

import numpy as np
from matplotlib import pyplot as plt
def 		UpperCamelCase_(    snake_case__:						List[str]     ) ->				Any:
 return 1 / (1 + np.exp(-1 * x     ))



class 					lowercase		:



 '''simple docstring'''


 def __init__(self , __a , __a=None , __a=None , __a=False				)				->      str:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self , __a				)				->      List[str]:






  """simple docstring"""





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




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






  """simple docstring"""





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




 def 						UpperCamelCase__							(self , __a				)				->      List[Any]:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self , __a				)				->      int:






  """simple docstring"""





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

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

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



 '''simple docstring'''


 def __init__(self				)				->      List[Any]:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self , __a				)				->      str:






  """simple docstring"""





  self.layers.append(lowercase_				)




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """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 , __a , __a , __a , __a				)				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       train_round
  UpperCAmelCase__           =       accuracy

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

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

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

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

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

   UpperCAmelCase__           =       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 						UpperCamelCase__							(self , __a , __a				)				->      Tuple:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self				)				->      Optional[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 		UpperCamelCase_(    ) ->				int:
 UpperCAmelCase__           =       np.random.randn(10						,					10     )
 UpperCAmelCase__           =       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],
     ]     )
 UpperCAmelCase__           =       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=1_00						,					accuracy=0.0_1     )


if __name__ == "__main__":
 example()






from __future__ import annotations



class 					lowercase		:



 '''simple docstring'''


 def __init__(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       vertices
  UpperCAmelCase__           =       [[0] * vertices for _ in range(__a				)]




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  print('Vertex \t Distance from Source'				)
  for vertex in range(self.vertices				):
   print(__a , '\t\t' , distances_from_source[vertex]				)




 def 						UpperCamelCase__							(self , __a , __a				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       1E7
  UpperCAmelCase__           =       0

  # Search not nearest vertex not in the shortest path tree
  for vertex in range(self.vertices				):
   if distances_from_source[vertex] < minimum and visited[vertex] is False:
    UpperCAmelCase__           =       distances_from_source[vertex]
    UpperCAmelCase__           =       vertex
  return min_index




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       [int(1E7				)] * self.vertices  # distances from the source
  UpperCAmelCase__           =       0
  UpperCAmelCase__           =       [False] * self.vertices

  for _ in range(self.vertices				):
   UpperCAmelCase__           =       self.minimum_distance(__a , __a				)
   UpperCAmelCase__           =       True

   # Update dist value of the adjacent vertices
   # of the picked vertex only if the current
   # distance is greater than new distance and
   # the vertex in not in the shortest path tree
   for v in range(self.vertices				):
    if (
        self.graph[u][v] > 0
        and visited[v] is False
        and distances[v] > distances[u] + self.graph[u][v]
    ):
     UpperCAmelCase__           =       distances[u] + self.graph[u][v]

  self.print_solution(__a				)


if __name__ == "__main__":
 _UpperCamelCase     =    Graph(9)
 _UpperCamelCase     =    [
     [0, 4, 0, 0, 0, 0, 0, 8, 0],
     [4, 0, 8, 0, 0, 0, 0, 11, 0],
     [0, 8, 0, 7, 0, 4, 0, 0, 2],
     [0, 0, 7, 0, 9, 14, 0, 0, 0],
     [0, 0, 0, 9, 0, 10, 0, 0, 0],
     [0, 0, 4, 14, 10, 0, 2, 0, 0],
     [0, 0, 0, 0, 0, 2, 0, 1, 6],
     [8, 11, 0, 0, 0, 0, 1, 0, 7],
     [0, 0, 2, 0, 0, 0, 6, 7, 0],
 ]
 graph.dijkstra(0)



