






"""simple docstring"""





import numpy as np
from matplotlib import pyplot as plt



def      __lowercase       (     a			:					Dict			)       ->       Optional[int]:
      return 1 / (1 + np.exp(-1 * x			))


class   _lowercase			:


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





            """simple docstring"""







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


      def _UpperCamelCase    (     self					:						Dict				, a					:						str    ):





            """simple docstring"""







            __snake_case   :     int	      =np.asmatrix(np.random.normal(0				, 0.5				, (self.units, back_units)    )    )
            __snake_case   :     int	      =np.asmatrix(np.random.normal(0				, 0.5				, self.units    )    ).T
            if self.activation is None:
                  __snake_case   :     Union[str, Any]	      =sigmoid


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            if self.activation == sigmoid:
                  __snake_case   :     Dict	      =np.dot(self.output				, (1 - self.output).T    )
                  __snake_case   :     int	      =np.diag(np.diag(a    )    )
            else:
                  __snake_case   :     Optional[int]	      =1
            return gradient_activation


      def _UpperCamelCase    (     self					:						int				, a					:						List[str]    ):





            """simple docstring"""







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







      def _UpperCamelCase    (     self					:						List[Any]				, a					:						List[Any]    ):





            """simple docstring"""







            __snake_case   :     Optional[Any]	      =self.cal_gradient()  # i * i 维
            __snake_case   :     Dict	      =np.asmatrix(np.dot(gradient.T				, a    )    )

            __snake_case   :     str	      =np.asmatrix(self.xdata    )
            __snake_case   :     Optional[int]	      =-1
            __snake_case   :     Tuple	      =self.weight

            __snake_case   :     Any	      =np.dot(gradient.T				, self._gradient_weight.T    )
            __snake_case   :     Optional[Any]	      =gradient * self._gradient_bias
            __snake_case   :     int	      =np.dot(a				, self._gradient_x    ).T
            # upgrade: the Negative gradient direction
            __snake_case   :     Tuple	      =self.weight - self.learn_rate * self.gradient_weight
            __snake_case   :     Optional[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   _lowercase			:


      def __init__(     self					:						Optional[Any]    ):





            """simple docstring"""







            __snake_case   :     List[Any]	      =[]
            __snake_case   :     Any	      =[]
            __snake_case   :     Optional[int]	      =plt.figure()
            __snake_case   :     Optional[Any]	      =self.fig_loss.add_subplot(1				, 1				, 1    )


      def _UpperCamelCase    (     self					:						List[str]				, a					:						List[str]    ):





            """simple docstring"""







            self.layers.append(a    )


      def _UpperCamelCase    (     self					:						Optional[Any]    ):





            """simple docstring"""







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


      def _UpperCamelCase    (     self					:						Optional[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					:						str				, a					:						Tuple				, a					:						Tuple				, a					:						Any				, a					:						str    ):





            """simple docstring"""







            __snake_case   :     str	      =train_round
            __snake_case   :     str	      =accuracy

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

            __snake_case   :     List[Any]	      =np.shape(a    )
            for _ in range(a    ):
                  __snake_case   :     Tuple	      =0
                  for row in range(x_shape[0]    ):
                        __snake_case   :     int	      =np.asmatrix(xdata[row, :]    ).T
                        __snake_case   :     Any	      =np.asmatrix(ydata[row, :]    ).T

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

                        __snake_case      ,					__snake_case   :     Tuple	      =self.cal_loss(_ydata				, _xdata    )
                        __snake_case   :     Union[str, Any]	      =all_loss + loss

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

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

                  self.plot_loss()

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


      def _UpperCamelCase    (     self					:						List[str]				, a					:						Optional[Any]				, a					:						Dict    ):





            """simple docstring"""







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







      def _UpperCamelCase    (     self					:						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      __lowercase       (     )       ->       Optional[int]:
      __snake_case   :     Dict	      =np.random.randn(10							,		10			)
      __snake_case   :     Dict	      =np.asarray(
          [
              [0.8, 0.4],
              [0.4, 0.3],
              [0.3_4, 0.4_5],
              [0.6_7, 0.3_2],
              [0.8_8, 0.6_7],
              [0.7_8, 0.7_7],
              [0.5_5, 0.6_6],
              [0.5_5, 0.4_3],
              [0.5_4, 0.1],
              [0.1, 0.5],
          ]			)
      __snake_case   :     Optional[Any]	      =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=100							,		accuracy=0.0_1			)


if __name__ == "__main__":
    example()










"""simple docstring"""





import numpy as np



def      __lowercase       (     a			:					Union[str, Any]							,		a			:					Optional[Any]							,		a			:					List[str]			)       ->       Dict:
      __snake_case   :     Dict	      =n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      __snake_case   :     int	      =np.floor(a			)
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      __snake_case   :     Dict	      =int(n_draws - floored.sum()			)
      if need_to_add > 0:
            __snake_case   :     List[str]	      =continuous - floored
            __snake_case   :     str	      =np.sort(np.unique(a			)			)[::-1]
            # add according to remainder, but break ties
            # randomly to avoid biases
            for value in values:
                  ((__snake_case)      ,					)   :     Tuple	      =np.where(remainder == value			)
                  # if we need_to_add less than what's in inds
                  # we draw randomly from them.
                  # if we need to add more, we add them all and
                  # go to the next value
                  __snake_case   :     int	      =min(len(a			)							,		a			)
                  __snake_case   :     Any	      =rng.choice(a							,		size=a							,		replace=a			)
                  floored[inds] += 1
                  need_to_add -= add_now
                  if need_to_add == 0:
                        break
      return floored.astype(np.intaa			)



def      __lowercase       (     a			:					Any							,		a			:					str							,		a			:					Dict							,		a			:					Union[str, Any]							,		a			:					Union[str, Any]=10			)       ->       Any:
      __snake_case      ,					__snake_case   :     int	      =np.unique(a							,		return_inverse=a			)
      __snake_case   :     Dict	      =classes.shape[0]
      __snake_case   :     int	      =np.bincount(a			)
      if np.min(a			) < 2:
            raise ValueError('''Minimum class count error'''			)
      if n_train < n_classes:
            raise ValueError(
                '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)			)
      if n_test < n_classes:
            raise ValueError(
                '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)			)
      __snake_case   :     str	      =np.split(np.argsort(a							,		kind='''mergesort'''			)							,		np.cumsum(a			)[:-1]			)
      for _ in range(a			):
            __snake_case   :     str	      =approximate_mode(a							,		a							,		a			)
            __snake_case   :     List[str]	      =class_counts - n_i
            __snake_case   :     Union[str, Any]	      =approximate_mode(a							,		a							,		a			)

            __snake_case   :     int	      =[]
            __snake_case   :     Union[str, Any]	      =[]

            for i in range(a			):
                  __snake_case   :     Union[str, Any]	      =rng.permutation(class_counts[i]			)
                  __snake_case   :     Dict	      =class_indices[i].take(a							,		mode='''clip'''			)
                  train.extend(perm_indices_class_i[: n_i[i]]			)
                  test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
            __snake_case   :     Any	      =rng.permutation(a			)
            __snake_case   :     int	      =rng.permutation(a			)

            yield train, test


