
"""simple docstring"""





import numpy
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__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 	snake_case_     (     self):
				__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 	snake_case_     (     self):
				__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 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				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 	snake_case_     (     self							,  lowerCAmelCase__):
				__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 						_lowerCAmelCase				(				UpperCamelCase_				):
		return 1 / (1 + numpy.exp(-value				))



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		return (value) * (1 - (value))



def 						_lowerCAmelCase				(				):
		__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=UpperCamelCase_  ,				output_array=UpperCamelCase_				)

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

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


if __name__ == "__main__":
							example()






"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_ = 100				):
		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		for i in range(1  ,				n + 1				):
				sum_of_squares += i**2
				sum_of_ints += i
		return sum_of_ints**2 - sum_of_squares


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




