



"""simple docstring"""






import numpy

class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  Optional[Any],  lowerCamelCase							:  numpy.ndarray,  lowerCamelCase							:  numpy.ndarray					)-> None:
								lowerCamelCase__		:		str		     =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__		:		List[str]		     =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__		:		Tuple		     =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__		:		int		     =numpy.random.rand(3,  1					)

								# Real output values provided.
								lowerCamelCase__		:		str		     =output_array

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



					def 		snake_case	(	self							:  Tuple					)-> numpy.ndarray:
								lowerCamelCase__		:		Optional[int]		     =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__		:		List[Any]		     =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							:  str					)-> None:
								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[int]		     =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							:  List[Any],  lowerCamelCase							:  numpy.ndarray,  lowerCamelCase							:  int,  lowerCamelCase							:  bool					)-> None:
								for iteration in range(1,  iterations + 1					):
											lowerCamelCase__		:		Any		     =self.feedforward()
											self.back_propagation()
											if give_loss:
														lowerCamelCase__		:		Optional[Any]		     =numpy.mean(numpy.square(output - self.feedforward()					)					)
														print(F'''Iteration {iteration} Loss: {loss}'''					)




					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  numpy.ndarray					)-> int:
								lowerCamelCase__		:		Any		     =input_arr

								lowerCamelCase__		:		int		     =sigmoid(
								    numpy.dot(self.array,  self.input_layer_and_first_hidden_layer_weights					)					)

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

								lowerCamelCase__		:		Any		     =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__       (		__lowerCamelCase    :   numpy.ndarray     ):
			"""simple docstring"""
			return 1 / (1 + numpy.exp(-value     ))






def 					snake_case__       (		__lowerCamelCase    :   numpy.ndarray     ):
			"""simple docstring"""
			return (value) * (1 - (value))






def 					snake_case__       (		):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple		     =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__		:		int		     =numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,		dtype=numpy.floataa     )

			# Calling neural network class.
			lowerCamelCase__		:		Dict		     =TwoHiddenLayerNeuralNetwork(
			    input_array=__lowerCamelCase							,		output_array=__lowerCamelCase     )

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

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


if __name__ == "__main__":
		example()









"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp






def 					snake_case__       (		__lowerCamelCase    :   jnp.ndarray							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1.0e4							,		__lowerCamelCase    :   bool = False							,		__lowerCamelCase    :   float = 1.0							,		):
			"""simple docstring"""
			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
			lowerCamelCase__		:		Any		     =float(embedding_dim // 2     )
			lowerCamelCase__		:		List[str]		     =math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			lowerCamelCase__		:		int		     =min_timescale * jnp.exp(jnp.arange(__lowerCamelCase							,		dtype=jnp.floataa     ) * -log_timescale_increment     )
			lowerCamelCase__		:		Tuple		     =jnp.expand_dims(__lowerCamelCase							,		1     ) * jnp.expand_dims(__lowerCamelCase							,		0     )

			# scale embeddings
			lowerCamelCase__		:		List[str]		     =scale * emb

			if flip_sin_to_cos:
						lowerCamelCase__		:		int		     =jnp.concatenate([jnp.cos(__lowerCamelCase     ), jnp.sin(__lowerCamelCase     )]							,		axis=1     )
			else:
						lowerCamelCase__		:		List[str]		     =jnp.concatenate([jnp.sin(__lowerCamelCase     ), jnp.cos(__lowerCamelCase     )]							,		axis=1     )
			lowerCamelCase__		:		str		     =jnp.reshape(__lowerCamelCase							,		[jnp.shape(__lowerCamelCase     )[0], embedding_dim]     )
			return signal

class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							jnp.floataa



					@nn.compact
					def __call__(	self							:  Optional[Any],  lowerCamelCase							:  int					)-> Any:
								lowerCamelCase__		:		Optional[Any]		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_1'''					)(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =nn.silu(lowerCamelCase					)
								lowerCamelCase__		:		Any		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_2'''					)(lowerCamelCase					)
								return temb





class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							False
					_a             =							1



					@nn.compact
					def __call__(	self							:  Any,  lowerCamelCase							:  int					)-> int:
								return get_sinusoidal_embeddings(
								    lowerCamelCase,  embedding_dim=self.dim,  flip_sin_to_cos=self.flip_sin_to_cos,  freq_shift=self.freq_shift					)




