

"""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  (  UpperCamelCase_     :       List[Any])	->						Optional[int]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Optional[int])	->						Tuple:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       Any,			UpperCamelCase_     :       Optional[int])	->						List[str]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       List[Any]=70000)	->						Tuple:
   '''simple docstring'''


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

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

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




   def   _A  (  UpperCamelCase_     :       str)	->						Tuple:
      '''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')
   ((_a)      ,						(_a))       =							(x[:, 0].min(), x[:, 0].max())
   ((_a)      ,						(_a))       =							(x[:, 1].min(), x[:, 1].max())
   ((_a)      ,						(_a))       =							np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   _a       =							np.c_[xxa.ravel(), xxa.ravel()]
   _a       =							predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

   plt.legend()

   plt.show()







"""simple docstring"""





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 					_lowerCAmelCase  (	unittest.TestCase     ,lowercase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						load_tool("text-classification"	)
    self.tool.setup()
    __lowercase								=						load_tool("text-classification",					remote=UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.tool("That's quite cool",					["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.remote_tool("That's quite cool",					["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.tool(text="That's quite cool",					labels=["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)



 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.remote_tool(text="That's quite cool",					labels=["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)




