



'''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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return 1 / (1 + np.exp(-z			))
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    return (-y * np.log(UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					np.dot(UpperCamelCase							,    UpperCamelCase			)
    return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase			)			)			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=70000			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					np.zeros(x.shape[1]			)

    for iterations in range(UpperCamelCase			):
        lowerCAmelCase__ :  Dict							      =					np.dot(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Union[str, Any]							      =					sigmoid_function(UpperCamelCase			)
        lowerCAmelCase__ :  Any							      =					np.dot(x.T							,    h - y			) / y.size
        lowerCAmelCase__ :  Dict							      =					theta - alpha * gradient  # updating the weights
        lowerCAmelCase__ :  Dict							      =					np.dot(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  int							      =					sigmoid_function(UpperCamelCase			)
        lowerCAmelCase__ :  Any							      =					cost_function(UpperCamelCase							,    UpperCamelCase			)
        if iterations % 100 == 0:
            print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
 _lowerCAmelCase      						=	datasets.load_iris()
 _lowerCAmelCase      						=	iris.data[:, :2]
 _lowerCAmelCase      						=	(iris.target != 0) * 1

 _lowerCAmelCase      						=	0.1
 _lowerCAmelCase      						=	logistic_reg(alpha, x, y, max_iterations=7_0000)
 print('''theta: ''', theta)  # printing the theta i.e our weights vector
 def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



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

 plt.legend()




 plt.show()







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase    :str     =					{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SqueezeBertForMaskedLM',
      'SqueezeBertForMultipleChoice',
      'SqueezeBertForQuestionAnswering',
      'SqueezeBertForSequenceClassification',
      'SqueezeBertForTokenClassification',
      'SqueezeBertModel',
      'SqueezeBertModule',
      'SqueezeBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :Optional[Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


