

import numpy as np
from matplotlib import pyplot as plt
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Any )				-> Dict:





    '''simple docstring'''






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


class 					A		:







    """simple docstring"""





    def __init__( self					:			int,lowercase_					:			Union[str, Any],lowercase_					:			List[Any]=None,lowercase_					:			Tuple=None,lowercase_					:			str=False						)->       Optional[int]:





        '''simple docstring'''







        A__        =  units
        A__        =  None
        A__        =  None
        A__        =  activation
        if learning_rate is None:
            A__        =  0.3
        A__        =  learning_rate
        A__        =  is_input_layer


    def 		snake_case__	( self					:			List[Any],lowercase_					:			Union[str, Any]						)->       Dict:





        '''simple docstring'''







        A__        =  np.asmatrix(np.random.normal(0,0.5,(self.units, back_units)						)						)
        A__        =  np.asmatrix(np.random.normal(0,0.5,self.units						)						).T
        if self.activation is None:
            A__        =  sigmoid


    def 		snake_case__	( self					:			int						)->       Optional[int]:





        '''simple docstring'''







        if self.activation == sigmoid:
            A__        =  np.dot(self.output,(1 - self.output).T						)
            A__        =  np.diag(np.diag(lowercase_						)						)
        else:
            A__        =  1
        return gradient_activation


    def 		snake_case__	( self					:			List[Any],lowercase_					:			Optional[int]						)->       Tuple:





        '''simple docstring'''







        A__        =  xdata
        if self.is_input_layer:
            # input layer
            A__        =  xdata
            A__        =  xdata
            return xdata
        else:
            A__        =  np.dot(self.weight,self.xdata						) - self.bias
            A__        =  self.activation(self.wx_plus_b						)
            return self.output


    def 		snake_case__	( self					:			Union[str, Any],lowercase_					:			List[Any]						)->       Optional[int]:





        '''simple docstring'''







        A__        =  self.cal_gradient()  # i * i 维
        A__        =  np.asmatrix(np.dot(gradient.T,lowercase_						)						)

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

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







    """simple docstring"""





    def __init__( self					:			Dict						)->       Optional[int]:





        '''simple docstring'''







        A__        =  []
        A__        =  []
        A__        =  plt.figure()
        A__        =  self.fig_loss.add_subplot(1,1,1						)


    def 		snake_case__	( self					:			Optional[Any],lowercase_					:			int						)->       Dict:





        '''simple docstring'''







        self.layers.append(lowercase_						)


    def 		snake_case__	( self					:			List[Any]						)->       Dict:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			str						)->       List[str]:





        '''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 		snake_case__	( self					:			List[str],lowercase_					:			Optional[Any],lowercase_					:			int,lowercase_					:			Optional[int],lowercase_					:			Tuple						)->       str:





        '''simple docstring'''







        A__        =  train_round
        A__        =  accuracy

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

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

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

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

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

            A__        =  all_loss / x_shape[0]
            self.train_mse.append(lowercase_						)

            self.plot_loss()

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


    def 		snake_case__	( self					:			str,lowercase_					:			Tuple,lowercase_					:			Any						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  np.sum(np.power((ydata - ydata_),2						)						)
        A__        =  2 * (ydata_ - ydata)
        # vector (shape is the same as _ydata.shape)
        return self.loss, self.loss_gradient


    def 		snake_case__	( self					:			Any						)->       Union[str, 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 		_snake_case(       )				-> Optional[int]:





    '''simple docstring'''






    A__        =  np.random.randn(10       ,     10 )
    A__        =  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],
        ] )
    A__        =  BPNN()
    for i in (10, 20, 30, 2):
        model.add_layer(DenseLayer(SCREAMING_SNAKE_CASE__ ) )
    model.build()
    model.summary()
    model.train(xdata=SCREAMING_SNAKE_CASE__       ,     ydata=SCREAMING_SNAKE_CASE__       ,     train_round=100       ,     accuracy=0.01 )


if __name__ == "__main__":
     example()




import unittest

from cer import CER


lowercase_									=    CER()


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			str						)->       Dict:





        '''simple docstring'''







        A__        =  ['White House']
        A__        =  ['white house']
        # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.1_818_181_818						) < 1E-6						)


    def 		snake_case__	( self					:			str						)->       Dict:





        '''simple docstring'''







        A__        =  ['were wolf']
        A__        =  ['werewolf']
        # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.1_111_111						) < 1E-6						)

        A__        =  ['werewolf']
        A__        =  ['weae     wolf']
        # S = 1, D = 1, I = 0, N = 8, CER = 0.25
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.25						) < 1E-6						)

        # consecutive whitespaces case 1
        A__        =  ['were wolf']
        A__        =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.0						) < 1E-6						)

        # consecutive whitespaces case 2
        A__        =  ['were   wolf']
        A__        =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.0						) < 1E-6						)


    def 		snake_case__	( self					:			int						)->       str:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  ['weaewolf']
        # S = 1, D = 0, I = 0, N = 8, CER = 0.125
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.125						) < 1E-6						)


    def 		snake_case__	( self					:			Optional[int]						)->       Optional[int]:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  ['wereawolf']
        # S = 0, D = 1, I = 0, N = 8, CER = 0.125
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.125						) < 1E-6						)


    def 		snake_case__	( self					:			List[Any]						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  ['wereolf']
        # S = 0, D = 0, I = 1, N = 8, CER = 0.125
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.125						) < 1E-6						)


    def 		snake_case__	( self					:			str						)->       Tuple:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertEqual(lowercase_,0.0						)


    def 		snake_case__	( self					:			Optional[int]						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  ['werewolf', 'I am your father']
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertEqual(lowercase_,0.0						)

        A__        =  ['werewolf', 'I am your father', 'doge']
        A__        =  ['werxwolf', 'I       am your father', 'doge']
        # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.03_571_428						) < 1E-6						)


    def 		snake_case__	( self					:			List[str]						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  ['My hovercraft', 'is full of eels']
        A__        =  ['My hovercraft is full', ' of eels']
        # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
        # whitespace at the front of " of eels" will be strip during preporcessing
        # so need to insert 2 whitespaces
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_,concatenate_texts=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.071_428						) < 1E-6						)


    def 		snake_case__	( self					:			Dict						)->       List[str]:





        '''simple docstring'''







        A__        =  ['我能吞下玻璃而不伤身体']
        A__        =  [' 能吞虾玻璃而 不霜身体啦']
        # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.4_545_454_545						) < 1E-6						)

        A__        =  ['我能吞下玻璃', '而不伤身体']
        A__        =  ['我    能 吞 下 玻 璃', '而不伤身体']
        # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.454_545_454_545						) < 1E-6						)

        A__        =  ['我能吞下玻璃而不伤身体']
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertFalse(lowercase_,0.0						)


    def 		snake_case__	( self					:			Any						)->       List[str]:





        '''simple docstring'''







        A__        =  ['']
        A__        =  ['Hypothesis']
        with self.assertRaises(lowercase_						):
            cer.compute(predictions=lowercase_,references=lowercase_						)


if __name__ == "__main__":
     unittest.main()

