distance_metrics_mcda.distance_metrics

Module Contents

Functions

euclidean(A, B)	Calculate Euclidean distance between two vectors A and B.
manhattan(A, B)	Calculate Manhattan (Taxicab) distance between two vectors A and B.
hausdorff_distance(A, B)
hausdorff(A, B)	Calculate Hausdorff distance between two vectors A and B.
correlation(A, B)	Calculate Correlation distance between two vectors A and B.
chebyshev(A, B)	Calculate Chebyshev distance between two vectors A and B.
std_euclidean(A, B)	Calculate Standardized Euclidean distance between two vectors A and B.
cosine(A, B)	Calculate Cosine distance between two vectors A and B.
csm(A, B)	Calculate Cosine similarity measure of distance between two vectors A and B.
squared_euclidean(A, B)	Calculate Squared Euclidean distance between two vectors A and B.
bray_curtis(A, B)	Calculate Bray-Curtis distance between two vectors A and B.
canberra(A, B)	Calculate Canberra distance between two vectors A and B.
lorentzian(A, B)	Calculate Lorentzian distance between two vectors A and B.
jaccard(A, B)	Calculate Jaccard distance between two vectors A and B.
dice(A, B)	Calculate Dice distance between two vectors A and B.
bhattacharyya(A, B)	Calculate Bhattacharyya distance between two vectors A and B.
hellinger(A, B)	Calculate Hellinger distance between two vectors A and B.
matusita(A, B)	Calculate Matusita distance between two vectors A and B.
squared_chord(A, B)	Calculate Squared-Chord distance between two vectors A and B.
pearson_chi_square(A, B)	Calculate Pearson Chi Square distance between two vectors A and B.
squared_chi_square(A, B)	Calculate Squared Chi Sqaure distance between two vectors A and B.

distance_metrics_mcda.distance_metrics.euclidean(A, B)

Calculate Euclidean distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = euclidean(A, B)

distance_metrics_mcda.distance_metrics.manhattan(A, B)

Calculate Manhattan (Taxicab) distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = manhattan(A, B)

distance_metrics_mcda.distance_metrics.hausdorff_distance(A, B)

distance_metrics_mcda.distance_metrics.hausdorff(A, B)

Calculate Hausdorff distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = hausdorff(A, B)

distance_metrics_mcda.distance_metrics.correlation(A, B)

Calculate Correlation distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = correlation(A, B)

distance_metrics_mcda.distance_metrics.chebyshev(A, B)

Calculate Chebyshev distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = chebyshev(A, B)

distance_metrics_mcda.distance_metrics.std_euclidean(A, B)

Calculate Standardized Euclidean distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = std_euclidean(A, B)

distance_metrics_mcda.distance_metrics.cosine(A, B)

Calculate Cosine distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = cosine(A, B)

distance_metrics_mcda.distance_metrics.csm(A, B)

Calculate Cosine similarity measure of distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = csm(A, B)

distance_metrics_mcda.distance_metrics.squared_euclidean(A, B)

Calculate Squared Euclidean distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = squared_euclidean(A, B)

distance_metrics_mcda.distance_metrics.bray_curtis(A, B)

Calculate Bray-Curtis distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = bray_curtis(A, B)

distance_metrics_mcda.distance_metrics.canberra(A, B)

Calculate Canberra distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = canberra(A, B)

distance_metrics_mcda.distance_metrics.lorentzian(A, B)

Calculate Lorentzian distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = lorentzian(A, B)

distance_metrics_mcda.distance_metrics.jaccard(A, B)

Calculate Jaccard distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = jaccard(A, B)

distance_metrics_mcda.distance_metrics.dice(A, B)

Calculate Dice distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = dice(A, B)

distance_metrics_mcda.distance_metrics.bhattacharyya(A, B)

Calculate Bhattacharyya distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = bhattacharyya(A, B)

distance_metrics_mcda.distance_metrics.hellinger(A, B)

Calculate Hellinger distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = hellinger(A, B)

distance_metrics_mcda.distance_metrics.matusita(A, B)

Calculate Matusita distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = matusita(A, B)

distance_metrics_mcda.distance_metrics.squared_chord(A, B)

Calculate Squared-Chord distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = squared_chord(A, B)

distance_metrics_mcda.distance_metrics.pearson_chi_square(A, B)

Calculate Pearson Chi Square distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = pearson_chi_square(A, B)

distance_metrics_mcda.distance_metrics.squared_chi_square(A, B)

Calculate Squared Chi Sqaure distance between two vectors A and B.

Parameters

• A (ndarray) – First vector containing values

• B (ndarray) – Second vector containing values

Returns

distance value between two vectors

Return type

float

Examples

>>> distance = squared_chi_square(A, B)