
# 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			(			a__  )       ->  Dict:
				return 1 / (1 + np.exp(-z  ))


def     __lowerCAmelCase			(			a__						,    a__  )       ->  Tuple:
				return (-y * np.log(a__  ) - (1 - y) * np.log(1 - h  )).mean()


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  List[Any]:
				__a										=    np.dot(a__						,    a__  )
				return np.sum(y * scores - np.log(1 + np.exp(a__  )  )  )


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__=7_0000  )       ->  Tuple:
				__a										=    np.zeros(x.shape[1]  )

				for iterations in range(a__  ):
								__a										=    np.dot(a__						,    a__  )
								__a										=    sigmoid_function(a__  )
								__a										=    np.dot(x.T						,    h - y  ) / y.size
								__a										=    theta - alpha * gradient  # updating the weights
								__a										=    np.dot(a__						,    a__  )
								__a										=    sigmoid_function(a__  )
								__a										=    cost_function(a__						,    a__  )
								if iterations % 100 == 0:
												print(F"""loss: {j} \t"""  )  # printing the loss after every 100 iterations
				return theta


# In[68]:

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

					A     :	Dict    = 0.1
					A     :	str    = logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
					print('theta: ', theta)  # printing the theta i.e our weights vector


					def     __lowerCAmelCase			(			a__  )       ->  Union[str, Any]:
									return sigmoid_function(
									    np.dot(a__						,    a__  )  )  # predicting the value of probability from the logistic regression algorithm

					plt.figure(figsize=(1_0, 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')
					((A) ,   (A))     :	Tuple    = (x[:, 0].min(), x[:, 0].max())
					((A) ,   (A))     :	int    = (x[:, 1].min(), x[:, 1].max())
					((A) ,   (A))     :	List[str]    = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					A     :	int    = np.c_[xxa.ravel(), xxa.ravel()]
					A     :	Any    = predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

					plt.legend()






					plt.show()

def     __lowerCAmelCase			(			a__						,    a__  )       ->  float:

				def get_matched_characters(a__						,    a__  ) -> str:
								__a										=    []
								__a										=    min(len(_stra  )						,    len(_stra  )  ) // 2
								for i, l in enumerate(_stra  ):
												__a										=    int(max(0						,    i - limit  )  )
												__a										=    int(min(i + limit + 1						,    len(_stra  )  )  )
												if l in _stra[left:right]:
																matched.append(a__  )
																__a										=    F"""{_stra[0:_stra.index(a__  )]} {_stra[_stra.index(a__  ) + 1:]}"""

								return "".join(a__  )

				# matching characters
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    len(a__  )

				# transposition
				__a										=    (
				    len([(ca, ca) for ca, ca in zip(a__						,    a__  ) if ca != ca]  ) // 2
				)

				if not match_count:
								__a										=    0.0
				else:
								__a										=    (
								    1
								    / 3
								    * (
								        match_count / len(a__  )
								        + match_count / len(a__  )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				__a										=    0
				for ca, ca in zip(stra[:4]						,    stra[:4]  ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(jaro_winkler('hello', 'world'))