

import numpy






class 						__magic_name__    :


 def __init__(						self		: Dict			,						lowerCAmelCase__		: numpy.ndarray			,						lowerCAmelCase__		: numpy.ndarray    )    ->      Any:
   UpperCAmelCase					      =  input_array

   # Random initial weights are assigned where first argument is the
   # number of nodes in previous layer and second argument is the
   # number of nodes in the next layer.

   # Random initial weights are assigned.
   # self.input_array.shape[1] is used to represent number of nodes in input layer.
   # First hidden layer consists of 4 nodes.
   UpperCAmelCase					      =  numpy.random.rand(
       self.input_array.shape[1]			,						4    )

   # Random initial values for the first hidden layer.
   # First hidden layer has 4 nodes.
   # Second hidden layer has 3 nodes.
   UpperCAmelCase					      =  numpy.random.rand(
       4			,						3    )

   # Random initial values for the second hidden layer.
   # Second hidden layer has 3 nodes.
   # Output layer has 1 node.
   UpperCAmelCase					      =  numpy.random.rand(3			,						1    )

   # Real output values provided.
   UpperCAmelCase					      =  output_array

   # Predicted output values by the neural network.
   # Predicted_output array initially consists of zeroes.
   UpperCAmelCase					      =  numpy.zeros(output_array.shape    )


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      List[Any]:
   UpperCAmelCase					      =  sigmoid(
       numpy.dot(self.input_array			,						self.input_layer_and_first_hidden_layer_weights    )    )

   # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
   # connecting the first hidden set of nodes with the second hidden set of nodes.
   UpperCAmelCase					      =  sigmoid(
       numpy.dot(
           self.layer_between_input_and_first_hidden_layer			,						self.first_hidden_layer_and_second_hidden_layer_weights			,						)    )

   # layer_between_second_hidden_layer_and_output is the layer connecting
   # second hidden layer with the output node.
   UpperCAmelCase					      =  sigmoid(
       numpy.dot(
           self.layer_between_first_hidden_layer_and_second_hidden_layer			,						self.second_hidden_layer_and_output_layer_weights			,						)    )

   return self.layer_between_second_hidden_layer_and_output


 def _UpperCamelCase	(						self		: Any    )    ->      int:
   UpperCAmelCase					      =  numpy.dot(
       self.layer_between_first_hidden_layer_and_second_hidden_layer.T			,						2
       * (self.output_array - self.predicted_output)
       * sigmoid_derivative(self.predicted_output    )			,						)
   UpperCAmelCase					      =  numpy.dot(
       self.layer_between_input_and_first_hidden_layer.T			,						numpy.dot(
           2
           * (self.output_array - self.predicted_output)
           * sigmoid_derivative(self.predicted_output    )			,						self.second_hidden_layer_and_output_layer_weights.T			,						)
       * sigmoid_derivative(
           self.layer_between_first_hidden_layer_and_second_hidden_layer    )			,						)
   UpperCAmelCase					      =  numpy.dot(
       self.input_array.T			,						numpy.dot(
           numpy.dot(
               2
               * (self.output_array - self.predicted_output)
               * sigmoid_derivative(self.predicted_output    )			,						self.second_hidden_layer_and_output_layer_weights.T			,						)
           * sigmoid_derivative(
               self.layer_between_first_hidden_layer_and_second_hidden_layer    )			,						self.first_hidden_layer_and_second_hidden_layer_weights.T			,						)
       * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer    )			,						)

   self.input_layer_and_first_hidden_layer_weights += (
       updated_input_layer_and_first_hidden_layer_weights
   )
   self.first_hidden_layer_and_second_hidden_layer_weights += (
       updated_first_hidden_layer_and_second_hidden_layer_weights
   )
   self.second_hidden_layer_and_output_layer_weights += (
       updated_second_hidden_layer_and_output_layer_weights
   )


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: numpy.ndarray			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: bool    )    ->      Optional[Any]:
   for iteration in range(1			,						iterations + 1    ):
     UpperCAmelCase					      =  self.feedforward()
     self.back_propagation()
     if give_loss:
       UpperCAmelCase					      =  numpy.mean(numpy.square(output - self.feedforward()    )    )
       print(f"Iteration {iteration} Loss: {loss}"    )


 def _UpperCamelCase	(						self		: Optional[int]			,						lowerCAmelCase__		: numpy.ndarray    )    ->      str:
   UpperCAmelCase					      =  input_arr

   UpperCAmelCase					      =  sigmoid(
       numpy.dot(self.array			,						self.input_layer_and_first_hidden_layer_weights    )    )

   UpperCAmelCase					      =  sigmoid(
       numpy.dot(
           self.layer_between_input_and_first_hidden_layer			,						self.first_hidden_layer_and_second_hidden_layer_weights			,						)    )

   UpperCAmelCase					      =  sigmoid(
       numpy.dot(
           self.layer_between_first_hidden_layer_and_second_hidden_layer			,						self.second_hidden_layer_and_output_layer_weights			,						)    )

   return int(self.layer_between_second_hidden_layer_and_output > 0.6    )

def      _lowerCAmelCase(	__A			):
  return 1 / (1 + numpy.exp(-value			))
def      _lowerCAmelCase(	__A			):
  return (value) * (1 - (value))
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  numpy.array(
      (
          [0, 0, 0],
          [0, 0, 1],
          [0, 1, 0],
          [0, 1, 1],
          [1, 0, 0],
          [1, 0, 1],
          [1, 1, 0],
          [1, 1, 1],
      )   ,       dtype=numpy.floataa   ,       )

  # True output values for the given input values.
  UpperCAmelCase					      =  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,       dtype=numpy.floataa			)

  # Calling neural network class.
  UpperCAmelCase					      =  TwoHiddenLayerNeuralNetwork(
      input_array=a_   ,       output_array=a_			)

  # Calling training function.
  # Set give_loss to True if you want to see loss in every iteration.
  neural_network.train(output=a_   ,       iterations=10   ,       give_loss=a_			)

  return neural_network.predict(numpy.array(([1, 1, 1])   ,       dtype=numpy.floataa			)			)


if __name__ == "__main__":
      example()






def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  while num > 0:
    digit_sum += num % 10
    num //= 10
  return digit_sum
def      _lowerCAmelCase(	__A = 100			):
  UpperCAmelCase					      =  1
  UpperCAmelCase					      =  2

  for i in range(2   ,       max_n + 1			):
    UpperCAmelCase					      =  pre_numerator
    UpperCAmelCase					      =  2 * i // 3 if i % 3 == 0 else 1
    UpperCAmelCase					      =  cur_numerator
    UpperCAmelCase					      =  e_cont * pre_numerator + temp

  return sum_digits(__A			)


if __name__ == "__main__":
      print(f"{solution() = }")



