left)
print(node.data, end=",")
in_order(node.right)
def post_order(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> post_order(root)
4,5,2,6,7,3,1,
"""
if not isinstance(node, TreeNode) or not node:
return
post_order(node.left)
post_order(node.right)
print(node.data, end=",")
def level_order(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> level_order(root)
1,2,3,4,5,6,7,
"""
if not isinstance(node, TreeNode) or not node:
return
q: queue.Queue = queue.Queue()
q.put(node)
while not q.empty():
node_dequeued = q.get()
print(node_dequeued.data, end=",")
if node_dequeued.left:
q.put(node_dequeued.left)
if node_dequeued.right:
q.put(node_dequeued.right)
def level_order_actual(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> level_order_actual(root)
1,
2,3,
4,5,6,7,
"""
if not isinstance(node, TreeNode) or not node:
return
q: queue.Queue = queue.Queue()
q.put(node)
while not q.empty():
list = []
while not q.empty():
node_dequeued = q.get()
print(node_dequeued.data, end=",")
if node_dequeued.left:
list.append(node_dequeued.left)
if node_dequeued.right:
list.append(node_dequeued.right)
print()
for node in list:
q.put(node)
# iteration version
def pre_order_iter(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> pre_order_iter(root)
1,2,4,5,3,6,7,
"""
if not isinstance(node, TreeNode) or not node:
return
stack: list[TreeNode] = []
n = node
while n or stack:
while n: # start from root node, find its left child
print(n.data, end=",")
stack.append(n)
n = n.left
# end of while means current node doesn't have left child
n = stack.pop()
# start to traverse its right child
n = n.right
def in_order_iter(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> in_order_iter(root)
4,2,5,1,6,3,7,
"""
if not isinstance(node, TreeNode) or not node:
return
stack: list[TreeNode] = []
n = node
while n or stack:
while n:
stack.append(n)
n = n.left
n = stack.pop()
print(n.data, end=",")
n = n.right
def post_order_iter(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> post_order_iter(root)
4,5,2,6,7,3,1,
"""
if not isinstance(node, TreeNode) or not node:
return
stack1, stack2 = [], []
n = node
stack1.append(n)
while stack1: # to find the reversed order of post order, store it in stack2
n = stack1.pop()
if n.left:
stack1.append(n.left)
if n.right:
stack1.append(n.right)
stack2.append(n)
while stack2: # pop up from stack2 will be the post order
print(stack2.pop().data, end=",")
def prompt(s: str = "", width=50, char="*") -> str:
if not s:
return "\n" + width * char
left, extra = divmod(width - len(s) - 2, 2)
return f"{left * char} {s} {(left + extra) * char}"
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt("Binary Tree Traversals"))
node = build_tree()
print(prompt("Pre Order Traversal"))
pre_order(node)
print(prompt() + "\n")
print(prompt("In Order Traversal"))
in_order(node)
print(prompt() + "\n")
print(prompt("Post Order Traversal"))
post_order(node)
print(prompt() + "\n")
print(prompt("Level Order Traversal"))
level_order(node)
print(prompt() + "\n")
print(prompt("Actual Level Order Traversal"))
level_order_actual(node)
print("*" * 50 + "\n")
print(prompt("Pre Order Traversal - Iteration Version"))
pre_order_iter(node)
print(prompt() + "\n")
print(prompt("In Order Traversal - Iteration Version"))
in_order_iter(node)
print(prompt() + "\n")
print(prompt("Post Order Traversal - Iteration Version"))
post_order_iter(node)
print(prompt())
| # flake8: noqa
"""
This is pure Python implementation of tree traversal algorithms
"""
from __future__ import annotations
import queue
class TreeNode:
def __init__(self, data):
self.data = data
self.right = None
self.left = None
def build_tree():
print("\n********Press N to stop entering at any point of time********\n")
check = input("Enter the value of the root node: ").strip().lower() or "n"
if check == "n":
return None
q: queue.Queue = queue.Queue()
tree_node = TreeNode(int(check))
q.put(tree_node)
while not q.empty():
node_found = q.get()
msg = "Enter the left node of %s: " % node_found.data
check = input(msg).strip().lower() or "n"
if check == "n":
return tree_node
left_node = TreeNode(int(check))
node_found.left = left_node
q.put(left_node)
msg = "Enter the right node of %s: " % node_found.data
check = input(msg).strip().lower() or "n"
if check == "n":
return tree_node
right_node = TreeNode(int(check))
node_found.right = right_node
q.put(right_node)
def pre_order(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> pre_order(root)
1,2,4,5,3,6,7,
"""
if not isinstance(node, TreeNode) or not node:
return
print(node.data, end=",")
pre_order(node.left)
pre_order(node.right)
def in_order(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> in_order(root)
4,2,5,1,6,3,7,
"""
if not isinstance(node, TreeNode) or not node:
return
in_order(node.left)
print(node.data, end=",")
in_order(node.right)
def post_order(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> post_order(root)
4,5,2,6,7,3,1,
"""
if not isinstance(node, TreeNode) or not node:
return
post_order(node.left)
post_order(node.right)
print(node.data, end=",")
def level_order(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> level_order(root)
1,2,3,4,5,6,7,
"""
if not isinstance(node, TreeNode) or not node:
return
q: queue.Queue = queue.Queue()
q.put(node)
while not q.empty():
node_dequeued = q.get()
print(node_dequeued.data, end=",")
if node_dequeued.left:
q.put(node_dequeued.left)
if node_dequeued.right:
q.put(node_dequeued.right)
def level_order_actual(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> level_order_actual(root)
1,
2,3,
4,5,6,7,
"""
if not isinstance(node, TreeNode) or not node:
return
q: queue.Queue = queue.Queue()
q.put(node)
while not q.empty():
list = []
while not q.empty():
node_dequeued = q.get()
print(node_dequeued.data, end=",")
if node_dequeued.left:
list.append(node_dequeued.left)
if node_dequeued.right:
list.append(node_dequeued.right)
print()
for node in list:
q.put(node)
# iteration version
def pre_order_iter(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> pre_order_iter(root)
1,2,4,5,3,6,7,
"""
if not isinstance(node, TreeNode) or not node:
return
stack: list[TreeNode] = []
n = node
while n or stack:
while n: # start from root node, find its left child
print(n.data, end=",")
stack.append(n)
n = n.left
# end of while means current node doesn't have left child
n = stack.pop()
# start to traverse its right child
n = n.right
def in_order_iter(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> in_order_iter(root)
4,2,5,1,6,3,7,
"""
if not isinstance(node, TreeNode) or not node:
return
stack: list[TreeNode] = []
n = node
while n or stack:
while n:
stack.append(n)
n = n.left
n = stack.pop()
print(n.data, end=",")
n = n.right
def post_order_iter(node: TreeNode) -> None:
"""
>>> root = TreeNode(1)
>>> tree_node2 = TreeNode(2)
>>> tree_node3 = TreeNode(3)
>>> tree_node4 = TreeNode(4)
>>> tree_node5 = TreeNode(5)
>>> tree_node6 = TreeNode(6)
>>> tree_node7 = TreeNode(7)
>>> root.left, root.right = tree_node2, tree_node3
>>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5
>>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7
>>> post_order_iter(root)
4,5,2,6,7,3,1,
"""
if not isinstance(node, TreeNode) or not node:
return
stack1, stack2 = [], []
n = node
stack1.append(n)
while stack1: # to find the reversed order of post order, store it in stack2
n = stack1.pop()
if n.left:
stack1.append(n.left)
if n.right:
stack1.append(n.right)
stack2.append(n)
while stack2: # pop up from stack2 will be the post order
print(stack2.pop().data, end=",")
def prompt(s: str = "", width=50, char="*") -> str:
if not s:
return "\n" + width * char
left, extra = divmod(width - len(s) - 2, 2)
return f"{left * char} {s} {(left + extra) * char}"
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt("Binary Tree Traversals"))
node = build_tree()
print(prompt("Pre Order Traversal"))
