

'''simple docstring'''






import numpy as np
from matplotlib import pyplot as plt


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[int]   )							->       List[Any]:
      """simple docstring"""

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




    def __init__( self    ,					lowercase__    ,					lowercase__=None    ,					lowercase__=None    ,					lowercase__=False      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	units
          SCREAMING_SNAKE_CASE_				:      Dict					=	None
          SCREAMING_SNAKE_CASE_				:      int					=	None
          SCREAMING_SNAKE_CASE_				:      Dict					=	activation
          if learning_rate is None:
                SCREAMING_SNAKE_CASE_				:      List[str]					=	0.3
          SCREAMING_SNAKE_CASE_				:      Tuple					=	learning_rate
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	is_input_layer




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	np.asmatrix(np.random.normal(0    ,					0.5    ,					(self.units, back_units)      )      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	np.asmatrix(np.random.normal(0    ,					0.5    ,					self.units      )      ).T
          if self.activation is None:
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	sigmoid




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          if self.activation == sigmoid:
                SCREAMING_SNAKE_CASE_				:      Tuple					=	np.dot(self.output    ,					(1 - self.output).T      )
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	np.diag(np.diag(lowercase__      )      )
          else:
                SCREAMING_SNAKE_CASE_				:      Dict					=	1
          return gradient_activation




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






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







    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	self.cal_gradient()  # i * i 维
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	np.asmatrix(np.dot(gradient.T    ,					lowercase__      )      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	np.asmatrix(self.xdata      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	-1
          SCREAMING_SNAKE_CASE_				:      Dict					=	self.weight

          SCREAMING_SNAKE_CASE_				:      int					=	np.dot(gradient.T    ,					self._gradient_weight.T      )
          SCREAMING_SNAKE_CASE_				:      str					=	gradient * self._gradient_bias
          SCREAMING_SNAKE_CASE_				:      Tuple					=	np.dot(lowercase__    ,					self._gradient_x      ).T
          # upgrade: the Negative gradient direction
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.weight - self.learn_rate * self.gradient_weight
          SCREAMING_SNAKE_CASE_				:      List[str]					=	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      SCREAMING_SNAKE_CASE__      :




    def __init__( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          self.layers.append(lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """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   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	train_round
          SCREAMING_SNAKE_CASE_				:      List[str]					=	accuracy

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

          SCREAMING_SNAKE_CASE_				:      Dict					=	np.shape(lowercase__      )
          for _ in range(lowercase__      ):
                SCREAMING_SNAKE_CASE_				:      Dict					=	0
                for row in range(x_shape[0]      ):
                      SCREAMING_SNAKE_CASE_				:      Dict					=	np.asmatrix(xdata[row, :]      ).T
                      SCREAMING_SNAKE_CASE_				:      List[Any]					=	np.asmatrix(ydata[row, :]      ).T

                      # forward propagation
                      for layer in self.layers:
                            SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	layer.forward_propagation(_xdata      )

                      SCREAMING_SNAKE_CASE_				:      Dict					=	self.cal_loss(_ydata    ,					_xdata      )
                      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	all_loss + loss

                      # back propagation: the input_layer does not upgrade
                      for layer in self.layers[:0:-1]:
                            SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	layer.back_propagation(lowercase__      )

                SCREAMING_SNAKE_CASE_				:      Tuple					=	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   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






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







    def   __lowerCamelCase				( self      ):






          """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  __lowerCamelCase      (				)							->       Optional[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	np.random.randn(1_0  ,  1_0   )
      SCREAMING_SNAKE_CASE_				:      Any					=	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],
          ]   )
      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	BPNN()
      for i in (1_0, 2_0, 3_0, 2):
            model.add_layer(DenseLayer(SCREAMING_SNAKE_CASE_   )   )
      model.build()
      model.summary()
      model.train(xdata=SCREAMING_SNAKE_CASE_  ,  ydata=SCREAMING_SNAKE_CASE_  ,  train_round=1_0_0  ,  accuracy=0.01   )


if __name__ == "__main__":
 example()




'''simple docstring'''






import heapq


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	[(0, start)]  # cost from start node,end node
      SCREAMING_SNAKE_CASE_				:      str					=	set()
      while heap:
            ((SCREAMING_SNAKE_CASE_),						(SCREAMING_SNAKE_CASE_))				:      Optional[int]					=	heapq.heappop(SCREAMING_SNAKE_CASE_   )
            if u in visited:
                  continue
            visited.add(SCREAMING_SNAKE_CASE_   )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  SCREAMING_SNAKE_CASE_				:      int					=	cost + c
                  heapq.heappush(SCREAMING_SNAKE_CASE_  ,  (next_item, v)   )
      return -1


snake_case_					  =	{
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

snake_case_					  =	dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()

