

import math
import random

def       a__					(		UpperCAmelCase							:      float    ,			UpperCAmelCase							:      bool = False      )  ->						float:
  if deriv:
    return value * (1 - value)
  return 1 / (1 + math.exp(-value      ))


# Initial Value
_lowerCamelCase    :       Optional[Any]        =							0.0_2

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      int      )  ->						float:
  UpperCAmelCase	:							int						=			float(2 * (random.randint(1    ,			100      )) - 1      )

  for _ in range(UpperCAmelCase      ):
    # Forward propagation
    UpperCAmelCase	:							List[str]						=			sigmoid_function(INITIAL_VALUE * weight      )
    # How much did we miss?
    UpperCAmelCase	:							Optional[Any]						=			(expected / 100) - layer_a
    # Error delta
    UpperCAmelCase	:							List[str]						=			layer_1_error * sigmoid_function(UpperCAmelCase    ,			UpperCAmelCase      )
    # Update weight
    weight += INITIAL_VALUE * layer_1_delta

  return layer_a * 100


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     _lowerCamelCase    :       int        =							int(input("Expected value: "))
     _lowerCamelCase    :       Optional[Any]        =							int(input("Number of propagations: "))
     print(forward_propagation(expected, number_propagations))








from ..utils import DummyObject, requires_backends





class  __UpperCAmelCase					(	metaclass=lowerCamelCase__						):
   UpperCamelCase       					=					["""onnx"""]



   def __init__(							self							:					int,		*__A							:					Optional[Any],		**__A							:					Dict						):
     requires_backends(self,		['''onnx''']						)



   @classmethod
   def 	__magic_name__   (							cls							:					Any,		*__A							:					Any,		**__A							:					Dict						):
     requires_backends(cls,		['''onnx''']						)





   @classmethod
   def 	__magic_name__   (							cls							:					Tuple,		*__A							:					List[str],		**__A							:					List[str]						):
     requires_backends(cls,		['''onnx''']						)





