
import numpy





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			numpy.ndarray							,A :			numpy.ndarray			):
					__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     UpperCamelCase_  (	self :			Optional[Any]			):
					__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     UpperCamelCase_  (	self :			str			):
					__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     UpperCamelCase_  (	self :			Tuple							,A :			numpy.ndarray							,A :			int							,A :			bool			):
					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     UpperCamelCase_  (	self :			List[Any]							,A :			numpy.ndarray			):
					__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 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return 1 / (1 + numpy.exp(-value						))


def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return (value) * (1 - (value))


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__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=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=1_0		,       give_loss=a_						)

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


if __name__ == "__main__":
					example()


import copy
import re





class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					"hp"
	snake_case_    =					{}
	snake_case_    =					None






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			Dict							,A :			Any			):
					__A             =			prefix
					__A             =			defaults
					cls.build_naming_info()






	@staticmethod
	def     UpperCamelCase_  (	A :			Dict							,A :			int			):
					if len(A			) == 0:
									return ""
					__A             =			None
					if any(char.isdigit() for char in word			):
									raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''			)
					if word in info["short_word"]:
									return info["short_word"][word]
					for prefix_len in range(1							,len(A			) + 1			):
									__A             =			word[:prefix_len]
									if prefix in info["reverse_short_word"]:
													continue
									else:
													__A             =			prefix
													break

					if short_word is None:
									# Paranoid fallback
									def int_to_alphabetic(A :			str			):
													__A             =			""
													while integer != 0:
																	__A             =			chr(ord("A"			) + integer % 10			) + s
																	integer //= 10
													return s

									__A             =			0
									while True:
													__A             =			word + "#" + int_to_alphabetic(A			)
													if sword in info["reverse_short_word"]:
																	continue
													else:
																	__A             =			sword
																	break

					__A             =			short_word
					__A             =			word
					return short_word






	@staticmethod
	def     UpperCamelCase_  (	A :			int							,A :			Tuple			):
					__A             =			param_name.split("_"			)

					__A             =			[TrialShortNamer.shortname_for_word(A							,A			) for word in words]

					# We try to create a separatorless short name, but if there is a collision we have to fallback
					# to a separated short name
					__A             =			["", "_"]

					for separator in separators:
									__A             =			separator.join(A			)
									if shortname not in info["reverse_short_param"]:
													__A             =			shortname
													__A             =			param_name
													return shortname

					return param_name






	@staticmethod
	def     UpperCamelCase_  (	A :			Optional[Any]							,A :			Tuple			):
					__A             =			TrialShortNamer.shortname_for_key(A							,A			)
					__A             =			short_name
					__A             =			param_name






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict			):
					if cls.NAMING_INFO is not None:
									return

					__A             =			{
					    "short_word": {},
					    "reverse_short_word": {},
					    "short_param": {},
					    "reverse_short_param": {},
					}

					__A             =			list(cls.DEFAULTS.keys()			)

					for k in field_keys:
									cls.add_new_param_name(A							,A			)

					__A             =			info






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			List[str]			):
					cls.build_naming_info()
					assert cls.PREFIX is not None
					__A             =			[copy.copy(cls.PREFIX			)]

					for k, v in params.items():
									if k not in cls.DEFAULTS:
													raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''			)
									if v == cls.DEFAULTS[k]:
													# The default value is not added to the name
													continue

									__A             =			cls.NAMING_INFO["short_param"][k]

									if isinstance(A							,A			):
													__A             =			1 if v else 0

									__A             =			"" if isinstance(A							,(int, float)			) else "-"
									__A             =			f'''{key}{sep}{v}'''
									name.append(A			)

					return "_".join(A			)







	@classmethod
	def     UpperCamelCase_  (	cls :			Tuple							,A :			Tuple			):
					__A             =			repr[len(cls.PREFIX			) + 1 :]
					if repr == "":
									__A             =			[]
					else:
									__A             =			repr.split("_"			)

					__A             =			{}

					for value in values:
									if "-" in value:
													__A      ,							__A             =			value.split("-"			)
									else:
													__A             =			re.sub("[0-9.]"							,""							,A			)
													__A             =			float(re.sub("[^0-9.]"							,""							,A			)			)

									__A             =			cls.NAMING_INFO["reverse_short_param"][p_k]

									__A             =			p_v

					for k in cls.DEFAULTS:
									if k not in parameters:
													__A             =			cls.DEFAULTS[k]

					return parameters
