

import numpy






class        UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		Any	,   __lowerCamelCase						:		numpy.ndarray	,   __lowerCamelCase						:		numpy.ndarray					)		->							Any:
       SCREAMING_SNAKE_CASE__    =    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.
       SCREAMING_SNAKE_CASE__    =    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.
       SCREAMING_SNAKE_CASE__    =    numpy.random.rand(
           4	,   3					)

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

       # Real output values provided.
       SCREAMING_SNAKE_CASE__    =    output_array

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






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







  def       lowercase_		(							self						:		Tuple	,   __lowerCamelCase						:		numpy.ndarray					)		->							List[str]:
       SCREAMING_SNAKE_CASE__    =    input_arr

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

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

       SCREAMING_SNAKE_CASE__    =    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				):





     '''simple docstring'''




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





     '''simple docstring'''




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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__    =    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.
     SCREAMING_SNAKE_CASE__    =    numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])      ,		dtype=numpy.floataa				)

     # Calling neural network class.
     SCREAMING_SNAKE_CASE__    =    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 math
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					list[list[float]]					,				__a     :					list[int]					):
								_a								= 0.0
								_a								= 0.0
								for i in range(len(__a					)					):
												da += math.pow((sample[i] - weights[0][i])					,				2					)
												da += math.pow((sample[i] - weights[1][i])					,				2					)
												return 0 if da > da else 1
								return 0




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					list[list[int | float]]					,				__a     :					list[int]					,				__a     :					int					,				__a     :					float					):
								for i in range(len(__a					)					):
												weights[j][i] += alpha * (sample[i] - weights[j][i])
								return weights






def  _lowerCamelCase				( )	->	None:
				# Training Examples ( m, n )
				_a								= [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

				# weight initialization ( n, C )
				_a								= [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

				# training
				_a								= SelfOrganizingMap()
				_a								= 3
				_a								= 0.5

				for _ in range(lowercase						):
								for j in range(len(lowercase						)						):
												# training sample
												_a								= training_samples[j]

												# Compute the winning vector
												_a								= self_organizing_map.get_winner(lowercase    ,							lowercase						)

												# Update the winning vector
												_a								= self_organizing_map.update(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

    # classify test sample
				_a								= [0, 0, 0, 1]
				_a								= self_organizing_map.get_winner(lowercase    ,							lowercase						)

				# results
				print(F'Clusters that the test sample belongs to : {winner}'						)
				print(F'Weights that have been trained : {weights}'						)


# running the main() function
if __name__ == "__main__":
					main()
