# 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 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase=70000							):







				"""simple docstring"""



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

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


# In[68]:

if __name__ == "__main__":
		__a  :     List[Any]       =  datasets.load_iris()
		__a  :     List[str]       =  iris.data[:, :2]
		__a  :     Union[str, Any]       =  (iris.target != 0) * 1

		__a  :     Union[str, Any]       =  0.1
		__a  :     Optional[Any]       =  logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
		print("""theta: """, theta)  # printing the theta i.e our weights vector





		def 				UpperCAmelCase						(    lowercase							):







						"""simple docstring"""



						return sigmoid_function(
						    np.dot(lowercase							,			lowercase							)							)  # 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""")
		((__a)    ,						(__a))  :     Any       =  (x[:, 0].min(), x[:, 0].max())
		((__a)    ,						(__a))  :     Optional[int]       =  (x[:, 1].min(), x[:, 1].max())
		((__a)    ,						(__a))  :     Optional[int]       =  np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
		__a  :     Tuple       =  np.c_[xxa.ravel(), xxa.ravel()]
		__a  :     Optional[Any]       =  predict_prob(grid).reshape(xxa.shape)
		plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

		plt.legend()




		plt.show()
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__a  :     int       =  logging.get_logger(__name__)

__a  :     str       =  {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[int]			 =						'''deta'''
			__a  : Optional[int]			 =						{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}





			def __init__(   self      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=9_00      ,   lowerCAmelCase__=20_48      ,   lowerCAmelCase__=6      ,   lowerCAmelCase__=20_48      ,   lowerCAmelCase__=8      ,   lowerCAmelCase__=6      ,   lowerCAmelCase__=10_24      ,   lowerCAmelCase__=8      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__="relu"      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1.0      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__="sine"      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=3_00      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=5      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.25      ,   **lowerCAmelCase__      ,   )     ->					List[Any]:

							'''simple docstring'''


							if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''						)
											__lowercase 			=							CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']						)
							else:
											if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						):
															__lowercase 			=							backbone_config.pop('''model_type'''						)
															__lowercase 			=							CONFIG_MAPPING[backbone_model_type]
															__lowercase 			=							config_class.from_dict(lowerCAmelCase__						)

							__lowercase 			=							backbone_config
							__lowercase 			=							num_queries
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							d_model
							__lowercase 			=							encoder_ffn_dim
							__lowercase 			=							encoder_layers
							__lowercase 			=							encoder_attention_heads
							__lowercase 			=							decoder_ffn_dim
							__lowercase 			=							decoder_layers
							__lowercase 			=							decoder_attention_heads
							__lowercase 			=							dropout
							__lowercase 			=							attention_dropout
							__lowercase 			=							activation_dropout
							__lowercase 			=							activation_function
							__lowercase 			=							init_std
							__lowercase 			=							init_xavier_std
							__lowercase 			=							encoder_layerdrop
							__lowercase 			=							auxiliary_loss
							__lowercase 			=							position_embedding_type
							# deformable attributes
							__lowercase 			=							num_feature_levels
							__lowercase 			=							encoder_n_points
							__lowercase 			=							decoder_n_points
							__lowercase 			=							two_stage
							__lowercase 			=							two_stage_num_proposals
							__lowercase 			=							with_box_refine
							__lowercase 			=							assign_first_stage
							if two_stage is True and with_box_refine is False:
											raise ValueError('''If two_stage is True, with_box_refine must be True.'''						)
							# Hungarian matcher
							__lowercase 			=							class_cost
							__lowercase 			=							bbox_cost
							__lowercase 			=							giou_cost
							# Loss coefficients
							__lowercase 			=							mask_loss_coefficient
							__lowercase 			=							dice_loss_coefficient
							__lowercase 			=							bbox_loss_coefficient
							__lowercase 			=							giou_loss_coefficient
							__lowercase 			=							eos_coefficient
							__lowercase 			=							focal_alpha
							super().__init__(is_encoder_decoder=lowerCAmelCase__      ,   **lowerCAmelCase__						)





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return self.encoder_attention_heads





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return self.d_model





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							copy.deepcopy(self.__dict__						)
							__lowercase 			=							self.backbone_config.to_dict()
							__lowercase 			=							self.__class__.model_type
							return output