



'''simple docstring'''



import numpy





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Tuple  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  str):







											'''simple docstring'''

											__lowercase       	=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.
											__lowercase       	=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.
											__lowercase       	=numpy.random.rand(
											    4  ,       3)

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

											# Real output values provided.
											__lowercase       	=output_array

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

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=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.
											__lowercase       	=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.
											__lowercase       	=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  :  Dict):







											'''simple docstring'''

											__lowercase       	=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)  ,       )
											__lowercase       	=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)  ,       )
											__lowercase       	=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  :  Any  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Dict  ,       _lowerCAmelCase  :  List[Any]):







											'''simple docstring'''

											for iteration in range(1  ,       iterations + 1):
																		__lowercase       	=self.feedforward()
																		self.back_propagation()
																		if give_loss:
																									__lowercase       	=numpy.mean(numpy.square(output - self.feedforward()))
																									print(f"""Iteration {iteration} Loss: {loss}""")

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  Dict):







											'''simple docstring'''

											__lowercase       	=input_arr

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

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

											__lowercase       	=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       (   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   ):



							"""simple docstring"""



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

							# Calling neural network class.
							__lowercase       	=TwoHiddenLayerNeuralNetwork(
							    input_array=_lowerCAmelCase  ,   output_array=_lowerCAmelCase					)

							# Calling training function.
							# Set give_loss to True if you want to see loss in every iteration.
							neural_network.train(output=_lowerCAmelCase  ,   iterations=10  ,   give_loss=_lowerCAmelCase					)

							return neural_network.predict(numpy.array(([1, 1, 1])  ,   dtype=numpy.floataa					)					)


if __name__ == "__main__":
							example()








'''simple docstring'''



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if number > 0:
														raise ValueError('input must be a negative integer'					)
							__lowercase       	=len(bin(_lowerCAmelCase					)[3:]					)
							__lowercase       	=bin(abs(_lowerCAmelCase					) - (1 << binary_number_length)					)[3:]
							__lowercase       	=(
							    (
							        '1'
							        + '0' * (binary_number_length - len(_lowerCAmelCase					))
							        + twos_complement_number
							    )
							    if number < 0
							    else '0'
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
		import doctest

		doctest.testmod()



