




import numpy







class a :
						"""simple docstring"""

						def __init__( self :     Any				,							lowerCamelCase__ :     numpy.ndarray				,							lowerCamelCase__ :     numpy.ndarray				)    ->						None:
													"""simple docstring"""


													__lowercase      =    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.
													__lowercase      =    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.
													__lowercase      =    numpy.random.rand(
													    4				,							3				)

													# Random initial values for the second hidden layer.
													# Second hidden layer has 3 nodes.
													# Output layer has 1 node.
													__lowercase      =    numpy.random.rand(3				,							1				)

													# Real output values provided.
													__lowercase      =    output_array

													# Predicted output values by the neural network.
													# Predicted_output array initially consists of zeroes.
													__lowercase      =    numpy.zeros(output_array.shape				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						numpy.ndarray:
													"""simple docstring"""


													__lowercase      =    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.
													__lowercase      =    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.
													__lowercase      =    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				)    ->						None:
													"""simple docstring"""


													__lowercase      =    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				)				,							)
													__lowercase      =    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				)				,							)
													__lowercase      =    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 :     Optional[int]				,							lowerCamelCase__ :     numpy.ndarray				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     bool				)    ->						None:
													"""simple docstring"""


													for iteration in range(1				,							iterations + 1				):
																				__lowercase      =    self.feedforward()
																				self.back_propagation()
																				if give_loss:
																											__lowercase      =    numpy.mean(numpy.square(output - self.feedforward()				)				)
																											print(f'Iteration {iteration} Loss: {loss}'				)






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     numpy.ndarray				)    ->						int:
													"""simple docstring"""


													__lowercase      =    input_arr

													__lowercase      =    sigmoid(
													    numpy.dot(self.array				,							self.input_layer_and_first_hidden_layer_weights				)				)

													__lowercase      =    sigmoid(
													    numpy.dot(
													        self.layer_between_input_and_first_hidden_layer				,							self.first_hidden_layer_and_second_hidden_layer_weights				,							)				)

													__lowercase      =    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 		_A(       UpperCamelCase__							:			numpy.ndarray				)    -> numpy.ndarray:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			numpy.ndarray				)    -> numpy.ndarray:
							'''simple docstring'''


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







def 		_A(       )    -> int:
							'''simple docstring'''


							__lowercase      =    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.
							__lowercase      =    numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])				,       dtype=numpy.floataa				)

							# Calling neural network class.
							__lowercase      =    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()












from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     float				)    ->						float:
													"""simple docstring"""


													return 0.0







def 		_A(       UpperCamelCase__							:			np.ndarray				,       UpperCamelCase__							:			int				)    -> tuple[int | float, int | float]:
							'''simple docstring'''


							__lowercase      =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
							__lowercase      =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
							return lowest, highest







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.abs(np.fft.fft(UpperCamelCase__				)				)
							__lowercase      =    20 * np.logaa(UpperCamelCase__				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							# Display within reasonable bounds
							__lowercase      =    get_bounds(UpperCamelCase__				,       UpperCamelCase__				)
							plt.ylim(max([-80, bounds[0]]				)				,       min([80, bounds[1]]				)				)
							plt.ylabel('''Gain (dB)'''				)

							plt.plot(UpperCamelCase__				)
							plt.show()







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.angle(np.fft.fft(UpperCamelCase__				)				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							plt.ylim(-2 * pi				,       2 * pi				)
							plt.ylabel('''Phase shift (Radians)'''				)
							plt.plot(np.unwrap(UpperCamelCase__				,       -2 * pi				)				)
							plt.show()






