
"""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__						(     __snake_case      )				->						Any:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case=7_00_00      )				->						int:


					"""simple docstring"""





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

					for iterations in range(__snake_case      ):
										_UpperCamelCase									=    np.dot(__snake_case,	__snake_case      )
										_UpperCamelCase									=    sigmoid_function(__snake_case      )
										_UpperCamelCase									=    np.dot(x.T,	h - y      ) / y.size
										_UpperCamelCase									=    theta - alpha * gradient  # updating the weights
										_UpperCamelCase									=    np.dot(__snake_case,	__snake_case      )
										_UpperCamelCase									=    sigmoid_function(__snake_case      )
										_UpperCamelCase									=    cost_function(__snake_case,	__snake_case      )
										if iterations % 1_00 == 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_0000)
				print("""theta: """, theta)  # printing the theta i.e our weights vector



				def  lowerCamelCase__						(     __snake_case      )				->						str:


									"""simple docstring"""





									return sigmoid_function(
									    np.dot(__snake_case,	__snake_case      )      )  # 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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['image_processor', 'tokenizer']
		lowercase__         = 'ViTImageProcessor'
		lowercase__         = ('CLIPTokenizer', 'CLIPTokenizerFast')



		def __init__(   self							,						__a=None							,						__a=None							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,						__a							,						)
												_UpperCamelCase									=    kwargs.pop('''feature_extractor''')

							_UpperCamelCase									=    image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.''')
							if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.''')

							super().__init__(__a							,						__a)



		def __call__(   self							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						**__a)			->			Tuple:






							'''simple docstring'''
							if text is None and visual_prompt is None and images is None:
												raise ValueError('''You have to specify either text, visual prompt or images.''')

							if text is not None and visual_prompt is not None:
												raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''')

							if text is not None:
												_UpperCamelCase									=    self.tokenizer(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

							if images is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None and images is not None:
												_UpperCamelCase									=    {
												    '''pixel_values''': image_features.pixel_values,
												    '''conditional_pixel_values''': prompt_features.pixel_values,
												}
												return encoding
							elif text is not None and images is not None:
												_UpperCamelCase									=    image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							elif visual_prompt is not None:
												_UpperCamelCase									=    {
												    '''conditional_pixel_values''': prompt_features.pixel_values,
												}
												return encoding
							else:
												return BatchEncoding(data=dict(**__a)							,						tensor_type=__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			Any:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			List[str]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a							,						**__a)



		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,						__a							,						)
							return self.image_processor_class




		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,						__a							,						)
							return self.image_processor






