






"""simple docstring"""


# 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       snake_case  (A_				:Dict				):







  '''simple docstring'''

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




def       snake_case  (A_				:List[Any]     ,      A_				:List[Any]				):







  '''simple docstring'''

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




def       snake_case  (A_				:List[Any]     ,      A_				:Union[str, Any]     ,      A_				:Tuple				):







  '''simple docstring'''

  a					:      Tuple    =       np.dot(A_     ,      A_				)
  return np.sum(y * scores - np.log(1 + np.exp(A_				)				)				)




def       snake_case  (A_				:Dict     ,      A_				:Union[str, Any]     ,      A_				:Optional[Any]     ,      A_				:str=7_0_0_0_0				):







  '''simple docstring'''

  a					:      int    =       np.zeros(x.shape[1]				)

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

   _UpperCamelCase    :   int   			=						0.1
   _UpperCamelCase    :   Tuple   			=						logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('theta: ', theta)  # printing the theta i.e our weights vector




   def       snake_case  (A_				:Dict				):







     '''simple docstring'''

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

   plt.legend()






   plt.show()












"""simple docstring"""


from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




