



import numpy



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							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    A__		(			self     ):
							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    A__		(			self     ):
							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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							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    A__		(			self    ,	lowerCAmelCase     ):
							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 snake_case__  (			__SCREAMING_SNAKE_CASE )		->							numpy.ndarray:
		return 1 / (1 + numpy.exp(-value ))







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							numpy.ndarray:
		return (value) * (1 - (value))







def snake_case__  (			)		->							int:
		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=__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()






def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				int(__SCREAMING_SNAKE_CASE )
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_					,						UpperCAmelCase_						=				divmod(__SCREAMING_SNAKE_CASE			,     2 )
		return binary_recursive(__SCREAMING_SNAKE_CASE ) + str(__SCREAMING_SNAKE_CASE )







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				str(__SCREAMING_SNAKE_CASE ).strip()
		if not number:
				raise ValueError("No input value was provided" )
		UpperCAmelCase_						=				"-" if number.startswith("-" ) else ""
		UpperCAmelCase_						=				number.lstrip("-" )
		if not number.isnumeric():
				raise ValueError("Input value is not an integer" )
		return f'''{negative}0b{binary_recursive(int(__SCREAMING_SNAKE_CASE ) )}'''


if __name__ == "__main__":
			from doctest import testmod

			testmod()

