




import numpy
class     a__					:






						def __init__(     self	,    _A	,    _A    ):




											"""simple docstring"""



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

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

											# Real output values provided.
											__lowerCAmelCase            = output_array

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 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.
											__lowerCAmelCase            = 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.
											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 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    )	,    )
											__lowerCAmelCase            = 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    )	,    )
											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = input_arr

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

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

											__lowerCAmelCase            = 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   (		SCREAMING_SNAKE_CASE_							:       numpy.ndarray  ):
					return 1 / (1 + numpy.exp(-value  ))







def 			_a   (		SCREAMING_SNAKE_CASE_							:       numpy.ndarray  ):
					return (value) * (1 - (value))







def 			_a   (		):
					__lowerCAmelCase            = 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.
					__lowerCAmelCase            = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,   dtype=numpy.floataa  )

					# Calling neural network class.
					__lowerCAmelCase            = 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 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):
					if divisor % 5 == 0 or divisor % 2 == 0:
										return 0
					__lowerCAmelCase            = 1
					__lowerCAmelCase            = 1
					while repunit:
										__lowerCAmelCase            = (10 * repunit + 1) % divisor
										repunit_index += 1
					return repunit_index







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int = 1_00_00_00  ):
					__lowerCAmelCase            = limit - 1
					if divisor % 2 == 0:
										divisor += 1
					while least_divisible_repunit(SCREAMING_SNAKE_CASE_  ) <= limit:
										divisor += 2
					return divisor


if __name__ == "__main__":
					print(f'''{solution() = }''')




