



'''simple docstring'''

from typing import Any






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						dict							,		_SCREAMING_SNAKE_CASE					:						dict							,		_SCREAMING_SNAKE_CASE					:						dict							,		)      ->						list:







	"""simple docstring"""
	_validation(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		)
	# Creates data structures and fill initial step
	lowerCAmelCase   =     {}
	lowerCAmelCase   =     {}
	for state in states_space:
		lowerCAmelCase   =     observations_space[0]
		lowerCAmelCase   =     (
		    initial_probabilities[state] * emission_probabilities[state][observation]
		)
		lowerCAmelCase   =     None

	# Fills the data structure with the probabilities of
	# different transitions and pointers to previous states
	for o in range(1							,		len(_SCREAMING_SNAKE_CASE							)							):
		lowerCAmelCase   =     observations_space[o]
		lowerCAmelCase   =     observations_space[o - 1]
		for state in states_space:
			# Calculates the argmax for probability function
			lowerCAmelCase   =     """"""
			lowerCAmelCase   =     -1
			for k_state in states_space:
				lowerCAmelCase   =     (
				    probabilities[(k_state, prior_observation)]
				    * transition_probabilities[k_state][state]
				    * emission_probabilities[state][observation]
				)
				if probability > max_probability:
					lowerCAmelCase   =     probability
					lowerCAmelCase   =     k_state

            # Update probabilities and pointers dicts
			lowerCAmelCase   =     (
			    probabilities[(arg_max, prior_observation)]
			    * transition_probabilities[arg_max][state]
			    * emission_probabilities[state][observation]
			)

			lowerCAmelCase   =     arg_max

    # The final observation
	lowerCAmelCase   =     observations_space[len(_SCREAMING_SNAKE_CASE							) - 1]

	# argmax for given final observation
	lowerCAmelCase   =     """"""
	lowerCAmelCase   =     -1
	for k_state in states_space:
		lowerCAmelCase   =     probabilities[(k_state, final_observation)]
		if probability > max_probability:
			lowerCAmelCase   =     probability
			lowerCAmelCase   =     k_state
	lowerCAmelCase   =     arg_max

	# Process pointers backwards
	lowerCAmelCase   =     last_state
	lowerCAmelCase   =     []
	for o in range(len(_SCREAMING_SNAKE_CASE							) - 1							,		-1							,		-1							):
		result.append(_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     pointers[previous, observations_space[o]]
	result.reverse()

	return result






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		)      ->						None:







	"""simple docstring"""
	_validate_not_empty(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		)
	_validate_lists(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	_validate_dicts(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		)      ->						None:







	"""simple docstring"""
	if not all(
	    [
	        observations_space,
	        states_space,
	        initial_probabilities,
	        transition_probabilities,
	        emission_probabilities,
	    ]							):
		raise ValueError("""There's an empty parameter"""							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							)      ->						None:







	"""simple docstring"""
	_validate_list(_SCREAMING_SNAKE_CASE							,		"""observations_space"""							)
	_validate_list(_SCREAMING_SNAKE_CASE							,		"""states_space"""							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						None:







	"""simple docstring"""
	if not isinstance(_object							,		_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     f'{var_name} must be a list'
		raise ValueError(_SCREAMING_SNAKE_CASE							)
	else:
		for x in _object:
			if not isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
				lowerCAmelCase   =     f'{var_name} must be a list of strings'
				raise ValueError(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		)      ->						None:







	"""simple docstring"""
	_validate_dict(_SCREAMING_SNAKE_CASE							,		"""initial_probabilities"""							,		_SCREAMING_SNAKE_CASE							)
	_validate_nested_dict(_SCREAMING_SNAKE_CASE							,		"""transition_probabilities"""							)
	_validate_nested_dict(_SCREAMING_SNAKE_CASE							,		"""emission_probabilities"""							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						None:







	"""simple docstring"""
	_validate_dict(_object							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	for x in _object.values():
		_validate_dict(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						type							,		_SCREAMING_SNAKE_CASE					:						bool = False							)      ->						None:







	"""simple docstring"""
	if not isinstance(_object							,		_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     f'{var_name} must be a dict'
		raise ValueError(_SCREAMING_SNAKE_CASE							)
	if not all(isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) for x in _object							):
		lowerCAmelCase   =     f'{var_name} all keys must be strings'
		raise ValueError(_SCREAMING_SNAKE_CASE							)
	if not all(isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) for x in _object.values()							):
		lowerCAmelCase   =     """nested dictionary """ if nested else """"""
		lowerCAmelCase   =     f'{var_name} {nested_text}all values must be {value_type.__name__}'
		raise ValueError(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
				from doctest import testmod

				testmod()




'''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()