


# 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()









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

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