



'''simple docstring'''




import numpy as np
from matplotlib import pyplot as plt


def     lowerCAmelCase_       (     _lowerCamelCase:       Any ):
		return 1 / (1 + np.exp(-1 * x ))






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			int=None							,					lowerCAmelCase__					:			List[str]=None							,					lowerCAmelCase__					:			List[Any]=False						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		units
					__SCREAMING_SNAKE_CASE	:		Any   	=		None
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		activation
					if learning_rate is None:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		0.3
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		learning_rate
					__SCREAMING_SNAKE_CASE	:		str   	=		is_input_layer

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		np.asmatrix(np.random.normal(0							,					0.5							,					(self.units, back_units)						)						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		np.asmatrix(np.random.normal(0							,					0.5							,					self.units						)						).T
					if self.activation is None:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		sigmoid

			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					if self.activation == sigmoid:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		np.dot(self.output							,					(1 - self.output).T						)
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		np.diag(np.diag(UpperCamelCase_						)						)
					else:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		1
					return gradient_activation

			def UpperCamelCase__ (							self					:			Tuple							,					lowerCAmelCase__					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		xdata
					if self.is_input_layer:
							# input layer
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		xdata
							__SCREAMING_SNAKE_CASE	:		int   	=		xdata
							return xdata
					else:
							__SCREAMING_SNAKE_CASE	:		int   	=		np.dot(self.weight							,					self.xdata						) - self.bias
							__SCREAMING_SNAKE_CASE	:		Dict   	=		self.activation(self.wx_plus_b						)
							return self.output



			def UpperCamelCase__ (							self					:			Any							,					lowerCAmelCase__					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		self.cal_gradient()  # i * i 维
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.asmatrix(np.dot(gradient.T							,					UpperCamelCase_						)						)

					__SCREAMING_SNAKE_CASE	:		int   	=		np.asmatrix(self.xdata						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		-1
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.weight

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		np.dot(gradient.T							,					self._gradient_weight.T						)
					__SCREAMING_SNAKE_CASE	:		str   	=		gradient * self._gradient_bias
					__SCREAMING_SNAKE_CASE	:		Any   	=		np.dot(UpperCamelCase_							,					self._gradient_x						).T
					# upgrade: the Negative gradient direction
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.weight - self.learn_rate * self.gradient_weight
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.bias - self.learn_rate * self.gradient_bias.T
					# updates the weights and bias according to learning rate (0.3 if undefined)
					return self.gradient






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		[]
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		[]
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		plt.figure()
					__SCREAMING_SNAKE_CASE	:		int   	=		self.fig_loss.add_subplot(1							,					1							,					1						)

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Optional[Any]						):





					"""simple docstring"""






					self.layers.append(UpperCamelCase_						)

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for i, layer in enumerate(self.layers[:]						):
							if i < 1:
									__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True
							else:
									layer.initializer(self.layers[i - 1].units						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for i, layer in enumerate(self.layers[:]						):
							print(F"------- layer {i} -------"						)
							print("""weight.shape """							,					np.shape(layer.weight						)						)
							print("""bias.shape """							,					np.shape(layer.bias						)						)

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			Union[str, Any]							,					lowerCAmelCase__					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		train_round
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		accuracy

					self.ax_loss.hlines(self.accuracy							,					0							,					self.train_round * 1.1						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.shape(UpperCamelCase_						)
					for _ in range(UpperCamelCase_						):
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		0
							for row in range(x_shape[0]						):
									__SCREAMING_SNAKE_CASE	:		Tuple   	=		np.asmatrix(xdata[row, :]						).T
									__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		np.asmatrix(ydata[row, :]						).T

									# forward propagation
									for layer in self.layers:
											__SCREAMING_SNAKE_CASE	:		List[Any]   	=		layer.forward_propagation(_xdata						)

									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		self.cal_loss(_ydata							,					_xdata						)
									__SCREAMING_SNAKE_CASE	:		str   	=		all_loss + loss

									# back propagation: the input_layer does not upgrade
									for layer in self.layers[:0:-1]:
											__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		layer.back_propagation(UpperCamelCase_						)

							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		all_loss / x_shape[0]
							self.train_mse.append(UpperCamelCase_						)

							self.plot_loss()

							if mse < self.accuracy:
									print("""----达到精度----"""						)
									return mse
					return None

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.sum(np.power((ydata - ydata_)							,					2						)						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		2 * (ydata_ - ydata)
					# vector (shape is the same as _ydata.shape)
					return self.loss, self.loss_gradient



			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					if self.ax_loss.lines:
							self.ax_loss.lines.remove(self.ax_loss.lines[0]						)
					self.ax_loss.plot(self.train_mse							,					"""r-"""						)
					plt.ion()
					plt.xlabel("""step"""						)
					plt.ylabel("""loss"""						)
					plt.show()
					plt.pause(0.1						)


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.random.randn(10	,				10 )
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.asarray(
		    [
		        [0.8, 0.4],
		        [0.4, 0.3],
		        [0.34, 0.45],
		        [0.67, 0.32],
		        [0.88, 0.67],
		        [0.78, 0.77],
		        [0.55, 0.66],
		        [0.55, 0.43],
		        [0.54, 0.1],
		        [0.1, 0.5],
		    ] )
		__SCREAMING_SNAKE_CASE	:		str   	=		BPNN()
		for i in (10, 20, 30, 2):
				model.add_layer(DenseLayer(__lowercase ) )
		model.build()
		model.summary()
		model.train(xdata=__lowercase	,				ydata=__lowercase	,				train_round=1_00	,				accuracy=0.01 )


if __name__ == "__main__":
						example()




'''simple docstring'''




UpperCamelCase__	:     List[Any]    =			10_00


def     lowerCAmelCase_       (     _lowerCamelCase:       float ):
		return x * x


def     lowerCAmelCase_       (     _lowerCamelCase:       Any	,				_lowerCamelCase:       float	,				_lowerCamelCase:       float	,				_lowerCamelCase:       int = 4 ):
		assert callable(
		    _lowerCamelCase ), F"the function(object) passed should be callable your input : {function}"
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"a should be float or integer your input : {a}"
		assert isinstance(function(_lowerCamelCase )	,				(float, int) ), (
		    "the function should return integer or float return type of your function, "
		    F"{type(_lowerCamelCase )}"
		)
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"b should be float or integer your input : {b}"
		assert (
		    isinstance(_lowerCamelCase	,				_lowerCamelCase ) and precision > 0
		), F"precision should be positive integer your input : {precision}"

		# just applying the formula of simpson for approximate integration written in
		# mentioned article in first comment of this file and above this function

		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(b - a) / N_STEPS
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		function(_lowerCamelCase ) + function(_lowerCamelCase )

		for i in range(1	,				_lowerCamelCase ):
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		a + h * i
				result += function(_lowerCamelCase ) * (4 if i % 2 else 2)

		result *= h / 3
		return round(_lowerCamelCase	,				_lowerCamelCase )


if __name__ == "__main__":
						import doctest

						doctest.testmod()