






import numpy



class 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						None:
           __lowerCAmelCase             =       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.
           __lowerCAmelCase             =       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.
           __lowerCAmelCase             =       numpy.random.rand(
               4       ,    3						)

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

           # Real output values provided.
           __lowerCAmelCase             =       output_array

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


      def 			__UpperCAmelCase		(						self						)->						numpy.ndarray:
           __lowerCAmelCase             =       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.
           __lowerCAmelCase             =       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.
           __lowerCAmelCase             =       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						)->						None:
           __lowerCAmelCase             =       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						)       ,    )
           __lowerCAmelCase             =       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						)       ,    )
           __lowerCAmelCase             =       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       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						None:
           for iteration in range(1       ,    iterations + 1						):
                __lowerCAmelCase             =       self.feedforward()
                self.back_propagation()
                if give_loss:
                     __lowerCAmelCase             =       numpy.mean(numpy.square(output - self.feedforward()						)						)
                     print(F"""Iteration {iteration} Loss: {loss}"""						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						int:
           __lowerCAmelCase             =       input_arr

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

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

           __lowerCAmelCase             =       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   (    __snake_case							):
     return 1 / (1 + numpy.exp(-value							))





def    __lowerCAmelCase   (    __snake_case							):
     return (value) * (1 - (value))





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       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.
     __lowerCAmelCase             =       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])       ,			dtype=numpy.floataa							)

     # Calling neural network class.
     __lowerCAmelCase             =       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()










class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				__a				:				int							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= n
          lowerCamelCase_						:   Dict		= [None] * self.n
          lowerCamelCase_						:   int		= 0  # index of the first element
          lowerCamelCase_						:   str		= 0
          lowerCamelCase_						:   Optional[Any]		= 0



       def __len__(						self				:				List[str]							)					->int:
          return self.size



       def 				_lowerCAmelCase							(						self				:				str							)					->bool:
          return self.size == 0



       def 				_lowerCAmelCase							(						self				:				str							)					->Any:
          return False if self.is_empty() else self.array[self.front]



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				int							)					->List[Any]:
          if self.size >= self.n:
             raise Exception("""QUEUE IS FULL"""							)

          lowerCamelCase_						:   Tuple		= data
          lowerCamelCase_						:   Tuple		= (self.rear + 1) % self.n
          self.size += 1
          return self




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->int:
          if self.size == 0:
             raise Exception("""UNDERFLOW"""							)

          lowerCamelCase_						:   str		= self.array[self.front]
          lowerCamelCase_						:   str		= None
          lowerCamelCase_						:   Optional[Any]		= (self.front + 1) % self.n
          self.size -= 1
          return temp





