



import numpy

class        UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A		):







													'''simple docstring'''

													__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 	_A     (   self		):







													'''simple docstring'''

													__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 	_A     (   self		):







													'''simple docstring'''

													__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 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													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 	_A     (   self				,							_A		):







													'''simple docstring'''

													__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      __lowercase       (			a__       )    ->			numpy.ndarray:
							return 1 / (1 + numpy.exp(-value       ))






def      __lowercase       (			a__       )    ->			numpy.ndarray:
							return (value) * (1 - (value))






def      __lowercase       (			)    ->			int:
							__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=a__							,       output_array=a__       )

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

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


if __name__ == "__main__":
			example()











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__		:					List[str]							={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TimesformerModel''',
			    '''TimesformerForVideoClassification''',
			    '''TimesformerPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_timesformer import (
						    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TimesformerForVideoClassification,
						    TimesformerModel,
						    TimesformerPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase__		:					Union[str, Any]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