pre_order(node)
print(prompt() + "\n")
print(prompt("In Order Traversal"))
in_order(node)
print(prompt() + "\n")
print(prompt("Post Order Traversal"))
post_order(node)
print(prompt() + "\n")
print(prompt("Level Order Traversal"))
level_order(node)
print(prompt() + "\n")
print(prompt("Actual Level Order Traversal"))
level_order_actual(node)
print("*" * 50 + "\n")
print(prompt("Pre Order Traversal - Iteration Version"))
pre_order_iter(node)
print(prompt() + "\n")
print(prompt("In Order Traversal - Iteration Version"))
in_order_iter(node)
print(prompt() + "\n")
print(prompt("Post Order Traversal - Iteration Version"))
post_order_iter(node)
print(prompt())
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 binascii import hexlify
from hashlib import sha256
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
primes = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class DiffieHellman:
"""
Class to represent the Diffie-Hellman key exchange protocol
>>> alice = DiffieHellman()
>>> bob = DiffieHellman()
>>> alice_private = alice.get_private_key()
>>> alice_public = alice.generate_public_key()
>>> bob_private = bob.get_private_key()
>>> bob_public = bob.generate_public_key()
>>> # generating shared key using the DH object
>>> alice_shared = alice.generate_shared_key(bob_public)
>>> bob_shared = bob.generate_shared_key(alice_public)
>>> assert alice_shared == bob_shared
>>> # generating shared key using static methods
>>> alice_shared = DiffieHellman.generate_shared_key_static(
... alice_private, bob_public
... )
>>> bob_shared = DiffieHellman.generate_shared_key_static(
... bob_private, alice_public
... )
>>> assert alice_shared == bob_shared
"""
# Current minimum recommendation is 2048 bit (group 14)
def __init__(self, group: int = 14) -> None:
if group not in primes:
raise ValueError("Unsupported Group")
self.prime = primes[group]["prime"]
self.generator = primes[group]["generator"]
self.__private_key = int(hexlify(urandom(32)), base=16)
def get_private_key(self) -> str:
return hex(self.__private_key)[2:]
def generate_public_key(self) -> str:
public_key = pow(self.generator, self.__private_key, self.prime)
return hex(public_key)[2:]
def is_valid_public_key(self, key: int) -> bool:
# check if the other public key is valid based on NIST SP800-56
if 2 <= key and key <= self.prime - 2:
if pow(key, (self.prime - 1) // 2, self.prime) == 1:
return True
return False
def generate_shared_key(self, other_key_str: str) -> str:
other_key = int(other_key_str, base=16)
if not self.is_valid_public_key(other_key):
raise ValueError("Invalid public key")
shared_key = pow(other_key, self.__private_key, self.prime)
return sha256(str(shared_key).encode()).hexdigest()
@staticmethod
def is_valid_public_key_static(
local_private_key_str: str, remote_public_key_str: str, prime: int
) -> bool:
# check if the other public key is valid based on NIST SP800-56
if 2 <= remote_public_key_str and remote_public_key_str <= prime - 2:
if pow(remote_public_key_str, (prime - 1) // 2, prime) == 1:
return True
return False
@staticmethod
def generate_shared_key_static(
local_private_key_str: str, remote_public_key_str: str, group: int = 14
) -> str:
local_private_key = int(local_private_key_str, base=16)
remote_public_key = int(remote_public_key_str, base=16)
prime = primes[group]["prime"]
if not DiffieHellman.is_valid_public_key_static(
local_private_key, remote_public_key, prime
):
raise ValueError("Invalid public key")
shared_key = pow(remote_public_key, local_private_key, prime)
return sha256(str(shared_key).encode()).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| from binascii import hexlify
from hashlib import sha256
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
primes = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class DiffieHellman:
"""
Class to represent the Diffie-Hellman key exchange protocol
>>> alice = DiffieHellman()
>>> bob = DiffieHellman()
>>> alice_private = alice.get_private_key()
>>> alice_public = alice.generate_public_key()
>>> bob_private = bob.get_private_key()
>>> bob_public = bob.generate_public_key()
>>> # generating shared key using the DH object
>>> alice_shared = alice.generate_shared_key(bob_public)
>>> bob_shared = bob.generate_shared_key(alice_public)
>>> assert alice_shared == bob_shared
>>> # generating shared key using static methods
>>> alice_shared = DiffieHellman.generate_shared_key_static(
... alice_private, bob_public
... )
>>> bob_shared = DiffieHellman.generate_shared_key_static(
... bob_private, alice_public
... )
>>> assert alice_shared == bob_shared
"""
# Current minimum recommendation is 2048 bit (group 14)
def __init__(self, group: int = 14) -> None:
if group not in primes:
raise ValueError("Unsupported Group")
self.prime = primes[group]["prime"]
self.generator = primes[group]["generator"]
self.__private_key = int(hexlify(urandom(32)), base=16)
def get_private_key(self) -> str:
return hex(self.__private_key)[2:]
def generate_public_key(self) -> str:
public_key = pow(self.generator, self.__private_key, self.prime)
return hex(public_key)[2:]
def is_valid_public_key(self, key: int) -> bool:
# check if the other public key is valid based on NIST SP800-56
if 2 <= key and key <= self.prime - 2:
if pow(key, (self.prime - 1) // 2, self.prime) == 1:
return True
return False
def generate_shared_key(self, other_key_str: str) -> str:
other_key = int(other_key_str, base=16)
if not self.is_valid_public_key(other_key):
raise ValueError("Invalid public key")
shared_key = pow(other_key, self.__private_key, self.prime)
return sha256(str(shared_key).encode()).hexdigest()
@staticmethod
def is_valid_public_key_static(
local_private_key_str: str, remote_public_key_str: str, prime: int
) -> bool:
# check if the other public key is valid based on NIST SP800-56
if 2 <= remote_public_key_str and remote_public_key_str <= prime - 2:
if pow(remote_public_key_str, (prime - 1) // 2, prime) == 1:
return True
return False
@staticmethod
def generate_shared_key_static(
local_private_key_str: str, remote_public_key_str: str, group: int = 14
) -> str:
local_private_key = int(local_private_key_str, base=16)
remote_public_key = int(remote_public_key_str, base=16)
prime = primes[group]["prime"]
if not DiffieHellman.is_valid_public_key_static(
local_private_key, remote_public_key, prime
):
raise ValueError("Invalid public key")
shared_key = pow(remote_public_key, local_private_key, prime)
return sha256(str(shared_key).encode()).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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}`.
| """
Ugly numbers are numbers whose only prime factors are 2, 3 or 5. The sequence
1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, … shows the first 11 ugly numbers. By convention,
1 is included.
Given an integer n, we have to find the nth ugly number.
For more details, refer this article
https://www.geeksforgeeks.org/ugly-numbers/
"""
def ugly_numbers(n: int) -> int:
"""
Returns the nth ugly number.
>>> ugly_numbers(100)
1536
>>> ugly_numbers(0)
1
>>> ugly_numbers(20)
36
>>> ugly_numbers(-5)
1
>>> ugly_numbers(-5.5)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
"""
ugly_nums = [1]
i2, i3, i5 = 0, 0, 0
next_2 = ugly_nums[i2] * 2
next_3 = ugly_nums[i3] * 3
next_5 = ugly_nums[i5] * 5
for i in range(1, n):
next_num = min(next_2, next_3, next_5)
ugly_nums.append(next_num)
if next_num == next_2:
i2 += 1
next_2 = ugly_nums[i2] * 2
if next_num == next_3:
i3 += 1
next_3 = ugly_nums[i3] * 3
if next_num == next_5:
i5 += 1
next_5 = ugly_nums[i5] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f"{ugly_numbers(200) = }")
| """
Ugly numbers are numbers whose only prime factors are 2, 3 or 5. The sequence
1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, … shows the first 11 ugly numbers. By convention,
1 is included.
Given an integer n, we have to find the nth ugly number.
