'''simple docstring'''

import numpy


class 				_lowercase	:
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  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__							:				Optional[Any]   				=  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__							:				Any   				=  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__							:				List[Any]   				=  numpy.random.rand(3					,1							)

         # Real output values provided.
         UpperCAmelCase__							:				Optional[int]   				=  output_array

         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
         UpperCAmelCase__							:				Union[str, Any]   				=  numpy.zeros(output_array.shape							)




      def 		lowerCAmelCase__					(				self							)			->			numpy.ndarray:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  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__							:				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.
         UpperCAmelCase__							:				Union[str, Any]   				=  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 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  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__							:				Optional[int]   				=  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__							:				List[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 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         for iteration in range(1					,iterations + 1							):
            UpperCAmelCase__							:				Dict   				=  self.feedforward()
            self.back_propagation()
            if give_loss:
               UpperCAmelCase__							:				List[str]   				=  numpy.mean(numpy.square(output - self.feedforward()							)							)
               print(f'''Iteration {iteration} Loss: {loss}'''							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  input_arr

         UpperCAmelCase__							:				Optional[int]   				=  sigmoid(
             numpy.dot(self.array					,self.input_layer_and_first_hidden_layer_weights							)							)

         UpperCAmelCase__							:				List[str]   				=  sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer					,self.first_hidden_layer_and_second_hidden_layer_weights					,)							)

         UpperCAmelCase__							:				str   				=  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 __UpperCamelCase(   _A					:				numpy.ndarray    ):
   '''simple docstring'''
   return 1 / (1 + numpy.exp(-value    ))







def __UpperCamelCase(   _A					:				numpy.ndarray    ):
   '''simple docstring'''
   return (value) * (1 - (value))







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  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__							:				Optional[int]   				=  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])  ,			dtype=numpy.floataa    )

   # Calling neural network class.
   UpperCAmelCase__							:				List[Any]   				=  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()





'''simple docstring'''

import re







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''

   if len(re.findall('''[ATCG]'''  ,			_A    )    ) != len(_A    ):
      raise ValueError('''Invalid Strand'''    )

   return dna.translate(dna.maketrans('''ATCG'''  ,			'''TAGC'''    )    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




