pymcdm.normalizations

pymcdm.normalizations.enhanced_accuracy_normalization(x, cost=False)

Calculate the normalized vector using the enhanced accuracy method.

Parameters:

• x (ndarray) – One-dimensional numpy array of values to be normalized

• cost (bool, optional) – Vector type. Default profit type.

Returns:

One-dimensional numpy array of normalized values.

Return type:

ndarray

pymcdm.normalizations.linear_normalization(x, cost=False)

Calculate the normalized vector using the linear method.

Parameters:

• x (ndarray) – One-dimensional numpy array of values to be normalized

• cost (bool, optional) – Vector type. Default profit type.

Returns:

One-dimensional numpy array of normalized values.

Return type:

ndarray

pymcdm.normalizations.logarithmic_normalization(x, cost=False)

Calculate the normalized vector using the logarithmic method.

Parameters:

• x (ndarray) – One-dimensional numpy array of values to be normalized

• cost (bool, optional) – Vector type. Default profit type.

Returns:

One-dimensional numpy array of normalized values.

Return type:

ndarray

pymcdm.normalizations.max_normalization(x, cost=False)

Calculate the normalized vector using the max method.

Parameters:

• x (ndarray) – One-dimensional numpy array of values to be normalized

• cost (bool, optional) – Vector type. Default profit type.

Returns:

One-dimensional numpy array of normalized values.

Return type:

ndarray

pymcdm.normalizations.minmax_normalization(x, cost=False)

Calculate the normalized vector using the min-max method.

Parameters:

• x (ndarray) – One-dimensional numpy array of values to be normalized

• cost (bool, optional) – Vector type. Default profit type.

Returns:

One-dimensional numpy array of normalized values.

Return type:

ndarray

pymcdm.normalizations.nonlinear_normalization(x, cost=False)

Calculate the normalized vector using the nonlinear method.

Parameters:

• x (ndarray) – One-dimensional numpy array of values to be normalized

• cost (bool, optional) – Vector type. Default profit type.

Returns:

One-dimensional numpy array of normalized values.

Return type:

ndarray

pymcdm.normalizations.sum_normalization(x, cost=False)

Calculate the normalized vector using the sum method.

Parameters:

• x (ndarray) – One-dimensional numpy array of values to be normalized

• cost (bool, optional) – Vector type. Default profit type.

Returns:

One-dimensional numpy array of normalized values.

Return type:

ndarray

pymcdm.normalizations.vector_normalization(x, cost=False)

Calculate the normalized vector using the vector method.

Parameters:

• x (ndarray) – One-dimensional numpy array of values to be normalized

• cost (bool, optional) – Vector type. Default profit type.

Returns:

One-dimensional numpy array of normalized values.

Return type:

ndarray

pymcdm.normalizations.zavadskas_turskis_normalization(x, cost=False)

Calculate the normalized vector using the Zavadskas-Turskis method.

Parameters:

• x (ndarray) – One-dimensional numpy array of values to be normalized

• cost (bool, optional) – Vector type. Default profit type.

Returns:

One-dimensional numpy array of normalized values.

Return type:

ndarray