


"""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       (   UpperCamelCase__	)      ->		Union[str, Any]:







		"""simple docstring"""





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







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





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







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Optional[int]:







		"""simple docstring"""





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







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=70000	)      ->		Union[str, Any]:







		"""simple docstring"""





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

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

	UpperCamelCase						:				Union[str, Any]             =      0.1
	UpperCamelCase						:				Union[str, Any]             =      logistic_reg(alpha, x, y, max_iterations=70_000)
	print("theta: ", theta)  # printing the theta i.e our weights vector







	def  __snake_case       (   UpperCamelCase__	)      ->		Dict:







			"""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")
	((UpperCamelCase)      ,						(UpperCamelCase))						:				Optional[int]             =      (x[:, 0].min(), x[:, 0].max())
	((UpperCamelCase)      ,						(UpperCamelCase))						:				Dict             =      (x[:, 1].min(), x[:, 1].max())
	((UpperCamelCase)      ,						(UpperCamelCase))						:				List[Any]             =      np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
	UpperCamelCase						:				Any             =      np.c_[xxa.ravel(), xxa.ravel()]
	UpperCamelCase						:				Union[str, Any]             =      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						:				Tuple             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """lilt"""







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Dict=30_522			,			_lowercase   :    Any=768			,			_lowercase   :    Union[str, Any]=12			,			_lowercase   :    List[str]=12			,			_lowercase   :    str=3_072			,			_lowercase   :    int="gelu"			,			_lowercase   :    Union[str, Any]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    Optional[Any]=512			,			_lowercase   :    Optional[Any]=2			,			_lowercase   :    Tuple=0.0_2			,			_lowercase   :    int=1e-12			,			_lowercase   :    Any=0			,			_lowercase   :    List[str]="absolute"			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Optional[int]=1_024			,			**_lowercase   :    Union[str, Any]			,			):
								super().__init__(pad_token_id=_lowercase			,			**_lowercase		)

								A					       = vocab_size
								A					       = hidden_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = hidden_act
								A					       = intermediate_size
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = type_vocab_size
								A					       = initializer_range
								A					       = layer_norm_eps
								A					       = position_embedding_type
								A					       = classifier_dropout
								A					       = channel_shrink_ratio
								A					       = max_ad_position_embeddings