For more details, refer this article
https://www.geeksforgeeks.org/ugly-numbers/
"""
def ugly_numbers(n: int) -> int:
"""
Returns the nth ugly number.
>>> ugly_numbers(100)
1536
>>> ugly_numbers(0)
1
>>> ugly_numbers(20)
36
>>> ugly_numbers(-5)
1
>>> ugly_numbers(-5.5)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
"""
ugly_nums = [1]
i2, i3, i5 = 0, 0, 0
next_2 = ugly_nums[i2] * 2
next_3 = ugly_nums[i3] * 3
next_5 = ugly_nums[i5] * 5
for i in range(1, n):
next_num = min(next_2, next_3, next_5)
ugly_nums.append(next_num)
if next_num == next_2:
i2 += 1
next_2 = ugly_nums[i2] * 2
if next_num == next_3:
i3 += 1
next_3 = ugly_nums[i3] * 3
if next_num == next_5:
i5 += 1
next_5 = ugly_nums[i5] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f"{ugly_numbers(200) = }")
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 datetime import datetime
import requests
def download_video(url: str) -> bytes:
base_url = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
video_url = requests.get(base_url + url).json()[0]["urls"][0]["src"]
return requests.get(video_url).content
if __name__ == "__main__":
url = input("Enter Video/IGTV url: ").strip()
file_name = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(f"Done. Video saved to disk as {file_name}.")
| from datetime import datetime
import requests
def download_video(url: str) -> bytes:
base_url = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
video_url = requests.get(base_url + url).json()[0]["urls"][0]["src"]
return requests.get(video_url).content
if __name__ == "__main__":
url = input("Enter Video/IGTV url: ").strip()
file_name = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(f"Done. Video saved to disk as {file_name}.")
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 stock span problem is a financial problem where we have a series of n daily
price quotes for a stock and we need to calculate span of stock's price for all n days.
The span Si of the stock's price on a given day i is defined as the maximum
number of consecutive days just before the given day, for which the price of the stock
on the current day is less than or equal to its price on the given day.
"""
def calculateSpan(price, S):
n = len(price)
# Create a stack and push index of fist element to it
st = []
st.append(0)
# Span value of first element is always 1
S[0] = 1
# Calculate span values for rest of the elements
for i in range(1, n):
# Pop elements from stack while stack is not
# empty and top of stack is smaller than price[i]
while len(st) > 0 and price[st[0]] <= price[i]:
st.pop()
# If stack becomes empty, then price[i] is greater
# than all elements on left of it, i.e. price[0],
# price[1], ..price[i-1]. Else the price[i] is
# greater than elements after top of stack
S[i] = i + 1 if len(st) <= 0 else (i - st[0])
# Push this element to stack
st.append(i)
# A utility function to print elements of array
def printArray(arr, n):
for i in range(0, n):
print(arr[i], end=" ")
# Driver program to test above function
price = [10, 4, 5, 90, 120, 80]
S = [0 for i in range(len(price) + 1)]
# Fill the span values in array S[]
calculateSpan(price, S)
# Print the calculated span values
printArray(S, len(price))
| """
The stock span problem is a financial problem where we have a series of n daily
price quotes for a stock and we need to calculate span of stock's price for all n days.
The span Si of the stock's price on a given day i is defined as the maximum
number of consecutive days just before the given day, for which the price of the stock
on the current day is less than or equal to its price on the given day.
"""
def calculateSpan(price, S):
n = len(price)
# Create a stack and push index of fist element to it
st = []
st.append(0)
# Span value of first element is always 1
S[0] = 1
# Calculate span values for rest of the elements
for i in range(1, n):
# Pop elements from stack while stack is not
# empty and top of stack is smaller than price[i]
while len(st) > 0 and price[st[0]] <= price[i]:
st.pop()
# If stack becomes empty, then price[i] is greater
# than all elements on left of it, i.e. price[0],
# price[1], ..price[i-1]. Else the price[i] is
# greater than elements after top of stack
S[i] = i + 1 if len(st) <= 0 else (i - st[0])
# Push this element to stack
st.append(i)
# A utility function to print elements of array
def printArray(arr, n):
for i in range(0, n):
print(arr[i], end=" ")
# Driver program to test above function
price = [10, 4, 5, 90, 120, 80]
S = [0 for i in range(len(price) + 1)]
# Fill the span values in array S[]
calculateSpan(price, S)
# Print the calculated span values
printArray(S, len(price))
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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__ = "Tobias Carryer"
from time import time
class LinearCongruentialGenerator:
"""
A pseudorandom number generator.
"""
def __init__(self, multiplier, increment, modulo, seed=int(time())):
"""
These parameters are saved and used when nextNumber() is called.
modulo is the largest number that can be generated (exclusive). The most
efficient values are powers of 2. 2^32 is a common value.
"""
self.multiplier = multiplier
self.increment = increment
self.modulo = modulo
self.seed = seed
def next_number(self):
"""
The smallest number that can be generated is zero.
The largest number that can be generated is modulo-1. modulo is set in the
constructor.
"""
self.seed = (self.multiplier * self.seed + self.increment) % self.modulo
return self.seed
if __name__ == "__main__":
# Show the LCG in action.
lcg = LinearCongruentialGenerator(1664525, 1013904223, 2 << 31)
while True:
print(lcg.next_number())
| __author__ = "Tobias Carryer"
from time import time
class LinearCongruentialGenerator:
"""
A pseudorandom number generator.
"""
def __init__(self, multiplier, increment, modulo, seed=int(time())):
"""
These parameters are saved and used when nextNumber() is called.
modulo is the largest number that can be generated (exclusive). The most
efficient values are powers of 2. 2^32 is a common value.
"""
self.multiplier = multiplier
self.increment = increment
self.modulo = modulo
self.seed = seed
def next_number(self):
"""
The smallest number that can be generated is zero.
The largest number that can be generated is modulo-1. modulo is set in the
constructor.
"""
self.seed = (self.multiplier * self.seed + self.increment) % self.modulo
return self.seed
if __name__ == "__main__":
# Show the LCG in action.
lcg = LinearCongruentialGenerator(1664525, 1013904223, 2 << 31)
while True:
print(lcg.next_number())
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 on Mon Feb 26 14:29:11 2018
@author: Christian Bender
@license: MIT-license
This module contains some useful classes and functions for dealing
with linear algebra in python.
Overview:
- class Vector
- function zeroVector(dimension)
- function unitBasisVector(dimension,pos)
- function axpy(scalar,vector1,vector2)
- function randomVector(N,a,b)
- class Matrix
- function squareZeroMatrix(N)
- function randomMatrix(W,H,a,b)
"""
import math
import random
class Vector:
"""
This class represents a vector of arbitrary size.
You need to give the vector components.
Overview about the methods:
constructor(components : list) : init the vector
set(components : list) : changes the vector components.
__str__() : toString method
component(i : int): gets the i-th component (start by 0)
__len__() : gets the size of the vector (number of components)
euclidLength() : returns the euclidean length of the vector.
operator + : vector addition
operator - : vector subtraction
operator * : scalar multiplication and dot product
copy() : copies this vector and returns it.
changeComponent(pos,value) : changes the specified component.
TODO: compare-operator
"""
def __init__(self, components=None):
"""
input: components or nothing
simple constructor for init the vector
"""
if components is None:
components = []
self.__components = list(components)
def set(self, components):
"""
input: new components
changes the components of the vector.
replace the components with newer one.
"""
if len(components) > 0:
self.__components = list(components)
else:
raise Exception("please give any vector")
def __str__(self):
"""
returns a string representation of the vector
"""
return "(" + ",".join(map(str, self.__components)) + ")"
def component(self, i):
"""
input: index (start at 0)
output: the i-th component of the vector.
