

"""simple docstring"""

import numpy as np
from matplotlib import pyplot as plt





def 				__UpperCamelCase   (    a__: Tuple			)				->			Optional[int]:




     """simple docstring"""



     return 1 / (1 + np.exp(-1 * x			))


class 			_snake_case     :







      def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=False							)			->						List[Any]:
           __lowercase												=			units
           __lowercase												=			None
           __lowercase												=			None
           __lowercase												=			activation
           if learning_rate is None:
                __lowercase												=			0.3
           __lowercase												=			learning_rate
           __lowercase												=			is_input_layer







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						int:
           __lowercase												=			np.asmatrix(np.random.normal(0     ,							0.5     ,							(self.units, back_units)							)							)
           __lowercase												=			np.asmatrix(np.random.normal(0     ,							0.5     ,							self.units							)							).T
           if self.activation is None:
                __lowercase												=			sigmoid







      def        _UpperCamelCase    (self							)			->						Union[str, Any]:
           # activation function may be sigmoid or linear
           if self.activation == sigmoid:
                __lowercase												=			np.dot(self.output     ,							(1 - self.output).T							)
                __lowercase												=			np.diag(np.diag(UpperCAmelCase_							)							)
           else:
                __lowercase												=			1
           return gradient_activation







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Any:
           __lowercase												=			xdata
           if self.is_input_layer:
                # input layer
                __lowercase												=			xdata
                __lowercase												=			xdata
                return xdata
           else:
                __lowercase												=			np.dot(self.weight     ,							self.xdata							) - self.bias
                __lowercase												=			self.activation(self.wx_plus_b							)
                return self.output







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Optional[Any]:
           __lowercase												=			self.cal_gradient()  # i * i 维
           __lowercase												=			np.asmatrix(np.dot(gradient.T     ,							UpperCAmelCase_							)							)

           __lowercase												=			np.asmatrix(self.xdata							)
           __lowercase												=			-1
           __lowercase												=			self.weight

           __lowercase												=			np.dot(gradient.T     ,							self._gradient_weight.T							)
           __lowercase												=			gradient * self._gradient_bias
           __lowercase												=			np.dot(UpperCAmelCase_     ,							self._gradient_x							).T
           # upgrade: the Negative gradient direction
           __lowercase												=			self.weight - self.learn_rate * self.gradient_weight
           __lowercase												=			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 			_snake_case     :







      def __init__(self							)			->						Union[str, Any]:
           __lowercase												=			[]
           __lowercase												=			[]
           __lowercase												=			plt.figure()
           __lowercase												=			self.fig_loss.add_subplot(1     ,							1     ,							1							)







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						int:
           self.layers.append(UpperCAmelCase_							)







      def        _UpperCamelCase    (self							)			->						str:
           for i, layer in enumerate(self.layers[:]							):
                if i < 1:
                     __lowercase												=			True
                else:
                     layer.initializer(self.layers[i - 1].units							)







      def        _UpperCamelCase    (self							)			->						Optional[int]:
           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     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						Dict:
           __lowercase												=			train_round
           __lowercase												=			accuracy

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

           __lowercase												=			np.shape(UpperCAmelCase_							)
           for _ in range(UpperCAmelCase_							):
                __lowercase												=			0
                for row in range(x_shape[0]							):
                     __lowercase												=			np.asmatrix(xdata[row, :]							).T
                     __lowercase												=			np.asmatrix(ydata[row, :]							).T

                     # forward propagation
                     for layer in self.layers:
                          __lowercase												=			layer.forward_propagation(_xdata							)

                     __lowercase						, __lowercase												=			self.cal_loss(_ydata     ,							_xdata							)
                     __lowercase												=			all_loss + loss

                     # back propagation: the input_layer does not upgrade
                     for layer in self.layers[:0:-1]:
                          __lowercase												=			layer.back_propagation(UpperCAmelCase_							)

                __lowercase												=			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     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						Optional[int]:
           __lowercase												=			np.sum(np.power((ydata - ydata_)     ,							2							)							)
           __lowercase												=			2 * (ydata_ - ydata)
           # vector (shape is the same as _ydata.shape)
           return self.loss, self.loss_gradient







      def        _UpperCamelCase    (self							)			->						Optional[int]:
           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 				__UpperCamelCase   (    )				->			Any:




     """simple docstring"""



     __lowercase												=			np.random.randn(10 ,	10			)
     __lowercase												=			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],
         ]			)
     __lowercase												=			BPNN()
     for i in (10, 20, 30, 2):
          model.add_layer(DenseLayer(a__			)			)
     model.build()
     model.summary()
     model.train(xdata=a__ ,	ydata=a__ ,	train_round=1_00 ,	accuracy=0.01			)


if __name__ == "__main__":
       example()





"""simple docstring"""

import unittest
import warnings

from datasets.utils import experimental





@experimental
def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					return "success"


class 			_snake_case     (						unittest.TestCase       ):







						def        _UpperCamelCase    (self							)			->						Tuple:
											with warnings.catch_warnings(record=UpperCAmelCase_							) as w:
																warnings.simplefilter('''always'''							)
																self.assertEqual(dummy_function()     ,							'''success'''							)
											self.assertEqual(len(UpperCAmelCase_							)     ,							1							)


