


"""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     __a   (	a       ):





       """simple docstring"""







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



def     __a   (	a,		a       ):





       """simple docstring"""







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



def     __a   (	a,		a,		a       ):





       """simple docstring"""







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



def     __a   (	a,		a,		a,		a=7_0_0_0_0       ):





       """simple docstring"""







       _a							 =					np.zeros(x.shape[1]       )

       for iterations in range(_UpperCamelCase       ):
              _a							 =					np.dot(_UpperCamelCase,		_UpperCamelCase       )
              _a							 =					sigmoid_function(_UpperCamelCase       )
              _a							 =					np.dot(x.T,		h - y       ) / y.size
              _a							 =					theta - alpha * gradient  # updating the weights
              _a							 =					np.dot(_UpperCamelCase,		_UpperCamelCase       )
              _a							 =					sigmoid_function(_UpperCamelCase       )
              _a							 =					cost_function(_UpperCamelCase,		_UpperCamelCase       )
              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__":
   __SCREAMING_SNAKE_CASE      		=    datasets.load_iris()
   __SCREAMING_SNAKE_CASE      		=    iris.data[:, :2]
   __SCREAMING_SNAKE_CASE      		=    (iris.target != 0) * 1

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



   def     __a   (	a       ):





          """simple docstring"""







          return sigmoid_function(
              np.dot(_UpperCamelCase,		_UpperCamelCase       )       )  # 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""")
   ((__SCREAMING_SNAKE_CASE)		,	(__SCREAMING_SNAKE_CASE))      		=    (x[:, 0].min(), x[:, 0].max())
   ((__SCREAMING_SNAKE_CASE)		,	(__SCREAMING_SNAKE_CASE))      		=    (x[:, 1].min(), x[:, 1].max())
   ((__SCREAMING_SNAKE_CASE)		,	(__SCREAMING_SNAKE_CASE))      		=    np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __SCREAMING_SNAKE_CASE      		=    np.c_[xxa.ravel(), xxa.ravel()]
   __SCREAMING_SNAKE_CASE      		=    predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

   plt.legend()
   plt.show()





from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = False						,					__UpperCAmelCase = False						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(
        features=__UpperCAmelCase						,					cache_dir=__UpperCAmelCase						,					keep_in_memory=__UpperCAmelCase						,					streaming=__UpperCAmelCase						,					num_proc=__UpperCAmelCase						,					**__UpperCAmelCase						,					)
    __lowerCamelCase    				=     Generator(
        cache_dir=__UpperCAmelCase						,					features=__UpperCAmelCase						,					generator=__UpperCAmelCase						,					gen_kwargs=__UpperCAmelCase						,					**__UpperCAmelCase						,					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Build iterable dataset
    if self.streaming:
       __lowerCamelCase    				=     self.builder.as_streaming_dataset(split='''train'''					)
    # Build regular (map-style) dataset
    else:
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None

       self.builder.download_and_prepare(
           download_config=__UpperCAmelCase						,					download_mode=__UpperCAmelCase						,					verification_mode=__UpperCAmelCase						,					base_path=__UpperCAmelCase						,					num_proc=self.num_proc						,					)
       __lowerCamelCase    				=     self.builder.as_dataset(
           split='''train'''						,					verification_mode=__UpperCAmelCase						,					in_memory=self.keep_in_memory					)
    return dataset