"""
if type(i) is int and -len(self.__components) <= i < len(self.__components):
return self.__components[i]
else:
raise Exception("index out of range")
def __len__(self):
"""
returns the size of the vector
"""
return len(self.__components)
def euclidLength(self):
"""
returns the euclidean length of the vector
"""
summe = 0
for c in self.__components:
summe += c ** 2
return math.sqrt(summe)
def __add__(self, other):
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the sum.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] + other.component(i) for i in range(size)]
return Vector(result)
else:
raise Exception("must have the same size")
def __sub__(self, other):
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the difference.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] - other.component(i) for i in range(size)]
return Vector(result)
else: # error case
raise Exception("must have the same size")
def __mul__(self, other):
"""
mul implements the scalar multiplication
and the dot-product
"""
if isinstance(other, float) or isinstance(other, int):
ans = [c * other for c in self.__components]
return Vector(ans)
elif isinstance(other, Vector) and (len(self) == len(other)):
size = len(self)
summe = 0
for i in range(size):
summe += self.__components[i] * other.component(i)
return summe
else: # error case
raise Exception("invalid operand!")
def copy(self):
"""
copies this vector and returns it.
"""
return Vector(self.__components)
def changeComponent(self, pos, value):
"""
input: an index (pos) and a value
changes the specified component (pos) with the
'value'
"""
# precondition
assert -len(self.__components) <= pos < len(self.__components)
self.__components[pos] = value
def zeroVector(dimension):
"""
returns a zero-vector of size 'dimension'
"""
# precondition
assert isinstance(dimension, int)
return Vector([0] * dimension)
def unitBasisVector(dimension, pos):
"""
returns a unit basis vector with a One
at index 'pos' (indexing at 0)
"""
# precondition
assert isinstance(dimension, int) and (isinstance(pos, int))
ans = [0] * dimension
ans[pos] = 1
return Vector(ans)
def axpy(scalar, x, y):
"""
input: a 'scalar' and two vectors 'x' and 'y'
output: a vector
computes the axpy operation
"""
# precondition
assert (
isinstance(x, Vector)
and (isinstance(y, Vector))
and (isinstance(scalar, int) or isinstance(scalar, float))
)
return x * scalar + y
def randomVector(N, a, b):
"""
input: size (N) of the vector.
random range (a,b)
output: returns a random vector of size N, with
random integer components between 'a' and 'b'.
"""
random.seed(None)
ans = [random.randint(a, b) for i in range(N)]
return Vector(ans)
class Matrix:
"""
class: Matrix
This class represents a arbitrary matrix.
Overview about the methods:
__str__() : returns a string representation
operator * : implements the matrix vector multiplication
implements the matrix-scalar multiplication.
changeComponent(x,y,value) : changes the specified component.
component(x,y) : returns the specified component.
width() : returns the width of the matrix
height() : returns the height of the matrix
operator + : implements the matrix-addition.
operator - _ implements the matrix-subtraction
"""
def __init__(self, matrix, w, h):
"""
simple constructor for initializing
the matrix with components.
"""
self.__matrix = matrix
self.__width = w
self.__height = h
def __str__(self):
"""
returns a string representation of this
matrix.
"""
ans = ""
for i in range(self.__height):
ans += "|"
for j in range(self.__width):
if j < self.__width - 1:
ans += str(self.__matrix[i][j]) + ","
else:
ans += str(self.__matrix[i][j]) + "|\n"
return ans
def changeComponent(self, x, y, value):
"""
changes the x-y component of this matrix
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
self.__matrix[x][y] = value
else:
raise Exception("changeComponent: indices out of bounds")
def component(self, x, y):
"""
returns the specified (x,y) component
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("changeComponent: indices out of bounds")
def width(self):
"""
getter for the width
"""
return self.__width
def height(self):
"""
getter for the height
"""
return self.__height
def determinate(self) -> float:
"""
returns the determinate of an nxn matrix using Laplace expansion
"""
if self.__height == self.__width and self.__width >= 2:
total = 0
if self.__width > 2:
for x in range(0, self.__width):
for y in range(0, self.__height):
total += (
self.__matrix[x][y]
* (-1) ** (x + y)
* Matrix(
self.__matrix[0:x] + self.__matrix[x + 1 :],
self.__width - 1,
self.__height - 1,
).determinate()
)
else:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
return total
else:
raise Exception("matrix is not square")
def __mul__(self, other):
"""
implements the matrix-vector multiplication.
implements the matrix-scalar multiplication
"""
if isinstance(other, Vector): # vector-matrix
if len(other) == self.__width:
ans = zeroVector(self.__height)
for i in range(self.__height):
summe = 0
for j in range(self.__width):
summe += other.component(j) * self.__matrix[i][j]
ans.changeComponent(i, summe)
summe = 0
return ans
else:
raise Exception(
"vector must have the same size as the "
+ "number of columns of the matrix!"
)
elif isinstance(other, int) or isinstance(other, float): # matrix-scalar
matrix = [
[self.__matrix[i][j] * other for j in range(self.__width)]
for i in range(self.__height)
]
return Matrix(matrix, self.__width, self.__height)
def __add__(self, other):
"""
implements the matrix-addition.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = []
for j in range(self.__width):
row.append(self.__matrix[i][j] + other.component(i, j))
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrix must have the same dimension!")
def __sub__(self, other):
"""
implements the matrix-subtraction.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = []
for j in range(self.__width):
row.append(self.__matrix[i][j] - other.component(i, j))
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrix must have the same dimension!")
def squareZeroMatrix(N):
"""
returns a square zero-matrix of dimension NxN
"""
ans = [[0] * N for i in range(N)]
return Matrix(ans, N, N)
def randomMatrix(W, H, a, b):
"""
returns a random matrix WxH with integer components
between 'a' and 'b'
"""
random.seed(None)
matrix = [[random.randint(a, b) for j in range(W)] for i in range(H)]
return Matrix(matrix, W, H)
| """
Created on Mon Feb 26 14:29:11 2018
@author: Christian Bender
@license: MIT-license
This module contains some useful classes and functions for dealing
with linear algebra in python.
Overview:
- class Vector
- function zeroVector(dimension)
- function unitBasisVector(dimension,pos)
- function axpy(scalar,vector1,vector2)
- function randomVector(N,a,b)
- class Matrix
- function squareZeroMatrix(N)
- function randomMatrix(W,H,a,b)
"""
import math
import random
class Vector:
"""
This class represents a vector of arbitrary size.
You need to give the vector components.
Overview about the methods:
constructor(components : list) : init the vector
set(components : list) : changes the vector components.
__str__() : toString method
component(i : int): gets the i-th component (start by 0)
__len__() : gets the size of the vector (number of components)
euclidLength() : returns the euclidean length of the vector.
operator + : vector addition
operator - : vector subtraction
operator * : scalar multiplication and dot product
copy() : copies this vector and returns it.
changeComponent(pos,value) : changes the specified component.
TODO: compare-operator
"""
def __init__(self, components=None):
"""
input: components or nothing
simple constructor for init the vector
"""
if components is None:
components = []
self.__components = list(components)
def set(self, components):
"""
input: new components
changes the components of the vector.
replace the components with newer one.
"""
if len(components) > 0:
self.__components = list(components)
else:
raise Exception("please give any vector")
def __str__(self):
"""
returns a string representation of the vector
"""
return "(" + ",".join(map(str, self.__components)) + ")"
def component(self, i):
"""
input: index (start at 0)
output: the i-th component of the vector.
"""
if type(i) is int and -len(self.__components) <= i < len(self.__components):
return self.__components[i]
else:
raise Exception("index out of range")
def __len__(self):
"""
returns the size of the vector
"""
return len(self.__components)
def euclidLength(self):
"""
returns the euclidean length of the vector
"""
summe = 0
for c in self.__components:
summe += c ** 2
return math.sqrt(summe)
def __add__(self, other):
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the sum.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] + other.component(i) for i in range(size)]
return Vector(result)
else:
raise Exception("must have the same size")
def __sub__(self, other):
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the difference.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] - other.component(i) for i in range(size)]
return Vector(result)
else: # error case
raise Exception("must have the same size")
def __mul__(self, other):
"""
mul implements the scalar multiplication
and the dot-product
"""
if isinstance(other, float) or isinstance(other, int):
ans = [c * other for c in self.__components]
return Vector(ans)
elif isinstance(other, Vector) and (len(self) == len(other)):
size = len(self)
summe = 0
for i in range(size):
summe += self.__components[i] * other.component(i)
return summe
else: # error case
raise Exception("invalid operand!")
def copy(self):
"""
copies this vector and returns it.
