


import numpy as np
from matplotlib import pyplot as plt
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 return 1 / (1 + np.exp(-1 * x						))


class        A_					:




      '''simple docstring'''


      def __init__(				self:       Optional[Any]						,      a:       str						,      a:       int=None						,      a:       List[Any]=None						,      a:       Any=False				):
       __lowerCamelCase		:							Dict							=						units
       __lowerCamelCase		:							Any							=						None
       __lowerCamelCase		:							Tuple							=						None
       __lowerCamelCase		:							str							=						activation
       if learning_rate is None:
        __lowerCamelCase		:							List[Any]							=						0.3
       __lowerCamelCase		:							Union[str, Any]							=						learning_rate
       __lowerCamelCase		:							Optional[int]							=						is_input_layer




      def      _snake_case	(				self:       int						,      a:       Dict				):
       __lowerCamelCase		:							Dict							=						np.asmatrix(np.random.normal(0						,      0.5						,      (self.units, back_units)				)				)
       __lowerCamelCase		:							int							=						np.asmatrix(np.random.normal(0						,      0.5						,      self.units				)				).T
       if self.activation is None:
        __lowerCamelCase		:							List[str]							=						sigmoid




      def      _snake_case	(				self:       Optional[int]				):
       # activation function may be sigmoid or linear
       if self.activation == sigmoid:
        __lowerCamelCase		:							Tuple							=						np.dot(self.output						,      (1 - self.output).T				)
        __lowerCamelCase		:							str							=						np.diag(np.diag(a				)				)
       else:
        __lowerCamelCase		:							List[Any]							=						1
       return gradient_activation




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




      def      _snake_case	(				self:       Tuple						,      a:       List[Any]				):
       __lowerCamelCase		:							int							=						self.cal_gradient()  # i * i 维
       __lowerCamelCase		:							str							=						np.asmatrix(np.dot(gradient.T						,      a				)				)

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

       __lowerCamelCase		:							int							=						np.dot(gradient.T						,      self._gradient_weight.T				)
       __lowerCamelCase		:							List[Any]							=						gradient * self._gradient_bias
       __lowerCamelCase		:							Dict							=						np.dot(a						,      self._gradient_x				).T
       # upgrade: the Negative gradient direction
       __lowerCamelCase		:							Union[str, Any]							=						self.weight - self.learn_rate * self.gradient_weight
       __lowerCamelCase		:							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        A_					:




      '''simple docstring'''


      def __init__(				self:       Optional[int]				):
       __lowerCamelCase		:							Dict							=						[]
       __lowerCamelCase		:							int							=						[]
       __lowerCamelCase		:							int							=						plt.figure()
       __lowerCamelCase		:							Tuple							=						self.fig_loss.add_subplot(1						,      1						,      1				)




      def      _snake_case	(				self:       Optional[int]						,      a:       str				):
       self.layers.append(a				)




      def      _snake_case	(				self:       Tuple				):
       for i, layer in enumerate(self.layers[:]				):
        if i < 1:
         __lowerCamelCase		:							List[Any]							=						True
        else:
         layer.initializer(self.layers[i - 1].units				)




      def      _snake_case	(				self:       Tuple				):
       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[Any]						,      a:       List[Any]						,      a:       Optional[int]						,      a:       Any						,      a:       Union[str, Any]				):
       __lowerCamelCase		:							Dict							=						train_round
       __lowerCamelCase		:							List[str]							=						accuracy

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

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

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

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

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

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




      def      _snake_case	(				self:       Dict				):
       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__     (  ):
 __lowerCamelCase		:							Union[str, Any]							=						np.random.randn(10			,       10						)
 __lowerCamelCase		:							str							=						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		:							Tuple							=						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()





from __future__ import annotations
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase					,     __lowerCamelCase		:							str							=						m - 1, 0
 while i >= 0 and j < n:
  if key == mat[i][j]:
   print(f'Key {key} found at row- {i + 1} column- {j + 1}'						)
   return
  if key < mat[i][j]:
   i -= 1
  else:
   j += 1
 print(f'Key {key} not found'						)
def       UpperCamelCase__     (  ):
 __lowerCamelCase		:							List[Any]							=						[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
 __lowerCamelCase		:							int							=						int(input('Enter the element to be searched:'						)						)
 print(SCREAMING_SNAKE_CASE__						)
 search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE__			,       len(SCREAMING_SNAKE_CASE__						)			,       len(mat[0]						)			,       SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()

