
import numpy


class 				lowercase					:



		def __init__( self				,      A_				,      A_       )  ->					int:
				"""simple docstring"""
				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       )  ->					Dict:
				"""simple docstring"""
				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       )  ->					str:
				"""simple docstring"""
				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				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""






				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				,      A_       )  ->					str:
				"""simple docstring"""
				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       A       (					lowercase )   ->  Any:
		'''simple docstring'''

		return 1 / (1 + numpy.exp(-value ))


def       A       (					lowercase )   ->  Dict:
		'''simple docstring'''

		return (value) * (1 - (value))


def       A       (					)   ->  Any:
		'''simple docstring'''

		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							):







	'''simple docstring'''





	snake_case_		    =     len(_A							)
	for i in range(_A							):
		for j in range(i + 1     ,	_A							):
			if numbers[j] < numbers[i]:
				snake_case_    ,      snake_case_		    =     numbers[j], numbers[i]
	return numbers


if __name__ == "__main__":
		lowercase__				:     int 		=						input("Enter numbers separated by a comma:\n").strip()
		lowercase__				:     Any 		=						[int(item) for item in user_input.split(",")]
		print(exchange_sort(unsorted))
