


# 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__							)	-> List[Any]:




      '''simple docstring'''

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




def    a   (							A__      ,    A__							)	-> Any:




      '''simple docstring'''

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




def    a   (							A__      ,    A__      ,    A__							)	-> Tuple:




      '''simple docstring'''

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




def    a   (							A__      ,    A__      ,    A__      ,    A__=7_0_0_0_0							)	-> Tuple:




      '''simple docstring'''

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

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

    a_ :Dict  							=			0.1
    a_ :str  							=			logistic_reg(alpha, x, y, max_iterations=7_00_00)
    print('theta: ', theta)  # printing the theta i.e our weights vector




    def    a   (							A__							)	-> int:




          '''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')
    ((a_)							,     (a_)) :str  							=			(x[:, 0].min(), x[:, 0].max())
    ((a_)							,     (a_)) :Tuple  							=			(x[:, 1].min(), x[:, 1].max())
    ((a_)							,     (a_)) :Dict  							=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
    a_ :Optional[int]  							=			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()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__					:     List[str]       =     [
     'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
     'SEWForCTC',
     'SEWForSequenceClassification',
     'SEWModel',
     'SEWPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_sew import (
      SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
      SEWForCTC,
      SEWForSequenceClassification,
      SEWModel,
      SEWPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__					:     Tuple       =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





