


"""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  (  _snake_case :	Optional[int]	):
      return 1 / (1 + np.exp(-z	))


def    _snake_case  (  _snake_case :	Tuple       ,      _snake_case :	str	):
      return (-y * np.log(_snake_case	) - (1 - y) * np.log(1 - h	)).mean()


def    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	List[Any]       ,      _snake_case :	str	):
      lowerCAmelCase    :		Any				=      np.dot(_snake_case       ,      _snake_case	)
      return np.sum(y * scores - np.log(1 + np.exp(_snake_case	)	)	)


def    _snake_case  (  _snake_case :	Tuple       ,      _snake_case :	Dict       ,      _snake_case :	Any       ,      _snake_case :	int=70000	):
      lowerCAmelCase    :		int				=      np.zeros(x.shape[1]	)

      for iterations in range(_snake_case	):
            lowerCAmelCase    :		Dict				=      np.dot(_snake_case       ,      _snake_case	)
            lowerCAmelCase    :		int				=      sigmoid_function(_snake_case	)
            lowerCAmelCase    :		Dict				=      np.dot(x.T       ,      h - y	) / y.size
            lowerCAmelCase    :		str				=      theta - alpha * gradient  # updating the weights
            lowerCAmelCase    :		str				=      np.dot(_snake_case       ,      _snake_case	)
            lowerCAmelCase    :		Dict				=      sigmoid_function(_snake_case	)
            lowerCAmelCase    :		str				=      cost_function(_snake_case       ,      _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__						:   int			 = datasets.load_iris()
 snake_case__						:   Optional[Any]			 = iris.data[:, :2]
 snake_case__						:   Union[str, Any]			 = (iris.target != 0) * 1

 snake_case__						:   str			 = 0.1
 snake_case__						:   str			 = logistic_reg(alpha, x, y, max_iterations=70_000)
 print('''theta: ''', theta)  # printing the theta i.e our weights vector


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

 plt.legend()






 plt.show()






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=UpperCAmelCase		,     attention_head_dim=UpperCAmelCase		,     in_channels=UpperCAmelCase		,     num_layers=UpperCAmelCase		,     dropout=UpperCAmelCase		,     norm_num_groups=UpperCAmelCase		,     cross_attention_dim=UpperCAmelCase		,     attention_bias=UpperCAmelCase		,     sample_size=UpperCAmelCase		,     num_vector_embeds=UpperCAmelCase		,     activation_fn=UpperCAmelCase		,     num_embeds_ada_norm=UpperCAmelCase		,     )
              for _ in range(2       )
          ]       )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _lowercase							      =0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _lowercase							      =[7_7, 2_5_7]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _lowercase							      =[1, 0]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

      _lowercase							      =[]
      _lowercase							      =0
      # attention_mask is not used yet
      for i in range(2       ):
       # for each of the two transformers, pass the corresponding condition tokens
       _lowercase							      =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       _lowercase							      =self.transformer_index_for_condition[i]
       _lowercase							      =self.transformers[transformer_index](
           UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     timestep=UpperCAmelCase		,     cross_attention_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]
       encoded_states.append(encoded_state - input_states       )
       tokens_start += self.condition_lengths[i]

      _lowercase							      =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _lowercase							      =output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

