





from typing import Any

import numpy as np

class    __A  :


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

								lowerCamelCase									=tableau
								lowerCamelCase			,	lowerCamelCase									=tableau.shape

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

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

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

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

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

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

								lowerCamelCase									=[""""""]

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

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


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

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

								# decision | slack | artificial
								lowerCamelCase									=["""x""", """s""", """a"""]
								lowerCamelCase									=[]
								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 _snake_case							(		self ,			UpperCAmelCase_				):
								lowerCamelCase									=self.objectives[-1]

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

								# RHS
								lowerCamelCase									=tableau[s, -1]

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

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

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


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								lowerCamelCase									=tableau[row_idx].copy()

								lowerCamelCase									=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
								lowerCamelCase									=piv_row
								return tableau


				def _snake_case							(		self ,			UpperCAmelCase_				):
								self.objectives.pop()

								if not self.objectives:
												return tableau

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

								# Delete the artificial variable columns
								lowerCamelCase									=np.delete(UpperCAmelCase_ ,			UpperCAmelCase_ ,			axis=1				)

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

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


				def _snake_case							(		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:
																lowerCamelCase									=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				)

												lowerCamelCase			,	lowerCamelCase									=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
																lowerCamelCase									=self.change_stage(self.tableau				)
												else:
																lowerCamelCase									=self.pivot(self.tableau ,			UpperCAmelCase_ ,			UpperCAmelCase_				)
								return {}


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								lowerCamelCase									={"""P""": abs(tableau[0, -1]				)}

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

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()












from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase__    :      Optional[Any]       					='''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase__    :      Optional[int]       					='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase__    :      List[str]       					='''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION     )
class    __A  (    datasets.Metric     ):


				def _snake_case							(		self				):
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,			citation=_CITATION ,			inputs_description=_KWARGS_DESCRIPTION ,			features=datasets.Features(
								        {
								            """predictions""": datasets.Sequence(datasets.Value("""int32"""				)				),
								            """references""": datasets.Sequence(datasets.Value("""int32"""				)				),
								        }
								        if self.config_name == """multilabel"""
								        else {
								            """predictions""": datasets.Value("""int32"""				),
								            """references""": datasets.Value("""int32"""				),
								        }				) ,			reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"""] ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None				):
								return {
								    "accuracy": float(
								        accuracy_score(UpperCAmelCase_ ,			UpperCAmelCase_ ,			normalize=UpperCAmelCase_ ,			sample_weight=UpperCAmelCase_				)				)
								}





