pyrepo_mcda.mcda_methods.vmcm

Classes

VMCM

Module Contents

class pyrepo_mcda.mcda_methods.vmcm.VMCM

Bases: pyrepo_mcda.mcda_methods.mcda_method.MCDA_method

_elimination(matrix)

Calculate significance coefficient values for each criterion. Criteria with significance coefficient values between 0 and 0.1 are recommended to be eliminated from the considered criteria set.

Parameters

matrixndarray
Decision matrix with m alternatives in rows and n criteria in columns.

Examples

>>> vmcm = VMCM()
>>> vmcm._elimination(matrix)

_weighting(matrix)

Calculate criteria weights

Parameters

matrixndarray
Decision matrix with m alternatives in rows and n criteria in columns.

Returns

ndarray
Vector with criteria weights

Examples

>>> vmcm = VMCM()
>>> weights = vmcm._weighting(matrix)

_normalization(matrix)

Calculates normalized matrix

Parameters

matrixndarray
Decision matrix with m alternatives in rows and n criteria in columns.

Returns

ndarray
Normalized matrix

Examples

>>> vmcm = VMCM()
>>> norm_matrix = vmcm._normalization(matrix)

_pattern_determination(matrix, types)

Automatic determination of pattern and anti-pattern

Parameters

matrixndarray
Decision matrix with m alternatives in rows and n criteria in columns.

Returns

ndarray, ndarray
Two vectors including values respectively of pattern and anti-pattern

Examples

>>> vmcm = VMCM()
>>> pattern, antipattern = vmcm._pattern_determination(matrix, types)

_classification(m)

Assign evaluated objects to classes

Parameters

mndarray
Vector with values of synthetic measure

Returns

ndarray
Vector including classes assigned to evaluated objects

Examples

>>> vmcm = VMCM()
>>> pref = vmcm(matrix, weights, types)
>>> classes = vmcm._classification(pref)

__call__(matrix, weights, types, pattern, anti_pattern)

Score alternatives provided in decision matrix matrix with m alternatives in rows and n criteria in columns using criteria weights and criteria types.

Parameters

matrixndarray
Decision matrix with m alternatives in rows and n criteria in columns.

weights: ndarray
Vector with criteria weights. Sum of weights must be equal to 1.

typesndarray
Vector with criteria types. Profit criteria are represented by 1 and cost by -1.

patternndarray
Vector with values of pattern

anti_patternndarray
Vector with values of anti-pattern

Returns

ndrarray
Vector with preference values of each alternative. The best alternative has the highest preference value.

Examples

>>> vmcm = VMCM()
>>> pattern, antipattern = vmcm._pattern_determination(matrix, types)
>>> pref = vmcm(matrix, weights, types, pattern, antipattern)
>>> rank = rank_preferences(pref, reverse = True)

static _vmcm(self, matrix, weights, types, pattern, anti_pattern)