


'''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      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]):

      '''simple docstring'''





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




def      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Dict):

      '''simple docstring'''





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




def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Union[str, Any]):

      '''simple docstring'''





      A_						:  List[str]          =				np.dot(lowerCamelCase							,						lowerCamelCase)
      return np.sum(y * scores - np.log(1 + np.exp(lowerCamelCase)))




def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      List[str]							,						lowerCamelCase  :      Union[str, Any]=7_0000):

      '''simple docstring'''





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

      for iterations in range(lowerCamelCase):
            A_						:  Tuple          =				np.dot(lowerCamelCase							,						lowerCamelCase)
            A_						:  str          =				sigmoid_function(lowerCamelCase)
            A_						:  str          =				np.dot(x.T							,						h - y) / y.size
            A_						:  Any          =				theta - alpha * gradient  # updating the weights
            A_						:  List[str]          =				np.dot(lowerCamelCase							,						lowerCamelCase)
            A_						:  Dict          =				sigmoid_function(lowerCamelCase)
            A_						:  str          =				cost_function(lowerCamelCase							,						lowerCamelCase)
            if iterations % 100 == 0:
                  print(F'loss: {j} \t')  # printing the loss after every 100 iterations
      return theta


# In[68]:

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

     __magic_name__			=      0.1
     __magic_name__			=      logistic_reg(alpha, x, y, max_iterations=70_000)
     print('theta: ', theta)  # printing the theta i.e our weights vector




     def      lowerCamelCase					(      lowerCamelCase  :      str):

           '''simple docstring'''





           return sigmoid_function(
               np.dot(lowerCamelCase							,						lowerCamelCase))  # 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')
     (__magic_name__)			=      (x[:, 0].min(), x[:, 0].max())
     (__magic_name__)			=      (x[:, 1].min(), x[:, 1].max())
     (__magic_name__)			=      np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
     __magic_name__			=      np.c_[xxa.ravel(), xxa.ravel()]
     __magic_name__			=      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_tf_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__			=      {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['ConvNextFeatureExtractor']
     __magic_name__			=      ['ConvNextImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ConvNextForImageClassification',
         'ConvNextModel',
         'ConvNextPreTrainedModel',
         'ConvNextBackbone',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'TFConvNextForImageClassification',
         'TFConvNextModel',
         'TFConvNextPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_convnext import ConvNextFeatureExtractor
          from .image_processing_convnext import ConvNextImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnext import (
              CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextBackbone,
              ConvNextForImageClassification,
              ConvNextModel,
              ConvNextPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure)
