
"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any						):






							"""simple docstring"""



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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					Any						):






							"""simple docstring"""



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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					List[Any]						):






							"""simple docstring"""



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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					int=7_0000						):






							"""simple docstring"""



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

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


# In[68]:

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

	_snake_case          =						0.1
	_snake_case          =						logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
	print('''theta: ''', theta)  # printing the theta i.e our weights vector






	def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






								"""simple docstring"""



								return sigmoid_function(
								    np.dot(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)						)  # predicting the value of probability from the logistic regression algorithm

	plt.figure(figsize=(1_0, 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''')
	((_snake_case)      ,    (_snake_case))          =						(x[:, 0].min(), x[:, 0].max())
	((_snake_case)      ,    (_snake_case))          =						(x[:, 1].min(), x[:, 1].max())
	((_snake_case)      ,    (_snake_case))          =						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
	_snake_case          =						np.c_[xxa.ravel(), xxa.ravel()]
	_snake_case          =						predict_prob(grid).reshape(xxa.shape)
	plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

	plt.legend()

	plt.show()








"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"mobilenet_v1"


				def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]=3				,     UpperCAmelCase_	:			str=224				,     UpperCAmelCase_	:			Union[str, Any]=1.0				,     UpperCAmelCase_	:			List[str]=8				,     UpperCAmelCase_	:			Union[str, Any]="relu6"				,     UpperCAmelCase_	:			Optional[Any]=True				,     UpperCAmelCase_	:			Optional[int]=0.999				,     UpperCAmelCase_	:			Optional[Any]=0.02				,     UpperCAmelCase_	:			Union[str, Any]=0.001				,     **UpperCAmelCase_	:			List[Any]				,     )		->  Dict:







											"""simple docstring"""




											super().__init__(**UpperCAmelCase_  )

											if depth_multiplier <= 0:
																		raise ValueError('depth_multiplier must be greater than zero.'  )

											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			depth_multiplier
											_lowerCAmelCase  	=			min_depth
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			tf_padding
											_lowerCAmelCase  	=			classifier_dropout_prob
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			layer_norm_eps






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					version.parse("1.11"							)


				@property
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											return OrderedDict([('pixel_values', {0: 'batch'})]  )


				@property
				def 				__lowerCamelCase	(					self	:			Dict  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											if self.task == "image-classification":
																		return OrderedDict([('logits', {0: 'batch'})]  )
											else:
																		return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]  )







				@property
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  float:







											"""simple docstring"""




											return 1E-4






