
# 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()
import math
import random




def    lowerCAmelCase_ (     __A,  __A = False							)     ->				float:







      '''simple docstring'''


      if deriv:
            return value * (1 - value)
      return 1 / (1 + math.exp(-value							))


# Initial Value
UpperCamelCase__											=					0.0_2




def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


      UpperCAmelCase__								=  float(2 * (random.randint(1,  100							)) - 1							)

      for _ in range(__A							):
            # Forward propagation
            UpperCAmelCase__								=  sigmoid_function(INITIAL_VALUE * weight							)
            # How much did we miss?
            UpperCAmelCase__								=  (expected / 100) - layer_a
            # Error delta
            UpperCAmelCase__								=  layer_1_error * sigmoid_function(__A,  __A							)
            # Update weight
            weight += INITIAL_VALUE * layer_1_delta

      return layer_a * 100


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					int(input('Expected value: '))
      UpperCamelCase__											=					int(input('Number of propagations: '))
      print(forward_propagation(expected, number_propagations))



