






"""simple docstring"""





from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__lowerCamelCase							=  TypeVar('KEY')
__lowerCamelCase							=  TypeVar('VAL')



@dataclass(frozen=SCREAMING_SNAKE_CASE_     ,slots=SCREAMING_SNAKE_CASE_			)
class       __A		(   Generic[KEY, VAL]			):
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				42



class       __A		(   _Item			):

					def __init__(			self  :      List[Any]					)			->							None:
											super().__init__(__snake_case     ,		__snake_case					)






					def __bool__(			self  :      Optional[int]					)			->							bool:
											return False


__lowerCamelCase							=  _DeletedItem()



class       __A		(   MutableMapping[KEY, VAL]			):

					def __init__(			self  :      Any     ,		__snake_case  :      int = 8     ,		__snake_case  :      float = 0.75					)			->							None:
											__magic_name__:	List[str]							=       initial_block_size
											__magic_name__:	list[_Item | None]							=       [None] * initial_block_size
											assert 0.0 < capacity_factor < 1.0
											__magic_name__:	List[Any]							=       capacity_factor
											__magic_name__:	List[str]							=       0

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      KEY					)			->							int:
											return hash(__snake_case					) % len(self._buckets					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      int					)			->							int:
											return (ind + 1) % len(self._buckets					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      KEY     ,		__snake_case  :      VAL					)			->							bool:
											__magic_name__:	List[Any]							=       self._buckets[ind]
											if not stored:
																	__magic_name__:	Dict							=       _Item(__snake_case     ,		__snake_case					)
																	self._len += 1
																	return True
											elif stored.key == key:
																	__magic_name__:	Tuple							=       _Item(__snake_case     ,		__snake_case					)
																	return True
											else:
																	return False

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							bool:
											__magic_name__:	Union[str, Any]							=       len(self._buckets					) * self._capacity_factor
											return len(self					) >= int(__snake_case					)

					def   lowerCamelCase__   (			self  :      Dict					)			->							bool:
											if len(self._buckets					) <= self._initial_block_size:
																	return False
											__magic_name__:	str							=       len(self._buckets					) * self._capacity_factor / 2
											return len(self					) < limit

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      int					)			->							None:
											__magic_name__:	List[str]							=       self._buckets
											__magic_name__:	Optional[int]							=       [None] * new_size
											__magic_name__:	Any							=       0
											for item in old_buckets:
																	if item:
																							self._add_item(item.key     ,		item.val					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							None:
											self._resize(len(self._buckets					) * 2					)

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							None:
											self._resize(len(self._buckets					) // 2					)

					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      KEY					)			->							Iterator[int]:
											__magic_name__:	Optional[int]							=       self._get_bucket_index(__snake_case					)
											for _ in range(len(self._buckets					)					):
																	yield ind
																	__magic_name__:	Dict							=       self._get_next_ind(__snake_case					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      KEY     ,		__snake_case  :      VAL					)			->							None:
											for ind in self._iterate_buckets(__snake_case					):
																	if self._try_set(__snake_case     ,		__snake_case     ,		__snake_case					):
																							break

					def __setitem__(			self  :      List[Any]     ,		__snake_case  :      KEY     ,		__snake_case  :      VAL					)			->							None:
											if self._is_full():
																	self._size_up()

											self._add_item(__snake_case     ,		__snake_case					)

					def __delitem__(			self  :      Optional[Any]     ,		__snake_case  :      KEY					)			->							None:
											for ind in self._iterate_buckets(__snake_case					):
																	__magic_name__:	Dict							=       self._buckets[ind]
																	if item is None:
																							raise KeyError(__snake_case					)
																	if item is _deleted:
																							continue
																	if item.key == key:
																							__magic_name__:	Tuple							=       _deleted
																							self._len -= 1
																							break
											if self._is_sparse():
																	self._size_down()

					def __getitem__(			self  :      Optional[int]     ,		__snake_case  :      KEY					)			->							VAL:
											for ind in self._iterate_buckets(__snake_case					):
																	__magic_name__:	Optional[Any]							=       self._buckets[ind]
																	if item is None:
																							break
																	if item is _deleted:
																							continue
																	if item.key == key:
																							return item.val
											raise KeyError(__snake_case					)

					def __len__(			self  :      str					)			->							int:
											return self._len

					def __iter__(			self  :      Any					)			->							Iterator[KEY]:
											yield from (item.key for item in self._buckets if item)






					def __repr__(			self  :      List[str]					)			->							str:
											__magic_name__:	Union[str, Any]							=       """ ,""".join(
											    F'{item.key}: {item.val}' for item in self._buckets if item					)
											return F'HashMap({val_string})'






# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							return 1 / (1 + np.exp(-z ))





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							return (-y * np.log(UpperCamelCase__ ) - (1 - y) * np.log(1 - h )).mean()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
							return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase__ ) ) )





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=70000 ):







							'''simple docstring'''






							snake_case_     							=       np.zeros(x.shape[1] )

							for iterations in range(UpperCamelCase__ ):
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       np.dot(x.T  ,				h - y ) / y.size
														snake_case_     							=       theta - alpha * gradient  # updating the weights
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       cost_function(UpperCamelCase__  ,				UpperCamelCase__ )
														if iterations % 100 == 0:
																					print(F'''loss: {j} \t''' )  # printing the loss after every 100 iterations
							return theta


# In[68]:

if __name__ == "__main__":
					_UpperCAmelCase						:  str									=			datasets.load_iris()
					_UpperCAmelCase						:  Union[str, Any]									=			iris.data[:, :2]
					_UpperCAmelCase						:  List[str]									=			(iris.target != 0) * 1

					_UpperCAmelCase						:  Optional[int]									=			0.1
					_UpperCAmelCase						:  Dict									=			logistic_reg(alpha, x, y, max_iterations=7_0000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def    __lowerCamelCase      (					UpperCamelCase__ ):







												'''simple docstring'''






												return sigmoid_function(
												    np.dot(UpperCamelCase__  ,				UpperCamelCase__ ) )  # predicting the value of probability from the logistic regression algorithm

					plt.figure(figsize=(10, 6))
					plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
					plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Any									=			(x[:, 0].min(), x[:, 0].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Union[str, Any]									=			(x[:, 1].min(), x[:, 1].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  int									=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					_UpperCAmelCase						:  List[Any]									=			np.c_[xxa.ravel(), xxa.ravel()]
					_UpperCAmelCase						:  Dict									=			predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()


					plt.show()