"""
return Vector(self.__components)
def changeComponent(self, pos, value):
"""
input: an index (pos) and a value
changes the specified component (pos) with the
'value'
"""
# precondition
assert -len(self.__components) <= pos < len(self.__components)
self.__components[pos] = value
def zeroVector(dimension):
"""
returns a zero-vector of size 'dimension'
"""
# precondition
assert isinstance(dimension, int)
return Vector([0] * dimension)
def unitBasisVector(dimension, pos):
"""
returns a unit basis vector with a One
at index 'pos' (indexing at 0)
"""
# precondition
assert isinstance(dimension, int) and (isinstance(pos, int))
ans = [0] * dimension
ans[pos] = 1
return Vector(ans)
def axpy(scalar, x, y):
"""
input: a 'scalar' and two vectors 'x' and 'y'
output: a vector
computes the axpy operation
"""
# precondition
assert (
isinstance(x, Vector)
and (isinstance(y, Vector))
and (isinstance(scalar, int) or isinstance(scalar, float))
)
return x * scalar + y
def randomVector(N, a, b):
"""
input: size (N) of the vector.
random range (a,b)
output: returns a random vector of size N, with
random integer components between 'a' and 'b'.
"""
random.seed(None)
ans = [random.randint(a, b) for i in range(N)]
return Vector(ans)
class Matrix:
"""
class: Matrix
This class represents a arbitrary matrix.
Overview about the methods:
__str__() : returns a string representation
operator * : implements the matrix vector multiplication
implements the matrix-scalar multiplication.
changeComponent(x,y,value) : changes the specified component.
component(x,y) : returns the specified component.
width() : returns the width of the matrix
height() : returns the height of the matrix
operator + : implements the matrix-addition.
operator - _ implements the matrix-subtraction
"""
def __init__(self, matrix, w, h):
"""
simple constructor for initializing
the matrix with components.
"""
self.__matrix = matrix
self.__width = w
self.__height = h
def __str__(self):
"""
returns a string representation of this
matrix.
"""
ans = ""
for i in range(self.__height):
ans += "|"
for j in range(self.__width):
if j < self.__width - 1:
ans += str(self.__matrix[i][j]) + ","
else:
ans += str(self.__matrix[i][j]) + "|\n"
return ans
def changeComponent(self, x, y, value):
"""
changes the x-y component of this matrix
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
self.__matrix[x][y] = value
else:
raise Exception("changeComponent: indices out of bounds")
def component(self, x, y):
"""
returns the specified (x,y) component
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("changeComponent: indices out of bounds")
def width(self):
"""
getter for the width
"""
return self.__width
def height(self):
"""
getter for the height
"""
return self.__height
def determinate(self) -> float:
"""
returns the determinate of an nxn matrix using Laplace expansion
"""
if self.__height == self.__width and self.__width >= 2:
total = 0
if self.__width > 2:
for x in range(0, self.__width):
for y in range(0, self.__height):
total += (
self.__matrix[x][y]
* (-1) ** (x + y)
* Matrix(
self.__matrix[0:x] + self.__matrix[x + 1 :],
self.__width - 1,
self.__height - 1,
).determinate()
)
else:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
return total
else:
raise Exception("matrix is not square")
def __mul__(self, other):
"""
implements the matrix-vector multiplication.
implements the matrix-scalar multiplication
"""
if isinstance(other, Vector): # vector-matrix
if len(other) == self.__width:
ans = zeroVector(self.__height)
for i in range(self.__height):
summe = 0
for j in range(self.__width):
summe += other.component(j) * self.__matrix[i][j]
ans.changeComponent(i, summe)
summe = 0
return ans
else:
raise Exception(
"vector must have the same size as the "
+ "number of columns of the matrix!"
)
elif isinstance(other, int) or isinstance(other, float): # matrix-scalar
matrix = [
[self.__matrix[i][j] * other for j in range(self.__width)]
for i in range(self.__height)
]
return Matrix(matrix, self.__width, self.__height)
def __add__(self, other):
"""
implements the matrix-addition.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = []
for j in range(self.__width):
row.append(self.__matrix[i][j] + other.component(i, j))
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrix must have the same dimension!")
def __sub__(self, other):
"""
implements the matrix-subtraction.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = []
for j in range(self.__width):
row.append(self.__matrix[i][j] - other.component(i, j))
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrix must have the same dimension!")
def squareZeroMatrix(N):
"""
returns a square zero-matrix of dimension NxN
"""
ans = [[0] * N for i in range(N)]
return Matrix(ans, N, N)
def randomMatrix(W, H, a, b):
"""
returns a random matrix WxH with integer components
between 'a' and 'b'
"""
random.seed(None)
matrix = [[random.randint(a, b) for j in range(W)] for i in range(H)]
return Matrix(matrix, W, H)
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -,16,12,21,-,-,-
16,-,-,17,20,-,-
12,-,-,28,-,31,-
21,17,28,-,18,19,23
-,20,-,18,-,-,11
-,-,31,19,-,-,27
-,-,-,23,11,27,-
| -,16,12,21,-,-,-
16,-,-,17,20,-,-
12,-,-,28,-,31,-
21,17,28,-,18,19,23
-,20,-,18,-,-,11
-,-,31,19,-,-,27
-,-,-,23,11,27,-
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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 random
class Dice:
NUM_SIDES = 6
def __init__(self):
""" Initialize a six sided dice """
self.sides = list(range(1, Dice.NUM_SIDES + 1))
def roll(self):
return random.choice(self.sides)
def _str_(self):
return "Fair Dice"
def throw_dice(num_throws: int, num_dice: int = 2) -> list[float]:
"""
Return probability list of all possible sums when throwing dice.
>>> random.seed(0)
>>> throw_dice(10, 1)
[10.0, 0.0, 30.0, 50.0, 10.0, 0.0]
>>> throw_dice(100, 1)
[19.0, 17.0, 17.0, 11.0, 23.0, 13.0]
>>> throw_dice(1000, 1)
[18.8, 15.5, 16.3, 17.6, 14.2, 17.6]
>>> throw_dice(10000, 1)
[16.35, 16.89, 16.93, 16.6, 16.52, 16.71]
>>> throw_dice(10000, 2)
[2.74, 5.6, 7.99, 11.26, 13.92, 16.7, 14.44, 10.63, 8.05, 5.92, 2.75]
"""
dices = [Dice() for i in range(num_dice)]
count_of_sum = [0] * (len(dices) * Dice.NUM_SIDES + 1)
for i in range(num_throws):
count_of_sum[sum(dice.roll() for dice in dices)] += 1
probability = [round((count * 100) / num_throws, 2) for count in count_of_sum]
return probability[num_dice:] # remove probability of sums that never appear
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
import random
class Dice:
NUM_SIDES = 6
def __init__(self):
""" Initialize a six sided dice """
self.sides = list(range(1, Dice.NUM_SIDES + 1))
def roll(self):
return random.choice(self.sides)
def _str_(self):
return "Fair Dice"
def throw_dice(num_throws: int, num_dice: int = 2) -> list[float]:
"""
Return probability list of all possible sums when throwing dice.
