






"""simple docstring"""
from typing import Any

import numpy as np



class 					UpperCamelCase__   :







    """simple docstring"""







    def __init__(		self   :  int							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Any   ):





           '''simple docstring'''

           if np.any(tableau[:, -1]							,						where=tableau[:, -1] < 0   ):
                  raise ValueError('RHS must be > 0'   )

           __magic_name__										=						tableau
           __magic_name__							,     __magic_name__										=						tableau.shape

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

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

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

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

           # Objectives for each stage
           __magic_name__										=						['max']

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

           __magic_name__										=						['']

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

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







    @staticmethod
    def 							a__ (		*UpperCamelCase_   :  Union[str, Any]   ):





           '''simple docstring'''

           if len(__snake_case   ) != 3:
                  raise ValueError('Must provide n_vars, n_slack, and n_art_vars'   )

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

           # decision | slack | artificial
           __magic_name__										=						['x', 's', 'a']
           __magic_name__										=						[]
           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   :  str							,						UpperCamelCase_   :  Union[str, Any]   ):





           '''simple docstring'''

           __magic_name__										=						self.objectives[-1]

           # Find entries of highest magnitude in objective rows
           __magic_name__										=						(objective == 'min') - (objective == 'max')
           __magic_name__										=						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:
                  __magic_name__										=						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
           __magic_name__										=						slice(self.n_stages							,						self.n_rows   )

           # RHS
           __magic_name__										=						tableau[s, -1]

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

           # Array filled with nans
           __magic_name__										=						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
           __magic_name__										=						np.divide(__snake_case							,						__snake_case							,						out=__snake_case							,						where=divisor > 0   )

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







    def 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  int   ):





           '''simple docstring'''

           __magic_name__										=						tableau[row_idx].copy()

           __magic_name__										=						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
           __magic_name__										=						piv_row
           return tableau







    def 							a__ (		self   :  int							,						UpperCamelCase_   :  List[str]   ):





           '''simple docstring'''

           self.objectives.pop()

           if not self.objectives:
                  return tableau

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

           # Delete the artificial variable columns
           __magic_name__										=						np.delete(__snake_case							,						__snake_case							,						axis=1   )

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

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







    def 							a__ (		self   :  Any   ):





           '''simple docstring'''

           for _ in range(1_0_0   ):
                  # Completion of each stage removes an objective. If both stages
                  # are complete, then no objectives are left
                  if not self.objectives:
                         __magic_name__										=						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   )

                  __magic_name__							,     __magic_name__										=						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
                         __magic_name__										=						self.change_stage(self.tableau   )
                  else:
                         __magic_name__										=						self.pivot(self.tableau							,						__snake_case							,						__snake_case   )
           return {}







    def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[str]   ):





           '''simple docstring'''

           __magic_name__										=						{'P': abs(tableau[0, -1]   )}

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

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

                  # If there is only one nonzero value in column, which is one
                  if n_nonzero == nonzero_val == 1:
                         __magic_name__										=						tableau[nonzero_rowidx, -1]
                         __magic_name__										=						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(__snake_case							,						0   )
           return output_dict


if __name__ == "__main__":
 import doctest

 doctest.testmod()







"""simple docstring"""
import numpy as np


def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:

       """simple docstring"""





       __magic_name__										=						n_draws * class_counts / class_counts.sum()
       # floored means we don't overshoot n_samples, but probably undershoot
       __magic_name__										=						np.floor(__snake_case     )
       # we add samples according to how much "left over" probability
       # they had, until we arrive at n_samples
       __magic_name__										=						int(n_draws - floored.sum()     )
       if need_to_add > 0:
              __magic_name__										=						continuous - floored
              __magic_name__										=						np.sort(np.unique(__snake_case     )     )[::-1]
              # add according to remainder, but break ties
              # randomly to avoid biases
              for value in values:
                     ((__magic_name__)							,     )										=						np.where(remainder == value     )
                     # if we need_to_add less than what's in inds
                     # we draw randomly from them.
                     # if we need to add more, we add them all and
                     # go to the next value
                     __magic_name__										=						min(len(__snake_case     )   ,    __snake_case     )
                     __magic_name__										=						rng.choice(__snake_case   ,    size=__snake_case   ,    replace=__snake_case     )
                     floored[inds] += 1
                     need_to_add -= add_now
                     if need_to_add == 0:
                            break
       return floored.astype(np.intaa     )






def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:

       """simple docstring"""





       __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
       __magic_name__										=						classes.shape[0]
       __magic_name__										=						np.bincount(__snake_case     )
       if np.min(__snake_case     ) < 2:
              raise ValueError('Minimum class count error'     )
       if n_train < n_classes:
              raise ValueError(
                  'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)     )
       if n_test < n_classes:
              raise ValueError(
                  'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)     )
       __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
       for _ in range(__snake_case     ):
              __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
              __magic_name__										=						class_counts - n_i
              __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )

              __magic_name__										=						[]
              __magic_name__										=						[]

              for i in range(__snake_case     ):
                     __magic_name__										=						rng.permutation(class_counts[i]     )
                     __magic_name__										=						class_indices[i].take(__snake_case   ,    mode='clip'     )
                     train.extend(perm_indices_class_i[: n_i[i]]     )
                     test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]     )
              __magic_name__										=						rng.permutation(__snake_case     )
              __magic_name__										=						rng.permutation(__snake_case     )

              yield train, test