
# 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_   )		->	Any:


  '''simple docstring'''






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

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






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

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Any:


  '''simple docstring'''






  lowercase__  :   int    		=      np.dot(lowercase_ , lowercase_   )
  return np.sum(y * scores - np.log(1 + np.exp(lowercase_   )   )   )

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_=7_00_00   )		->	Tuple:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      np.zeros(x.shape[1]   )

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


# In[68]:

if __name__ == "__main__":
     lowerCamelCase__    :		Optional[int]          =   datasets.load_iris()
     lowerCamelCase__    :		Dict          =   iris.data[:, :2]
     lowerCamelCase__    :		str          =   (iris.target != 0) * 1

     lowerCamelCase__    :		Tuple          =   0.1
     lowerCamelCase__    :		List[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_   )		->	str:


       '''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""")
     ((lowerCamelCase__)      ,						(lowerCamelCase__))    :		Tuple          =   (x[:, 0].min(), x[:, 0].max())
     ((lowerCamelCase__)      ,						(lowerCamelCase__))    :		Optional[Any]          =   (x[:, 1].min(), x[:, 1].max())
     ((lowerCamelCase__)      ,						(lowerCamelCase__))    :		List[Any]          =   np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
     lowerCamelCase__    :		Union[str, Any]          =   np.c_[xxa.ravel(), xxa.ravel()]
     lowerCamelCase__    :		Optional[int]          =   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 ..bit import BitConfig


lowerCamelCase__    :		List[str]          =   logging.get_logger(__name__)

lowerCamelCase__    :		str          =   {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Optional[int]        =			'dpt'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=7_68					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=30_72					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=1E-12					,  SCREAMING_SNAKE_CASE_=3_84					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=[2, 5, 8, 11]					,  SCREAMING_SNAKE_CASE_="project"					,  SCREAMING_SNAKE_CASE_=[4, 2, 1, 0.5]					,  SCREAMING_SNAKE_CASE_=[96, 1_92, 3_84, 7_68]					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_=-1					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=0.4					,  SCREAMING_SNAKE_CASE_=2_55					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=[1, 10_24, 24, 24]					,  SCREAMING_SNAKE_CASE_=[0, 1]					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      hidden_size
   lowercase__  :   str    		=      is_hybrid

   if self.is_hybrid:
     if backbone_config is None:
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   Any    		=      {
           """global_padding""": """same""",
           """layer_type""": """bottleneck""",
           """depths""": [3, 4, 9],
           """out_features""": ["""stage1""", """stage2""", """stage3"""],
           """embedding_dynamic_padding""": True,
       }
       lowercase__  :   Optional[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   List[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       lowercase__  :   int    		=      backbone_config
     else:
       raise ValueError(
           f'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.')

     lowercase__  :   Optional[Any]    		=      backbone_featmap_shape
     lowercase__  :   List[str]    		=      neck_ignore_stages

     if readout_type != "project":
       raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""")
   else:
     lowercase__  :   Optional[int]    		=      None
     lowercase__  :   Union[str, Any]    		=      None
     lowercase__  :   Tuple    		=      []

   lowercase__  :   List[str]    		=      num_hidden_layers
   lowercase__  :   Union[str, Any]    		=      num_attention_heads
   lowercase__  :   List[Any]    		=      intermediate_size
   lowercase__  :   Optional[Any]    		=      hidden_act
   lowercase__  :   int    		=      hidden_dropout_prob
   lowercase__  :   str    		=      attention_probs_dropout_prob
   lowercase__  :   Dict    		=      initializer_range
   lowercase__  :   Tuple    		=      layer_norm_eps
   lowercase__  :   List[str]    		=      image_size
   lowercase__  :   Tuple    		=      patch_size
   lowercase__  :   Tuple    		=      num_channels
   lowercase__  :   Dict    		=      qkv_bias
   lowercase__  :   List[Any]    		=      backbone_out_indices
   if readout_type not in ["ignore", "add", "project"]:
     raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""")
   lowercase__  :   Dict    		=      readout_type
   lowercase__  :   Tuple    		=      reassemble_factors
   lowercase__  :   Any    		=      neck_hidden_sizes
   lowercase__  :   Dict    		=      fusion_hidden_size
   lowercase__  :   Tuple    		=      head_in_index
   lowercase__  :   Tuple    		=      use_batch_norm_in_fusion_residual
   # auxiliary head attributes (semantic segmentation)
   lowercase__  :   Tuple    		=      use_auxiliary_head
   lowercase__  :   int    		=      auxiliary_loss_weight
   lowercase__  :   Union[str, Any]    		=      semantic_loss_ignore_index
   lowercase__  :   str    		=      semantic_classifier_dropout



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      copy.deepcopy(self.__dict__)

   if output["backbone_config"] is not None:
     lowercase__  :   Optional[Any]    		=      self.backbone_config.to_dict()

   lowercase__  :   Dict    		=      self.__class__.model_type
   return output