>>> random.seed(0)
>>> throw_dice(10, 1)
[10.0, 0.0, 30.0, 50.0, 10.0, 0.0]
>>> throw_dice(100, 1)
[19.0, 17.0, 17.0, 11.0, 23.0, 13.0]
>>> throw_dice(1000, 1)
[18.8, 15.5, 16.3, 17.6, 14.2, 17.6]
>>> throw_dice(10000, 1)
[16.35, 16.89, 16.93, 16.6, 16.52, 16.71]
>>> throw_dice(10000, 2)
[2.74, 5.6, 7.99, 11.26, 13.92, 16.7, 14.44, 10.63, 8.05, 5.92, 2.75]
"""
dices = [Dice() for i in range(num_dice)]
count_of_sum = [0] * (len(dices) * Dice.NUM_SIDES + 1)
for i in range(num_throws):
count_of_sum[sum(dice.roll() for dice in dices)] += 1
probability = [round((count * 100) / num_throws, 2) for count in count_of_sum]
return probability[num_dice:] # remove probability of sums that never appear
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **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}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,305 | [mypy] fix hashes folder | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T08:55:40Z" | "2021-04-03T08:01:46Z" | 5229c749553d9ec65d455e0183a574e45ac3e73e | a53fcf221bc1d361b26c055aa12be38c4e8b2022 | [mypy] fix hashes folder. ### **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/Strongly_connected_component
Finding strongly connected components in directed graph
"""
test_graph_1 = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
test_graph_2 = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def topology_sort(graph: dict, vert: int, visited: list) -> list:
"""
Use depth first search to sort graph
At this time graph is the same as input
>>> topology_sort(test_graph_1, 0, 5 * [False])
[1, 2, 4, 3, 0]
>>> topology_sort(test_graph_2, 0, 6 * [False])
[2, 1, 5, 4, 3, 0]
"""
visited[vert] = True
order = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(graph, neighbour, visited)
order.append(vert)
return order
def find_components(reversed_graph: dict, vert: int, visited: list) -> list:
"""
Use depth first search to find strongliy connected
vertices. Now graph is reversed
>>> find_components({0: [1], 1: [2], 2: [0]}, 0, 5 * [False])
[0, 1, 2]
>>> find_components({0: [2], 1: [0], 2: [0, 1]}, 0, 6 * [False])
[0, 2, 1]
"""
visited[vert] = True
component = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(reversed_graph, neighbour, visited)
return component
def strongly_connected_components(graph: dict) -> list:
"""
This function takes graph as a parameter
and then returns the list of strongly connected components
>>> strongly_connected_components(test_graph_1)
[[0, 1, 2], [3], [4]]
>>> strongly_connected_components(test_graph_2)
[[0, 2, 1], [3, 5, 4]]
"""
visited = len(graph) * [False]
reversed_graph = {vert: [] for vert in range(len(graph))}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(vert)
order = []
for i, was_visited in enumerate(visited):
if not was_visited:
order += topology_sort(graph, i, visited)
components_list = []
visited = len(graph) * [False]
for i in range(len(graph)):
vert = order[len(graph) - i - 1]
if not visited[vert]:
component = find_components(reversed_graph, vert, visited)
components_list.append(component)
return components_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
https://en.wikipedia.org/wiki/Strongly_connected_component
Finding strongly connected components in directed graph
"""
test_graph_1 = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
test_graph_2 = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def topology_sort(graph: dict, vert: int, visited: list) -> list:
"""
Use depth first search to sort graph
At this time graph is the same as input
>>> topology_sort(test_graph_1, 0, 5 * [False])
[1, 2, 4, 3, 0]
>>> topology_sort(test_graph_2, 0, 6 * [False])
[2, 1, 5, 4, 3, 0]
"""
visited[vert] = True
order = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(graph, neighbour, visited)
order.append(vert)
return order
def find_components(reversed_graph: dict, vert: int, visited: list) -> list:
"""
Use depth first search to find strongliy connected
vertices. Now graph is reversed
>>> find_components({0: [1], 1: [2], 2: [0]}, 0, 5 * [False])
[0, 1, 2]
>>> find_components({0: [2], 1: [0], 2: [0, 1]}, 0, 6 * [False])
[0, 2, 1]
"""
visited[vert] = True
component = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(reversed_graph, neighbour, visited)
return component
def strongly_connected_components(graph: dict) -> list:
"""
This function takes graph as a parameter
and then returns the list of strongly connected components
>>> strongly_connected_components(test_graph_1)
[[0, 1, 2], [3], [4]]
>>> strongly_connected_components(test_graph_2)
[[0, 2, 1], [3, 5, 4]]
"""
visited = len(graph) * [False]
reversed_graph = {vert: [] for vert in range(len(graph))}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(vert)
order = []
for i, was_visited in enumerate(visited):
if not was_visited:
order += topology_sort(graph, i, visited)
components_list = []
visited = len(graph) * [False]
for i in range(len(graph)):
vert = order[len(graph) - i - 1]
if not visited[vert]:
component = find_components(reversed_graph, vert, visited)
components_list.append(component)
return components_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,304 | [mypy] Fix directory arithmetic_analysis | ### **Describe your change:**
Related Issue: #4052
* [ ] 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}`.
| algobytewise | "2021-04-02T07:15:46Z" | "2021-04-02T07:32:13Z" | 895bca36541598a04dba525568a20d2282e0ffd9 | 5229c749553d9ec65d455e0183a574e45ac3e73e | [mypy] Fix directory arithmetic_analysis. ### **Describe your change:**
Related Issue: #4052
* [ ] 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 '(arithmetic_analysis|ciphers|conversions|data_structures|digital_image_processing|dynamic_programming|graphs|hashes|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 '(ciphers|conversions|data_structures|digital_image_processing|dynamic_programming|graphs|hashes|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: ${{ success() }}
run: scripts/build_directory_md.py 2>&1 | tee DIRECTORY.md
| 1 |
TheAlgorithms/Python | 4,304 | [mypy] Fix directory arithmetic_analysis | ### **Describe your change:**
Related Issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T07:15:46Z" | "2021-04-02T07:32:13Z" | 895bca36541598a04dba525568a20d2282e0ffd9 | 5229c749553d9ec65d455e0183a574e45ac3e73e | [mypy] Fix directory arithmetic_analysis. ### **Describe your change:**
Related Issue: #4052
* [ ] 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: pre-commit
on: [push, pull_request]
jobs:
pre-commit:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/cache@v2
with:
path: |
~/.cache/pre-commit
~/.cache/pip
key: ${{ runner.os }}-pre-commit-${{ hashFiles('.pre-commit-config.yaml') }}
- uses: actions/setup-python@v2
- uses: psf/black@stable
- name: Install pre-commit
run: |
python -m pip install --upgrade pip
python -m pip install --upgrade pre-commit
- run: pre-commit run --verbose --all-files --show-diff-on-failure
| name: pre-commit
on: [push, pull_request]
jobs:
pre-commit:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/cache@v2
with:
path: |
~/.cache/pre-commit
~/.cache/pip
key: ${{ runner.os }}-pre-commit-${{ hashFiles('.pre-commit-config.yaml') }}
- uses: actions/setup-python@v2
- uses: psf/black@20.8b1
- name: Install pre-commit
run: |
python -m pip install --upgrade pip
python -m pip install --upgrade pre-commit
- run: pre-commit run --verbose --all-files --show-diff-on-failure
| 1 |
TheAlgorithms/Python | 4,304 | [mypy] Fix directory arithmetic_analysis | ### **Describe your change:**
Related Issue: #4052
* [ ] 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: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T07:15:46Z" | "2021-04-02T07:32:13Z" | 895bca36541598a04dba525568a20d2282e0ffd9 | 5229c749553d9ec65d455e0183a574e45ac3e73e | [mypy] Fix directory arithmetic_analysis. ### **Describe your change:**
Related Issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Gaussian elimination method for solving a system of linear equations.
Gaussian elimination - https://en.wikipedia.org/wiki/Gaussian_elimination
"""
import numpy as np
def retroactive_resolution(coefficients: np.matrix, vector: np.array) -> np.array:
"""
This function performs a retroactive linear system resolution
for triangular matrix
Examples:
2x1 + 2x2 - 1x3 = 5 2x1 + 2x2 = -1
0x1 - 2x2 - 1x3 = -7 0x1 - 2x2 = -1
0x1 + 0x2 + 5x3 = 15
>>> gaussian_elimination([[2, 2, -1], [0, -2, -1], [0, 0, 5]], [[5], [-7], [15]])
array([[2.],
[2.],
[3.]])
