





import numpy as np
from matplotlib import pyplot as plt


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		int):



  '''simple docstring'''






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

class   __snake_case				:






    '''simple docstring'''





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

      """simple docstring"""





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





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		Optional[Any]							):

      """simple docstring"""





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





    def 	A		(   self						:		str							):

      """simple docstring"""





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





    def 	A		(   self						:		Tuple			,						snake_case						:		int							):

      """simple docstring"""





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





    def 	A		(   self						:		Any			,						snake_case						:		int							):

      """simple docstring"""





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

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

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






    '''simple docstring'''





    def __init__(   self						:		int							):

      """simple docstring"""





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





    def 	A		(   self						:		Union[str, Any]			,						snake_case						:		Dict							):

      """simple docstring"""





      self.layers.append(_A							)





    def 	A		(   self						:		Dict							):

      """simple docstring"""





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





    def 	A		(   self						:		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 	A		(   self						:		Optional[int]			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		List[Any]			,						snake_case						:		Optional[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				train_round
      __lowerCAmelCase			=				accuracy

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

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

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

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

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

        __lowerCAmelCase			=				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 	A		(   self						:		Union[str, Any]			,						snake_case						:		Tuple			,						snake_case						:		Optional[Any]							):

      """simple docstring"""





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





    def 	A		(   self						:		List[str]							):

      """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 					__SCREAMING_SNAKE_CASE  (							):



  '''simple docstring'''






  __lowerCAmelCase			=				np.random.randn(10     ,							10)
  __lowerCAmelCase			=				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],
      ])
  __lowerCAmelCase			=				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()







class   __snake_case				:






    '''simple docstring'''





    def __init__(   self						:		Any							):

      """simple docstring"""





      __lowerCAmelCase			=				{}
      # indexmap keys are an index and it's values are the number at that index
      __lowerCAmelCase			=				{}





    def 	A		(   self						:		List[Any]			,						snake_case						:		list | str | range			,						snake_case						:		int							):

      """simple docstring"""





      if isinstance(snake_case			,						(range, str)							):
        __lowerCAmelCase			=				list(snake_case							)
      elif not isinstance(snake_case			,						snake_case							):
        raise TypeError(
            '''binary_search_delete() only accepts either a list, range or str'''							)

      __lowerCAmelCase			=				0
      __lowerCAmelCase			=				len(snake_case							) - 1

      while low <= high:
        __lowerCAmelCase			=				(low + high) // 2
        if array[mid] == item:
          array.pop(snake_case							)
          return array
        elif array[mid] < item:
          __lowerCAmelCase			=				mid + 1
        else:
          __lowerCAmelCase			=				mid - 1
      raise ValueError(
          '''Either the item is not in the array or the array was unsorted'''							)





    def 	A		(   self						:		Tuple			,						snake_case						:		list | str | range			,						snake_case						:		int							):

      """simple docstring"""





      if isinstance(snake_case			,						(range, str)							):
        __lowerCAmelCase			=				list(snake_case							)
      elif not isinstance(snake_case			,						snake_case							):
        raise TypeError(
            '''binary_search_insert() only accepts either a list, range or str'''							)

      __lowerCAmelCase			=				0
      __lowerCAmelCase			=				len(snake_case							) - 1

      while low <= high:
        __lowerCAmelCase			=				(low + high) // 2
        if array[mid] == index:
          # If the item already exists in the array,
          # insert it after the existing item
          array.insert(mid + 1			,						snake_case							)
          return array
        elif array[mid] < index:
          __lowerCAmelCase			=				mid + 1
        else:
          __lowerCAmelCase			=				mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
      array.insert(snake_case			,						snake_case							)
      return array





    def 	A		(   self						:		Tuple			,						snake_case						:		int			,						snake_case						:		int							):

      """simple docstring"""





      if index in self.indexmap:
        __lowerCAmelCase			=				self.indexmap[index]
        if len(self.numbermap[n]							) == 1:
          del self.numbermap[n]
        else:
          __lowerCAmelCase			=				self.binary_search_delete(self.numbermap[n]			,						snake_case							)

        # Set new index
      __lowerCAmelCase			=				number

      # Number not seen before or empty so insert number value
      if number not in self.numbermap:
        __lowerCAmelCase			=				[index]

      # Here we need to perform a binary search insertion in order to insert
      # The item in the correct place
      else:
        __lowerCAmelCase			=				self.binary_search_insert(
            self.numbermap[number]			,						snake_case							)





    def 	A		(   self						:		List[str]			,						snake_case						:		int							):

      """simple docstring"""





      return self.numbermap.get(snake_case			,						[-1]							)[0]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
