





'''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		(  lowerCamelCase_	):


		"""simple docstring"""



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



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



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



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



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



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_=7_00_00	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Optional[Any]		=			np.zeros(x.shape[1]	)

		for iterations in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			np.dot(lowerCamelCase_			,			lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Tuple		=			sigmoid_function(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				List[Any]		=			np.dot(x.T			,			h - y	) / y.size
				SCREAMING_SNAKE_CASE :				Tuple		=			theta - alpha * gradient  # updating the weights
				SCREAMING_SNAKE_CASE :				List[Any]		=			np.dot(lowerCamelCase_			,			lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				int		=			sigmoid_function(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			cost_function(lowerCamelCase_			,			lowerCamelCase_	)
				if iterations % 1_00 == 0:
						print(f'''loss: {j} \t'''	)  # printing the loss after every 100 iterations
		return theta


# In[68]:

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

							__UpperCAmelCase            =       0.1
							__UpperCAmelCase            =       logistic_reg(alpha, x, y, max_iterations=70000)
							print("""theta: """, theta)  # printing the theta i.e our weights vector



							def 		__A		(  lowerCamelCase_	):


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

							plt.legend()


							plt.show()










'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''fnet'''


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			List[str]=3_20_00      ,						lowerCamelCase_				:			List[Any]=7_68      ,						lowerCamelCase_				:			Union[str, Any]=12      ,						lowerCamelCase_				:			Optional[Any]=30_72      ,						lowerCamelCase_				:			Optional[Any]="gelu_new"      ,						lowerCamelCase_				:			Any=0.1      ,						lowerCamelCase_				:			Union[str, Any]=5_12      ,						lowerCamelCase_				:			Optional[int]=4      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			str=1e-12      ,						lowerCamelCase_				:			Any=False      ,						lowerCamelCase_				:			str=5_12      ,						lowerCamelCase_				:			str=3      ,						lowerCamelCase_				:			int=1      ,						lowerCamelCase_				:			Optional[Any]=2      ,						**lowerCamelCase_				:			Optional[int]      ,						):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_      ,						bos_token_id=lowerCamelCase_      ,						eos_token_id=lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			max_position_embeddings
							SCREAMING_SNAKE_CASE :				str		=			hidden_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			intermediate_size
							SCREAMING_SNAKE_CASE :				int		=			hidden_act
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			initializer_range
							SCREAMING_SNAKE_CASE :				List[Any]		=			type_vocab_size
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				int		=			use_tpu_fourier_optimizations
							SCREAMING_SNAKE_CASE :				List[Any]		=			tpu_short_seq_length