>>> gaussian_elimination([[2, 2], [0, -2]], [[-1], [-1]])
array([[-1. ],
[ 0.5]])
"""
rows, columns = np.shape(coefficients)
x = np.zeros((rows, 1), dtype=float)
for row in reversed(range(rows)):
sum = 0
for col in range(row + 1, columns):
sum += coefficients[row, col] * x[col]
x[row, 0] = (vector[row] - sum) / coefficients[row, row]
return x
def gaussian_elimination(coefficients: np.matrix, vector: np.array) -> np.array:
"""
This function performs Gaussian elimination method
Examples:
1x1 - 4x2 - 2x3 = -2 1x1 + 2x2 = 5
5x1 + 2x2 - 2x3 = -3 5x1 + 2x2 = 5
1x1 - 1x2 + 0x3 = 4
>>> gaussian_elimination([[1, -4, -2], [5, 2, -2], [1, -1, 0]], [[-2], [-3], [4]])
array([[ 2.3 ],
[-1.7 ],
[ 5.55]])
>>> gaussian_elimination([[1, 2], [5, 2]], [[5], [5]])
array([[0. ],
[2.5]])
"""
# coefficients must to be a square matrix so we need to check first
rows, columns = np.shape(coefficients)
if rows != columns:
return []
# augmented matrix
augmented_mat = np.concatenate((coefficients, vector), axis=1)
augmented_mat = augmented_mat.astype("float64")
# scale the matrix leaving it triangular
for row in range(rows - 1):
pivot = augmented_mat[row, row]
for col in range(row + 1, columns):
factor = augmented_mat[col, row] / pivot
augmented_mat[col, :] -= factor * augmented_mat[row, :]
x = retroactive_resolution(
augmented_mat[:, 0:columns], augmented_mat[:, columns : columns + 1]
)
return x
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Gaussian elimination method for solving a system of linear equations.
Gaussian elimination - https://en.wikipedia.org/wiki/Gaussian_elimination
"""
import numpy as np
def retroactive_resolution(coefficients: np.matrix, vector: np.ndarray) -> np.ndarray:
"""
This function performs a retroactive linear system resolution
for triangular matrix
Examples:
2x1 + 2x2 - 1x3 = 5 2x1 + 2x2 = -1
0x1 - 2x2 - 1x3 = -7 0x1 - 2x2 = -1
0x1 + 0x2 + 5x3 = 15
>>> gaussian_elimination([[2, 2, -1], [0, -2, -1], [0, 0, 5]], [[5], [-7], [15]])
array([[2.],
[2.],
[3.]])
>>> gaussian_elimination([[2, 2], [0, -2]], [[-1], [-1]])
array([[-1. ],
[ 0.5]])
"""
rows, columns = np.shape(coefficients)
x = np.zeros((rows, 1), dtype=float)
for row in reversed(range(rows)):
sum = 0
for col in range(row + 1, columns):
sum += coefficients[row, col] * x[col]
x[row, 0] = (vector[row] - sum) / coefficients[row, row]
return x
def gaussian_elimination(coefficients: np.matrix, vector: np.ndarray) -> np.ndarray:
"""
This function performs Gaussian elimination method
Examples:
1x1 - 4x2 - 2x3 = -2 1x1 + 2x2 = 5
5x1 + 2x2 - 2x3 = -3 5x1 + 2x2 = 5
1x1 - 1x2 + 0x3 = 4
>>> gaussian_elimination([[1, -4, -2], [5, 2, -2], [1, -1, 0]], [[-2], [-3], [4]])
array([[ 2.3 ],
[-1.7 ],
[ 5.55]])
>>> gaussian_elimination([[1, 2], [5, 2]], [[5], [5]])
array([[0. ],
[2.5]])
"""
# coefficients must to be a square matrix so we need to check first
rows, columns = np.shape(coefficients)
if rows != columns:
return np.array((), dtype=float)
# augmented matrix
augmented_mat = np.concatenate((coefficients, vector), axis=1)
augmented_mat = augmented_mat.astype("float64")
# scale the matrix leaving it triangular
for row in range(rows - 1):
pivot = augmented_mat[row, row]
for col in range(row + 1, columns):
factor = augmented_mat[col, row] / pivot
augmented_mat[col, :] -= factor * augmented_mat[row, :]
x = retroactive_resolution(
augmented_mat[:, 0:columns], augmented_mat[:, columns : columns + 1]
)
return x
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 4,304 | [mypy] Fix directory arithmetic_analysis | ### **Describe your change:**
Related Issue: #4052
* [ ] 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: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T07:15:46Z" | "2021-04-02T07:32:13Z" | 895bca36541598a04dba525568a20d2282e0ffd9 | 5229c749553d9ec65d455e0183a574e45ac3e73e | [mypy] Fix directory arithmetic_analysis. ### **Describe your change:**
Related Issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Checks if a system of forces is in static equilibrium.
"""
from typing import List
from numpy import array, cos, cross, radians, sin
def polar_force(
magnitude: float, angle: float, radian_mode: bool = False
) -> List[float]:
"""
Resolves force along rectangular components.
(force, angle) => (force_x, force_y)
>>> polar_force(10, 45)
[7.0710678118654755, 7.071067811865475]
>>> polar_force(10, 3.14, radian_mode=True)
[-9.999987317275394, 0.01592652916486828]
"""
if radian_mode:
return [magnitude * cos(angle), magnitude * sin(angle)]
return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))]
def in_static_equilibrium(
forces: array, location: array, eps: float = 10 ** -1
) -> bool:
"""
Check if a system is in equilibrium.
It takes two numpy.array objects.
forces ==> [
[force1_x, force1_y],
[force2_x, force2_y],
....]
location ==> [
[x1, y1],
[x2, y2],
....]
>>> force = array([[1, 1], [-1, 2]])
>>> location = array([[1, 0], [10, 0]])
>>> in_static_equilibrium(force, location)
False
"""
# summation of moments is zero
moments: array = cross(location, forces)
sum_moments: float = sum(moments)
return abs(sum_moments) < eps
if __name__ == "__main__":
# Test to check if it works
forces = array(
[polar_force(718.4, 180 - 30), polar_force(879.54, 45), polar_force(100, -90)]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
forces = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])
location = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| """
Checks if a system of forces is in static equilibrium.
"""
from typing import List
from numpy import array, cos, cross, ndarray, radians, sin
def polar_force(
magnitude: float, angle: float, radian_mode: bool = False
) -> List[float]:
"""
Resolves force along rectangular components.
(force, angle) => (force_x, force_y)
>>> polar_force(10, 45)
[7.0710678118654755, 7.071067811865475]
>>> polar_force(10, 3.14, radian_mode=True)
[-9.999987317275394, 0.01592652916486828]
"""
if radian_mode:
return [magnitude * cos(angle), magnitude * sin(angle)]
return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))]
def in_static_equilibrium(
forces: ndarray, location: ndarray, eps: float = 10 ** -1
) -> bool:
"""
Check if a system is in equilibrium.
It takes two numpy.array objects.
forces ==> [
[force1_x, force1_y],
[force2_x, force2_y],
....]
location ==> [
[x1, y1],
[x2, y2],
....]
