pyrepo_mcda.mcda_methods.multimoora

Classes

`MULTIMOORA_RS`
`MULTIMOORA_RP`
`MULTIMOORA_FMF`
`MULTIMOORA`

Module Contents

class pyrepo_mcda.mcda_methods.multimoora.MULTIMOORA_RS

Bases: pyrepo_mcda.mcda_methods.mcda_method.MCDA_method

__call__(matrix, weights, types)

Score alternatives provided in decision matrix matrix using vector with criteria weights weights and vector with criteria types types.

Parameters

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

weights: ndarray
Criteria weights. Sum of weights must be equal to 1.

types: ndarray
Criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

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

Examples

>>> multimoora_rs = MULTIMOORA_RS()
>>> pref = multimoora_rs(matrix, weights, types)
>>> rank = rank_preferences(pref, reverse = True)

static _multimoora_rs(matrix, weights, types)

class pyrepo_mcda.mcda_methods.multimoora.MULTIMOORA_RP

Bases: pyrepo_mcda.mcda_methods.mcda_method.MCDA_method

__call__(matrix, weights, types)

Score alternatives provided in decision matrix matrix using vector with criteria weights weights and vector with criteria types types.

Parameters

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

weights: ndarray
Criteria weights. Sum of weights must be equal to 1.

types: ndarray
Criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

ndrarray
Preference values of each alternative. The best alternative has the lowest preference value.

Examples

>>> multimoora_rp = MULTIMOORA_RP()
>>> pref = multimoora_rp(matrix, weights, types)
>>> rank = rank_preferences(pref, reverse = False)

static _multimoora_rp(matrix, weights, types)

class pyrepo_mcda.mcda_methods.multimoora.MULTIMOORA_FMF

Bases: pyrepo_mcda.mcda_methods.mcda_method.MCDA_method

__call__(matrix, weights, types)

Score alternatives provided in decision matrix matrix using vector with criteria weights weights and vector with criteria types types.

Parameters

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

weights: ndarray
Criteria weights. Sum of weights must be equal to 1.

types: ndarray
Criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

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

Examples

>>> multimoora_fmf = MULTIMOORA_FMF()
>>> pref = multimoora_fmf(matrix, weights, types)
>>> rank = rank_preferences(pref, reverse = True)

static _multimoora_fmf(matrix, weights, types)

class pyrepo_mcda.mcda_methods.multimoora.MULTIMOORA(compromise_rank_method=dominance_directed_graph)

Bases: pyrepo_mcda.mcda_methods.mcda_method.MCDA_method

compromise_rank_method

__call__(matrix, weights, types)

Score alternatives provided in decision matrix matrix using vector with criteria weights weights and vector with criteria types types.

Parameters

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

weights: ndarray
Criteria weights. Sum of weights must be equal to 1.

types: ndarray
Criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

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

Examples

>>> multimoora = MULTIMOORA()
>>> rank = multimoora(matrix, weights, types)

_multimoora(weights, types, compromise_rank_method)