






import numpy
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       numpy.ndarray						,_SCREAMING_SNAKE_CASE		:       numpy.ndarray		)    ->   None:


						'''simple docstring'''
						A	 =      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.
						A	 =      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.
						A	 =      numpy.random.rand(
						    4						,3		)

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

						# Real output values provided.
						A	 =      output_array

						# Predicted output values by the neural network.
						# Predicted_output array initially consists of zeroes.
						A	 =      numpy.zeros(output_array.shape		)
		def       A(  self		:       int		)    ->   numpy.ndarray:


						'''simple docstring'''
						A	 =      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.
						A	 =      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.
						A	 =      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		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      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		)						,)
						A	 =      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		)						,)
						A	 =      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		:       Dict						,_SCREAMING_SNAKE_CASE		:       numpy.ndarray						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       bool		)    ->   None:


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







		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       numpy.ndarray		)    ->   int:


						'''simple docstring'''
						A	 =      input_arr

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

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

						A	 =      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		(							UpperCAmelCase	:       numpy.ndarray   ):
				return 1 / (1 + numpy.exp(-value   ))







def      snake_case		(							UpperCAmelCase	:       numpy.ndarray   ):
				return (value) * (1 - (value))







def      snake_case		(							):
				A	 =      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.
				A	 =      numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]),						dtype=numpy.floataa   )

				# Calling neural network class.
				A	 =      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()













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





