objective_weighting.normalizations

Module Contents

Functions

linear_normalization(matrix, types)	Normalize decision matrix using linear normalization method.
minmax_normalization(matrix, types)	Normalize decision matrix using minimum-maximum normalization method.
max_normalization(matrix, types)	Normalize decision matrix using maximum normalization method.
sum_normalization(matrix, types)	Normalize decision matrix using sum normalization method.
vector_normalization(matrix, types)	Normalize decision matrix using vector normalization method.

objective_weighting.normalizations.linear_normalization(matrix, types)

Normalize decision matrix using linear normalization method.

Parameters

• matrix (ndarray) – Decision matrix with m alternatives in rows and n criteria in columns

• types (ndarray) – Criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

Normalized decision matrix

Return type

ndarray

Examples

>>> nmatrix = linear_normalization(matrix, types)

objective_weighting.normalizations.minmax_normalization(matrix, types)

Normalize decision matrix using minimum-maximum normalization method.

Parameters

• matrix (ndarray) – Decision matrix with m alternatives in rows and n criteria in columns

• types (ndarray) – Criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

Normalized decision matrix

Return type

ndarray

Examples

>>> nmatrix = minmax_normalization(matrix, types)

objective_weighting.normalizations.max_normalization(matrix, types)

Normalize decision matrix using maximum normalization method.

Parameters

• matrix (ndarray) – Decision matrix with m alternatives in rows and n criteria in columns

• types (ndarray) – Criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

Normalized decision matrix

Return type

ndarray

Examples

>>> nmatrix = max_normalization(matrix, types)

objective_weighting.normalizations.sum_normalization(matrix, types)

Normalize decision matrix using sum normalization method.

Parameters

• matrix (ndarray) – Decision matrix with m alternatives in rows and n criteria in columns

• types (ndarray) – Criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

Normalized decision matrix

Return type

ndarray

Examples

>>> nmatrix = sum_normalization(matrix, types)

objective_weighting.normalizations.vector_normalization(matrix, types)

Normalize decision matrix using vector normalization method.

Parameters

• matrix (ndarray) – Decision matrix with m alternatives in rows and n criteria in columns

• types (ndarray) – Criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

Normalized decision matrix

Return type

ndarray

Examples

>>> nmatrix = vector_normalization(matrix, types)