




import numpy as np
from matplotlib import pyplot as plt


def 		_lowerCamelCase				(					UpperCamelCase							)			->		List[str]:





  """simple docstring"""





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

class      UpperCAmelCase						:






   def __init__(	self   :       List[Any]						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       List[Any]=None						,					UpperCamelCase_   :       Optional[int]=None						,					UpperCamelCase_   :       Any=False     )->			Optional[int]:
     _lowercase: Optional[int]						     =    units
     _lowercase: str						     =    None
     _lowercase: str						     =    None
     _lowercase: Tuple						     =    activation
     if learning_rate is None:
       _lowercase: List[str]						     =    0.3
     _lowercase: List[Any]						     =    learning_rate
     _lowercase: List[str]						     =    is_input_layer






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       int     )->			List[Any]:
     _lowercase: Any						     =    np.asmatrix(np.random.normal(0						,					0.5						,					(self.units, back_units)     )     )
     _lowercase: int						     =    np.asmatrix(np.random.normal(0						,					0.5						,					self.units     )     ).T
     if self.activation is None:
       _lowercase: Any						     =    sigmoid






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






   def  _a						(	self   :       int						,					UpperCamelCase_   :       int     )->			Optional[Any]:
     _lowercase: Dict						     =    xdata
     if self.is_input_layer:
       # input layer
       _lowercase: Dict						     =    xdata
       _lowercase: Union[str, Any]						     =    xdata
       return xdata
     else:
       _lowercase: Any						     =    np.dot(self.weight						,					self.xdata     ) - self.bias
       _lowercase: Union[str, Any]						     =    self.activation(self.wx_plus_b     )
       return self.output






   def  _a						(	self   :       int						,					UpperCamelCase_   :       Union[str, Any]     )->			Optional[Any]:
     _lowercase: Dict						     =    self.cal_gradient()  # i * i 维
     _lowercase: List[Any]						     =    np.asmatrix(np.dot(gradient.T						,					UpperCamelCase_     )     )

     _lowercase: Any						     =    np.asmatrix(self.xdata     )
     _lowercase: Tuple						     =    -1
     _lowercase: int						     =    self.weight

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






   def __init__(	self   :       List[Any]     )->			Dict:
     _lowercase: Union[str, Any]						     =    []
     _lowercase: Optional[Any]						     =    []
     _lowercase: Tuple						     =    plt.figure()
     _lowercase: int						     =    self.fig_loss.add_subplot(1						,					1						,					1     )






   def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			List[Any]:
     self.layers.append(UpperCamelCase_     )






   def  _a						(	self   :       Union[str, Any]     )->			str:
     for i, layer in enumerate(self.layers[:]     ):
       if i < 1:
         _lowercase: Optional[Any]						     =    True
       else:
         layer.initializer(self.layers[i - 1].units     )






   def  _a						(	self   :       Optional[Any]     )->			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  _a						(	self   :       Union[str, Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       List[Any]						,					UpperCamelCase_   :       Union[str, Any]     )->			List[Any]:
     _lowercase: Any						     =    train_round
     _lowercase: Optional[int]						     =    accuracy

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

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

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

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

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

       _lowercase: Tuple						     =    all_loss / x_shape[0]
       self.train_mse.append(UpperCamelCase_     )

       self.plot_loss()

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






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






   def  _a						(	self   :       Optional[int]     )->			Optional[int]:
     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				(					)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: Dict						     =    np.random.randn(10					,     10							)
  _lowercase: List[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],
      ]							)
  _lowercase: int						     =    BPNN()
  for i in (10, 20, 30, 2):
    model.add_layer(DenseLayer(UpperCamelCase							)							)
  model.build()
  model.summary()
  model.train(xdata=UpperCamelCase					,     ydata=UpperCamelCase					,     train_round=100					,     accuracy=0.01							)


if __name__ == "__main__":
   example()






from __future__ import annotations

from pathlib import Path


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		int:





  """simple docstring"""





  return pointa[0] * pointa[1] - pointa[1] * pointa[0]


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		bool:





  """simple docstring"""





  _lowercase: tuple[int, int]						     =    (xa, ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: float						     =    -vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)
  _lowercase: float						     =    +vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)

  return a > 0 and b > 0 and a + b < 1


def 		_lowerCamelCase				(					UpperCamelCase = "p102_triangles.txt"							)			->		int:





  """simple docstring"""





  _lowercase: str						     =    Path(UpperCamelCase							).parent.joinpath(UpperCamelCase							).read_text(encoding='utf-8'							)

  _lowercase: list[list[int]]						     =    []
  for line in data.strip().split('\n'							):
    triangles.append([int(UpperCamelCase							) for number in line.split(','							)]							)

  _lowercase: int						     =    0
  _lowercase: list[int]

  for triangle in triangles:
    ret += contains_origin(*UpperCamelCase							)

  return ret


if __name__ == "__main__":
   print(f'''{solution() = }''')
