





_A :   dict[str, float]  			=  {
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.355_818,
}



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
      lowerCamelCase__       :						Optional[int]        =							(
          f"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
          f"Valid values are: {', '.join(UpperCAmelCase					)}"
      )
      raise ValueError(UpperCAmelCase					)
   return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
       import doctest

       doctest.testmod()










import math
import random



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    float:







   """simple docstring"""
   if deriv:
      return value * (1 - value)
   return 1 / (1 + math.exp(-value					))


# Initial Value
_A :   Tuple  			=  0.02



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							float(2 * (random.randint(1      ,  100					)) - 1					)

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

       _A :   Union[str, Any]  			=  int(input('Expected value: '))
       _A :   int  			=  int(input('Number of propagations: '))
       print(forward_propagation(expected, number_propagations))



