





# 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  :Optional[int]       )						->							Dict:






		"""simple docstring"""

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







def   __lowerCamelCase   (				__a  :Optional[int]       ,	__a  :List[Any]       )						->							Tuple:






		"""simple docstring"""

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







def   __lowerCamelCase   (				__a  :Any       ,	__a  :Optional[int]       ,	__a  :str       )						->							List[str]:






		"""simple docstring"""

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







def   __lowerCamelCase   (				__a  :Union[str, Any]       ,	__a  :str       ,	__a  :Union[str, Any]       ,	__a  :Any=7_0_0_0_0       )						->							Union[str, Any]:






		"""simple docstring"""

		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 % 1_0_0 == 0:
						print(F'loss: {j} \t'       )  # printing the loss after every 100 iterations
		return theta


# In[68]:

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

					A       :   Union[str, Any]					= 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]       )						->							List[Any]:






							"""simple docstring"""

							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))       :   List[Any]					= (x[:, 0].min(), x[:, 0].max())
					((A),      (A))       :   Union[str, Any]					= (x[:, 1].min(), x[:, 1].max())
					((A),      (A))       :   Union[str, Any]					= np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					A       :   Optional[Any]					= np.c_[xxa.ravel(), xxa.ravel()]
					A       :   Union[str, 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  :int       ,	__a  :int       )						->							Optional[Any]:






		"""simple docstring"""

		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )
		else:
				return a * actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )







def   __lowerCamelCase   (				__a  :int       ,	__a  :int       )						->							float:






		"""simple docstring"""

		if b < 0:
				return 1 / actual_power(__a       ,	__a       )
		return actual_power(__a       ,	__a       )


if __name__ == "__main__":
					print(power(-2, -3))



