MCDM

Multiple-criteria decision-making

is_normalized_matrix

 is_normalized_matrix (z_matrix:<built-infunctionarray>)

Return a Boolean value to indicate whether the matrix is normalized or not

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is_normalized_vector

 is_normalized_vector (w_vector:list)

Return a Boolean value to indicate whether the vector is normalized or not

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check_scoring_input

 check_scoring_input (z_matrix:<built-infunctionarray>, w_vector:list,
                      is_benefit_z:list, s_method:str)

Raise an exception if any argument is inappropriate for the corresponding scoring method

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check_weighting_input

 check_weighting_input (z_matrix:<built-infunctionarray>, c_method:str,
                        w_method:str)

Raise an exception if any argument is inappropriate for the corresponding weighting method

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check_normalization_input

 check_normalization_input (x_matrix:<built-infunctionarray>,
                            is_benefit_x:list, n_method:str)

Raise an exception if any argument is inappropriate for the corresponding normalization method

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abspearson

 abspearson (z_matrix:<built-infunctionarray>)

Return the absolute value of the Pearson correlation coefficients of the provided matrix.

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dcor

 dcor (z_matrix:<built-infunctionarray>)

Return the distance correlation coefficients of the provided matrix.

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squared_dcov_matrix

 squared_dcov_matrix (z_matrix:<built-infunctionarray>)

Return the matrix of squared distance covariance between the columns of the provided matrix.

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squared_dcor

 squared_dcor (jl_dcov2, j_dvar2, l_dvar2)

Return the squared distance correlation between the corresponding columns.

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squared_dcov

 squared_dcov (j_func, l_func)

Return the squared distance covariance between the corresponding columns.

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lin_func

 lin_func (dmatrix)

Return the result of the linear function for the provided distance matrix.

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dist_matrix

 dist_matrix (z_vector:<built-infunctionarray>)

Return the Euclidean distance matrix of the provided vector.

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pearson

 pearson (z_matrix:<built-infunctionarray>)

Return the Pearson correlation coefficients of the provided matrix.

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correlate

 correlate (z_matrix:<built-infunctionarray>, c_method:str)

Return the selected correlation coefficients of the provided matrix.

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em

 em (z_matrix:<built-infunctionarray>)

Return the weight vector of the provided decision matrix using the Entropy Measure method.

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mw

 mw (z_matrix:<built-infunctionarray>)

Return the weight vector of the provided decision matrix using the Mean Weights method.

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sd

 sd (z_matrix)

Return the weight vector of the provided decision matrix using the Standard Deviation method.

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vic

 vic (z_matrix:<built-infunctionarray>, c_method:str='dCor')

Return the weight vector of the provided decision matrix using the Variability and Interdependencies of Criteria method.

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linear1

 linear1 (x_matrix:<built-infunctionarray>, is_benefit_x:list)

Return the normalized version of the provided matrix using the Linear Normalization (1) method.

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linear2

 linear2 (x_matrix:<built-infunctionarray>, is_benefit_x:list)

Return the normalized version of the provided matrix using the Linear Normalization (2) method.

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linear3

 linear3 (x_matrix:<built-infunctionarray>, is_benefit_x:list)

Return the normalized version of the provided matrix using the Linear Normalization (3) method.

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vector

 vector (x_matrix:<built-infunctionarray>, is_benefit_x:list)

Return the normalized version of the provided matrix using the Vector Normalization method.

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normalize

 normalize (x_matrix:<built-infunctionarray>, is_benefit_x:list,
            n_method:str)

Return the normalized version of the provided matrix using the selected normalization method.

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critic

 critic (z_matrix:<built-infunctionarray>, c_method:str='Pearson')

Return the weight vector of the provided decision matrix using the Criteria Importance Through Intercriteria Correlation method.

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weigh

 weigh (z_matrix:<built-infunctionarray>, w_method:str, c_method:str=None)

Return the weight vector of the provided decision matrix using the selected weighting method.

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topsis

 topsis (z_matrix:<built-infunctionarray>, w_vector:str,
         is_benefit_z:list)

Return the Technique for Order Preference by Similarity to Ideal Solution scores of the provided decision matrix with the provided weight vector.

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cp

 cp (z_matrix:<built-infunctionarray>, w_vector:list, is_benefit_z:list)

Return the Technique for Order Preference by Similarity to Ideal Solution scores of the provided decision matrix with the provided weight vector.

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score

 score (z_matrix:<built-infunctionarray>, is_benefit_z:list,
        w_vector:list, s_method:str)

Return the selected scores of the provided decision matrix with the provided weight vector.