>>> force = array([[1, 1], [-1, 2]])
>>> location = array([[1, 0], [10, 0]])
>>> in_static_equilibrium(force, location)
False
"""
# summation of moments is zero
moments: ndarray = cross(location, forces)
sum_moments: float = sum(moments)
return abs(sum_moments) < eps
if __name__ == "__main__":
# Test to check if it works
forces = array(
[polar_force(718.4, 180 - 30), polar_force(879.54, 45), polar_force(100, -90)]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
forces = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])
location = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 4,304 | [mypy] Fix directory arithmetic_analysis | ### **Describe your change:**
Related Issue: #4052
* [ ] 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: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T07:15:46Z" | "2021-04-02T07:32:13Z" | 895bca36541598a04dba525568a20d2282e0ffd9 | 5229c749553d9ec65d455e0183a574e45ac3e73e | [mypy] Fix directory arithmetic_analysis. ### **Describe your change:**
Related Issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,304 | [mypy] Fix directory arithmetic_analysis | ### **Describe your change:**
Related Issue: #4052
* [ ] 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: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T07:15:46Z" | "2021-04-02T07:32:13Z" | 895bca36541598a04dba525568a20d2282e0ffd9 | 5229c749553d9ec65d455e0183a574e45ac3e73e | [mypy] Fix directory arithmetic_analysis. ### **Describe your change:**
Related Issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 14: https://projecteuler.net/problem=14
Problem Statement:
The following iterative sequence is defined for the set of positive integers:
n → n/2 (n is even)
n → 3n + 1 (n is odd)
Using the rule above and starting with 13, we generate the following sequence:
13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It can be seen that this sequence (starting at 13 and finishing at 1) contains
10 terms. Although it has not been proved yet (Collatz Problem), it is thought
that all starting numbers finish at 1.
Which starting number, under one million, produces the longest chain?
"""
def solution(n: int = 1000000) -> int:
"""Returns the number under n that generates the longest sequence using the
formula:
n → n/2 (n is even)
n → 3n + 1 (n is odd)
# The code below has been commented due to slow execution affecting Travis.
# >>> solution(1000000)
# 837799
>>> solution(200)
171
>>> solution(5000)
3711
>>> solution(15000)
13255
"""
largest_number = 0
pre_counter = 0
for input1 in range(n):
counter = 1
number = input1
while number > 1:
if number % 2 == 0:
number /= 2
counter += 1
else:
number = (3 * number) + 1
counter += 1
if counter > pre_counter:
largest_number = input1
pre_counter = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| """
Problem 14: https://projecteuler.net/problem=14
Problem Statement:
The following iterative sequence is defined for the set of positive integers:
n → n/2 (n is even)
n → 3n + 1 (n is odd)
Using the rule above and starting with 13, we generate the following sequence:
13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It can be seen that this sequence (starting at 13 and finishing at 1) contains
10 terms. Although it has not been proved yet (Collatz Problem), it is thought
that all starting numbers finish at 1.
Which starting number, under one million, produces the longest chain?
"""
def solution(n: int = 1000000) -> int:
"""Returns the number under n that generates the longest sequence using the
formula:
n → n/2 (n is even)
n → 3n + 1 (n is odd)
# The code below has been commented due to slow execution affecting Travis.
# >>> solution(1000000)
# 837799
>>> solution(200)
171
>>> solution(5000)
3711
>>> solution(15000)
13255
"""
largest_number = 0
pre_counter = 0
for input1 in range(n):
counter = 1
number = input1
while number > 1:
if number % 2 == 0:
number /= 2
counter += 1
else:
number = (3 * number) + 1
counter += 1
if counter > pre_counter:
largest_number = input1
pre_counter = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| -1 |
TheAlgorithms/Python | 4,304 | [mypy] Fix directory arithmetic_analysis | ### **Describe your change:**
Related Issue: #4052
* [ ] 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: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T07:15:46Z" | "2021-04-02T07:32:13Z" | 895bca36541598a04dba525568a20d2282e0ffd9 | 5229c749553d9ec65d455e0183a574e45ac3e73e | [mypy] Fix directory arithmetic_analysis. ### **Describe your change:**
Related Issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
from .number_theory.prime_numbers import next_prime
class HashTable:
"""
Basic Hash Table example with open addressing and linear probing
"""
def __init__(self, size_table, charge_factor=None, lim_charge=None):
self.size_table = size_table
self.values = [None] * self.size_table
self.lim_charge = 0.75 if lim_charge is None else lim_charge
self.charge_factor = 1 if charge_factor is None else charge_factor
self.__aux_list = []
self._keys = {}
def keys(self):
return self._keys
def balanced_factor(self):
return sum([1 for slot in self.values if slot is not None]) / (
self.size_table * self.charge_factor
)
def hash_function(self, key):
return key % self.size_table
def _step_by_step(self, step_ord):
print(f"step {step_ord}")
print([i for i in range(len(self.values))])
print(self.values)
def bulk_insert(self, values):
i = 1
self.__aux_list = values
for value in values:
self.insert_data(value)
self._step_by_step(i)
i += 1
def _set_value(self, key, data):
self.values[key] = data
self._keys[key] = data
def _collision_resolution(self, key, data=None):
new_key = self.hash_function(key + 1)
while self.values[new_key] is not None and self.values[new_key] != key:
if self.values.count(None) > 0:
new_key = self.hash_function(new_key + 1)
else:
new_key = None
break
return new_key
def rehashing(self):
survivor_values = [value for value in self.values if value is not None]
self.size_table = next_prime(self.size_table, factor=2)
self._keys.clear()
self.values = [None] * self.size_table # hell's pointers D: don't DRY ;/
for value in survivor_values:
self.insert_data(value)
def insert_data(self, data):
key = self.hash_function(data)
if self.values[key] is None:
self._set_value(key, data)
elif self.values[key] == data:
pass
else:
collision_resolution = self._collision_resolution(key, data)
if collision_resolution is not None:
self._set_value(collision_resolution, data)
else:
self.rehashing()
self.insert_data(data)
| #!/usr/bin/env python3
from .number_theory.prime_numbers import next_prime
class HashTable:
"""
Basic Hash Table example with open addressing and linear probing
"""
def __init__(self, size_table, charge_factor=None, lim_charge=None):
self.size_table = size_table
self.values = [None] * self.size_table
self.lim_charge = 0.75 if lim_charge is None else lim_charge
self.charge_factor = 1 if charge_factor is None else charge_factor
self.__aux_list = []
self._keys = {}
def keys(self):
return self._keys
def balanced_factor(self):
return sum([1 for slot in self.values if slot is not None]) / (
self.size_table * self.charge_factor
)
def hash_function(self, key):
return key % self.size_table
def _step_by_step(self, step_ord):
print(f"step {step_ord}")
print([i for i in range(len(self.values))])
print(self.values)
def bulk_insert(self, values):
i = 1
self.__aux_list = values
for value in values:
self.insert_data(value)
self._step_by_step(i)
i += 1
def _set_value(self, key, data):
self.values[key] = data
self._keys[key] = data
def _collision_resolution(self, key, data=None):
new_key = self.hash_function(key + 1)
while self.values[new_key] is not None and self.values[new_key] != key:
if self.values.count(None) > 0:
new_key = self.hash_function(new_key + 1)
else:
new_key = None
break
return new_key
def rehashing(self):
survivor_values = [value for value in self.values if value is not None]
self.size_table = next_prime(self.size_table, factor=2)
self._keys.clear()
self.values = [None] * self.size_table # hell's pointers D: don't DRY ;/
for value in survivor_values:
self.insert_data(value)
def insert_data(self, data):
key = self.hash_function(data)
if self.values[key] is None:
self._set_value(key, data)
elif self.values[key] == data:
pass
else:
collision_resolution = self._collision_resolution(key, data)
if collision_resolution is not None:
self._set_value(collision_resolution, data)
else:
self.rehashing()
self.insert_data(data)
| -1 |
TheAlgorithms/Python | 4,304 | [mypy] Fix directory arithmetic_analysis | ### **Describe your change:**
Related Issue: #4052
* [ ] 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: #{$ISSUE_NO}`.
| algobytewise | "2021-04-02T07:15:46Z" | "2021-04-02T07:32:13Z" | 895bca36541598a04dba525568a20d2282e0ffd9 | 5229c749553d9ec65d455e0183a574e45ac3e73e | [mypy] Fix directory arithmetic_analysis. ### **Describe your change:**
Related Issue: #4052
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
Subsets and Splits