
'''simple docstring'''





import numpy as np
from matplotlib import pyplot as plt





def 							snake_case				(		UpperCAmelCase						)->      Dict:







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


class 			UpperCamelCase__	:






 def __init__(      self	:Tuple						,					_A	:Optional[Any]						,					_A	:str=None						,					_A	:Tuple=None						,					_A	:List[Any]=False      )					->      Optional[Any]:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Dict						,					_A	:Any      )					->      Tuple:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:List[Any]      )					->      Tuple:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:int						,					_A	:Union[str, Any]      )					->      str:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Tuple						,					_A	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



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

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

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







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:Tuple      )					->      Union[str, Any]:







       '''simple docstring'''



       self.layers.append(_A      )






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







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Tuple      )					->      List[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 						lowercase_    (      self	:Tuple						,					_A	:List[str]						,					_A	:Optional[int]						,					_A	:Union[str, Any]						,					_A	:str      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      train_round
       __A													=      accuracy

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

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

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

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

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

             __A													=      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 						lowercase_    (      self	:Dict						,					_A	:Tuple						,					_A	:str      )					->      Tuple:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Tuple      )					->      Dict:







       '''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 							snake_case				(		)->      List[str]:







      """simple docstring"""
      __A													=      np.random.randn(1_0   ,	1_0						)
      __A													=      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],
          ]						)
      __A													=      BPNN()
      for i in (1_0, 2_0, 3_0, 2):
            model.add_layer(DenseLayer(UpperCAmelCase						)						)
      model.build()
      model.summary()
      model.train(xdata=UpperCAmelCase   ,	ydata=UpperCAmelCase   ,	train_round=1_0_0   ,	accuracy=0.01						)


if __name__ == "__main__":
 example()











from typing import Any

import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_		):
											# Check if RHS is negative
											if np.any(tableau[:, -1]	,	where=tableau[:, -1] < 0		):
															raise ValueError("""RHS must be > 0"""		)

											a__					   =		tableau
											a__   ,      a__					   =		tableau.shape

											# Number of decision variables x1, x2, x3...
											a__					   =		n_vars

											# Number of artificial variables to be minimised
											a__					   =		len(np.where(tableau[self.n_vars : -1] == -1		)[0]		)

											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
											a__					   =		(self.n_art_vars > 0) + 1

											# Number of slack variables added to make inequalities into equalities
											a__					   =		self.n_rows - self.n_stages

											# Objectives for each stage
											a__					   =		["""max"""]

											# In two stage simplex, first minimise then maximise
											if self.n_art_vars:
															self.objectives.append("""min"""		)

											a__					   =		[""""""]

											# Index of current pivot row and column
											a__					   =		None
											a__					   =		None

											# Does objective row only contain (non)-negative values?
											a__					   =		False


							@staticmethod
							def _a   (				*a_		):
											if len(a_		) != 3:
															raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""		)

											if not all(x >= 0 and isinstance(a_	,	a_		) for x in args		):
															raise ValueError("""All arguments must be non-negative integers"""		)

											# decision | slack | artificial
											a__					   =		["""x""", """s""", """a"""]
											a__					   =		[]
											for i in range(3		):
															for j in range(args[i]		):
																			titles.append(string_starts[i] + str(j + 1		)		)
											titles.append("""RHS"""		)
											return titles


							def _a   (				self	,	a_		):
											a__					   =		self.objectives[-1]

											# Find entries of highest magnitude in objective rows
											a__					   =		(objective == """min""") - (objective == """max""")
											a__					   =		np.argmax(sign * tableau[0, : self.n_vars]		)

											# Choice is only valid if below 0 for maximise, and above for minimise
											if sign * self.tableau[0, col_idx] <= 0:
															a__					   =		True
															return 0, 0

											# Pivot row is chosen as having the lowest quotient when elements of
											# the pivot column divide the right-hand side

											# Slice excluding the objective rows
											a__					   =		slice(self.n_stages	,	self.n_rows		)

											# RHS
											a__					   =		tableau[s, -1]

											# Elements of pivot column within slice
											a__					   =		tableau[s, col_idx]

											# Array filled with nans
											a__					   =		np.full(self.n_rows - self.n_stages	,	np.nan		)

											# If element in pivot column is greater than zeron_stages, return
											# quotient or nan otherwise
											a__					   =		np.divide(a_	,	a_	,	out=a_	,	where=divisor > 0		)

											# Arg of minimum quotient excluding the nan values. n_stages is added
											# to compensate for earlier exclusion of objective columns
											a__					   =		np.nanargmin(a_		) + self.n_stages
											return row_idx, col_idx


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		tableau[row_idx].copy()

											a__					   =		piv_row[col_idx]

											# Entry becomes 1
											piv_row *= 1 / piv_val

											# Variable in pivot column becomes basic, ie the only non-zero entry
											for idx, coeff in enumerate(tableau[:, col_idx]		):
															tableau[idx] += -coeff * piv_row
											a__					   =		piv_row
											return tableau


							def _a   (				self	,	a_		):
											self.objectives.pop()

											if not self.objectives:
															return tableau

											# Slice containing ids for artificial columns
											a__					   =		slice(-self.n_art_vars - 1	,	-1		)

											# Delete the artificial variable columns
											a__					   =		np.delete(a_	,	a_	,	axis=1		)

											# Delete the objective row of the first stage
											a__					   =		np.delete(a_	,	0	,	axis=0		)

											a__					   =		1
											self.n_rows -= 1
											a__					   =		0
											a__					   =		False
											return tableau


							def _a   (				self		):
											# Stop simplex algorithm from cycling.
											for _ in range(100		):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																			a__					   =		self.generate_col_titles(
																			    self.n_vars	,	self.n_slack	,	self.n_art_vars		)

																			# Find the values of each variable at optimal solution
																			return self.interpret_tableau(self.tableau	,	self.col_titles		)

															a__   ,      a__					   =		self.find_pivot(self.tableau		)

															# If there are no more negative values in objective row
															if self.stop_iter:
																			# Delete artificial variable columns and rows. Update attributes
																			a__					   =		self.change_stage(self.tableau		)
															else:
																			a__					   =		self.pivot(self.tableau	,	a_	,	a_		)
											return {}







							def _a   (				self	,	a_	,	a_		):
											a__					   =		{"""P""": abs(tableau[0, -1]		)}

											for i in range(self.n_vars		):
															# Gives ids of nonzero entries in the ith column
															a__					   =		np.nonzero(tableau[:, i]		)
															a__					   =		len(nonzero[0]		)

															# First entry in the nonzero ids
															a__					   =		nonzero[0][0]
															a__					   =		tableau[nonzero_rowidx, i]

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																			a__					   =		tableau[nonzero_rowidx, -1]
																			a__					   =		rhs_val

        # Check for basic variables
											for title in col_titles:
															# Don't add RHS or slack variables to output dict
															if title[0] not in "R-s-a":
																			output_dict.setdefault(a_	,	0		)
											return output_dict


if __name__ == "__main__":
	import doctest

	doctest.testmod()

