



'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						Dict:







	"""simple docstring"""
	if not head:
		return True
	# split the list to two parts
	lowerCAmelCase,       lowerCAmelCase   =     head.next, head
	while fast and fast.next:
		lowerCAmelCase   =     fast.next.next
		lowerCAmelCase   =     slow.next
	lowerCAmelCase   =     slow.next
	lowerCAmelCase   =     None  # Don't forget here! But forget still works!
	# reverse the second part
	lowerCAmelCase   =     None
	while second:
		lowerCAmelCase   =     second.next
		lowerCAmelCase   =     node
		lowerCAmelCase   =     second
		lowerCAmelCase   =     nxt
	# compare two parts
	# second part has the same or one less node
	while node:
		if node.val != head.val:
			return False
		lowerCAmelCase   =     node.next
		lowerCAmelCase   =     head.next
	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						Optional[Any]:







	"""simple docstring"""
	if not head or not head.next:
		return True

	# 1. Get the midpoint (slow)
	lowerCAmelCase   =     lowerCAmelCase   =     lowerCAmelCase   =     head
	while fast and fast.next:
		lowerCAmelCase,       lowerCAmelCase   =     fast.next.next, slow.next

	# 2. Push the second half into the stack
	lowerCAmelCase   =     [slow.val]
	while slow.next:
		lowerCAmelCase   =     slow.next
		stack.append(slow.val							)

	# 3. Comparison
	while stack:
		if stack.pop() != cur.val:
			return False
		lowerCAmelCase   =     cur.next

	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						Dict:







	"""simple docstring"""
	if not head or not head.next:
		return True
	lowerCAmelCase   =     {}
	lowerCAmelCase   =     0
	while head:
		if head.val in d:
			d[head.val].append(_SCREAMING_SNAKE_CASE							)
		else:
			lowerCAmelCase   =     [pos]
		lowerCAmelCase   =     head.next
		pos += 1
	lowerCAmelCase   =     pos - 1
	lowerCAmelCase   =     0
	for v in d.values():
		if len(_SCREAMING_SNAKE_CASE							) % 2 != 0:
			middle += 1
		else:
			lowerCAmelCase   =     0
			for i in range(0							,		len(_SCREAMING_SNAKE_CASE							)							):
				if v[i] + v[len(_SCREAMING_SNAKE_CASE							) - 1 - step] != checksum:
					return False
				step += 1
		if middle > 1:
			return False
	return True




'''simple docstring'''

import numpy

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_				)     ->     None:
							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     __snake_case   ( self				)     ->     numpy.ndarray:
							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     __snake_case   ( self				)     ->     None:
							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     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     None:
							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     __snake_case   ( self     ,	A_				)     ->     int:
							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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







	"""simple docstring"""
	return 1 / (1 + numpy.exp(-value							))






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







	"""simple docstring"""
	return (value) * (1 - (value))






def 		_snake_case		( )      ->						int:







	"""simple docstring"""
	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()