






# 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 							A     (       a_					)     ->						Optional[Any]:
					return 1 / (1 + np.exp(-z					))






def 							A     (       a_							,a_					)     ->						int:
					return (-y * np.log(a_					) - (1 - y) * np.log(1 - h					)).mean()






def 							A     (       a_							,a_							,a_					)     ->						Tuple:
					__UpperCamelCase      :					Optional[int]       		=np.dot(a_							,a_					)
					return np.sum(y * scores - np.log(1 + np.exp(a_					)					)					)






def 							A     (       a_							,a_							,a_							,a_=70_000					)     ->						str:
					__UpperCamelCase      :					Union[str, Any]       		=np.zeros(x.shape[1]					)

					for iterations in range(a_					):
										__UpperCamelCase      :					str       		=np.dot(a_							,a_					)
										__UpperCamelCase      :					Any       		=sigmoid_function(a_					)
										__UpperCamelCase      :					Optional[Any]       		=np.dot(x.T							,h - y					) / y.size
										__UpperCamelCase      :					int       		=theta - alpha * gradient  # updating the weights
										__UpperCamelCase      :					Dict       		=np.dot(a_							,a_					)
										__UpperCamelCase      :					Optional[Any]       		=sigmoid_function(a_					)
										__UpperCamelCase      :					List[Any]       		=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_       :Optional[Any]       =						datasets.load_iris()
							A_       :Any       =						iris.data[:, :2]
							A_       :Optional[Any]       =						(iris.target != 0) * 1

							A_       :Tuple       =						0.1
							A_       :Any       =						logistic_reg(alpha, x, y, max_iterations=70000)
							print('''theta: ''', theta)  # printing the theta i.e our weights vector






							def 							A     (       a_					)     ->						str:
												return sigmoid_function(
												    np.dot(a_							,a_					)					)  # 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''')
							((A_)  ,(A_))       :Union[str, Any]       =						(x[:, 0].min(), x[:, 0].max())
							((A_)  ,(A_))       :Optional[Any]       =						(x[:, 1].min(), x[:, 1].max())
							((A_)  ,(A_))       :Tuple       =						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
							A_       :List[str]       =						np.c_[xxa.ravel(), xxa.ravel()]
							A_       :Optional[int]       =						predict_prob(grid).reshape(xxa.shape)
							plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

							plt.legend()


							plt.show()








'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     return " ".join(
         "".join(word[::-1]       ) if len(__lowercase       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(reverse_long_words('''Hey wollef sroirraw'''))




