






'''simple docstring'''







import numpy

class _lowercase	:




							def __init__(							self:				Any			,							UpperCamelCase__:				numpy.ndarray			,							UpperCamelCase__:				numpy.ndarray     ):
														lowerCamelCase__					:	Tuple         =       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__					:	Union[str, Any]         =       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__					:	List[Any]         =       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__					:	str         =       numpy.random.rand(3			,							1     )

														# Real output values provided.
														lowerCamelCase__					:	Dict         =       output_array

														# Predicted output values by the neural network.
														# Predicted_output array initially consists of zeroes.
														lowerCamelCase__					:	List[str]         =       numpy.zeros(output_array.shape     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Union[str, Any]         =       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__					:	Any         =       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__					:	Dict         =       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 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	int         =       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__					:	Dict         =       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__					:	Optional[Any]         =       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 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				numpy.ndarray			,							UpperCamelCase__:				int			,							UpperCamelCase__:				bool     ):
														for iteration in range(1			,							iterations + 1     ):
																					lowerCamelCase__					:	List[Any]         =       self.feedforward()
																					self.back_propagation()
																					if give_loss:
																												lowerCamelCase__					:	Any         =       numpy.mean(numpy.square(output - self.feedforward()     )     )
																												print(F'''Iteration {iteration} Loss: {loss}'''     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				numpy.ndarray     ):
														lowerCamelCase__					:	Dict         =       input_arr

														lowerCamelCase__					:	Optional[Any]         =       sigmoid(
														    numpy.dot(self.array			,							self.input_layer_and_first_hidden_layer_weights     )     )

														lowerCamelCase__					:	Optional[Any]         =       sigmoid(
														    numpy.dot(
														        self.layer_between_input_and_first_hidden_layer			,							self.first_hidden_layer_and_second_hidden_layer_weights			,							)     )

														lowerCamelCase__					:	Tuple         =       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  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       numpy.ndarray:
							return 1 / (1 + numpy.exp(-value      ))





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





def  SCREAMING_SNAKE_CASE_       ()      ->       int:
							lowerCamelCase__					:	Union[str, Any]         =       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__					:	str         =       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])				,			dtype=numpy.floataa      )

							# Calling neural network class.
							lowerCamelCase__					:	List[Any]         =       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'''







class _lowercase	:




							def __init__(							self:				Optional[Any]     ):
														lowerCamelCase__					:	dict[str, TrieNode]         =       {}  # Mapping from char to TrieNode
														lowerCamelCase__					:	List[str]         =       False




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				list[str]     ):
														for word in words:
																					self.insert(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	List[Any]         =       self
														for char in word:
																					if char not in curr.nodes:
																												lowerCamelCase__					:	Tuple         =       TrieNode()
																					lowerCamelCase__					:	List[Any]         =       curr.nodes[char]
														lowerCamelCase__					:	Any         =       True




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       self
														for char in word:
																					if char not in curr.nodes:
																												return False
																					lowerCamelCase__					:	Any         =       curr.nodes[char]
														return curr.is_leaf




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str     ):

														def _delete(UpperCamelCase__:				TrieNode			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int     ) -> bool:
																					if index == len(UpperCamelCase__     ):
																												# If word does not exist
																												if not curr.is_leaf:
																																			return False
																												lowerCamelCase__					:	str         =       False
																												return len(curr.nodes     ) == 0
																					lowerCamelCase__					:	List[str]         =       word[index]
																					lowerCamelCase__					:	Dict         =       curr.nodes.get(UpperCamelCase__     )
																					# If char not in current trie node
																					if not char_node:
																												return False
																					# Flag to check if node can be deleted
																					lowerCamelCase__					:	List[Any]         =       _delete(UpperCamelCase__			,							UpperCamelCase__			,							index + 1     )
																					if delete_curr:
																												del curr.nodes[char]
																												return len(curr.nodes     ) == 0
																					return delete_curr

														_delete(self			,							UpperCamelCase__			,							0     )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       None:
							if node.is_leaf:
														print(UpperCamelCase				,			end=""" """      )

							for key, value in node.nodes.items():
														print_words(UpperCamelCase				,			word + key      )





def  SCREAMING_SNAKE_CASE_       ()      ->       bool:
							lowerCamelCase__					:	str         =       """banana bananas bandana band apple all beast""".split()
							lowerCamelCase__					:	Union[str, Any]         =       TrieNode()
							root.insert_many(UpperCamelCase      )
							# print_words(root, "")
							assert all(root.find(UpperCamelCase      ) for word in words      )
							assert root.find("""banana"""      )
							assert not root.find("""bandanas"""      )
							assert not root.find("""apps"""      )
							assert root.find("""apple"""      )
							assert root.find("""all"""      )
							root.delete("""all"""      )
							assert not root.find("""all"""      )
							root.delete("""banana"""      )
							assert not root.find("""banana"""      )
							assert root.find("""bananas"""      )
							return True





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       None:
							print(str(UpperCamelCase      )				,			"""works!""" if passes else """doesn't work :("""      )





def  SCREAMING_SNAKE_CASE_       ()      ->       None:
							assert test_trie()





def  SCREAMING_SNAKE_CASE_       ()      ->       None:
							print_results("""Testing trie functionality"""				,			test_trie()      )


if __name__ == "__main__":
			main()

