





import numpy







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__							):



        '''simple docstring'''




        a__     : Dict  =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.
        a__     : int  =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.
        a__     : Tuple  =numpy.random.rand(
            4		,				3							)

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

        # Real output values provided.
        a__     : int  =output_array

        # Predicted output values by the neural network.
        # Predicted_output array initially consists of zeroes.
        a__     : Optional[int]  =numpy.zeros(output_array.shape							)







      def        _lowercase  (   self							):



        '''simple docstring'''




        a__     : Any  =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.
        a__     : Optional[Any]  =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.
        a__     : List[str]  =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        _lowercase  (   self							):



        '''simple docstring'''




        a__     : List[str]  =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							)		,				)
        a__     : Union[str, Any]  =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							)		,				)
        a__     : str  =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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							):



        '''simple docstring'''




        for iteration in range(1		,				iterations + 1							):
          a__     : Optional[Any]  =self.feedforward()
          self.back_propagation()
          if give_loss:
            a__     : Optional[int]  =numpy.mean(numpy.square(output - self.feedforward()							)							)
            print(F'''Iteration {iteration} Loss: {loss}'''							)







      def        _lowercase  (   self		,				lowerCAmelCase__							):



        '''simple docstring'''




        a__     : List[Any]  =input_arr

        a__     : Optional[Any]  =sigmoid(
            numpy.dot(self.array		,				self.input_layer_and_first_hidden_layer_weights							)							)

        a__     : Tuple  =sigmoid(
            numpy.dot(
                self.layer_between_input_and_first_hidden_layer		,				self.first_hidden_layer_and_second_hidden_layer_weights		,				)							)

        a__     : Optional[int]  =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 					_A	(		SCREAMING_SNAKE_CASE					:       numpy.ndarray						):





  """simple docstring"""



  return 1 / (1 + numpy.exp(-value						))
def 					_A	(		SCREAMING_SNAKE_CASE					:       numpy.ndarray						):





  """simple docstring"""



  return (value) * (1 - (value))
def 					_A	(		):





  """simple docstring"""



  a__     : List[Any]  =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.
  a__     : List[str]  =numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])	,	dtype=numpy.floataa						)

  # Calling neural network class.
  a__     : List[Any]  =TwoHiddenLayerNeuralNetwork(
      input_array=SCREAMING_SNAKE_CASE	,	output_array=SCREAMING_SNAKE_CASE						)

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

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


if __name__ == "__main__":
     example()













from maths.prime_check import is_prime
def 					_A	(		SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
    a__     : Dict  =f'''Input value of [number={number}] must be an integer'''
    raise TypeError(SCREAMING_SNAKE_CASE						)
  if is_prime(SCREAMING_SNAKE_CASE						) and is_prime(number + 2						):
    return number + 2
  else:
    return -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()






