Package for Multi-Criteria Decision Analysis with distance-based TOPSIS method
Project description
distance-metrics-for-mcda
Python 3 library for Multi-Criteria Decision Analysis based on distance metrics.
Installation
pip install distance-metrics-mcda
Methods
This is Python 3 library providing package distance_metrics_mcda
that includes metrics that can measure alternatives distance from
reference solutions in multi-criteria decision analysis. This library contains module weighting_methods
with the following distance metrics:
- Euclidean distance
euclidean
- Manhattan (Taxicab) distance
manhattan
- Hausdorff distance
hausdorff
- Correlation distance
correlation
- Chebyshev distance
chebyshev
- Standardized euclidean distance
std_euclidean
- Cosine distance
cosine
- Cosine similarity measure
csm
- Squared Euclidean distance
squared_euclidean
- Sorensen or Bray-Curtis distance
bray_curtis
- Canberra distance
canberra
- Lorentzian distance
lorentzian
- Jaccard distance
jaccard
- Dice distance
dice
- Bhattacharyya distance
bhattacharyya
- Hellinger distance
hellinger
- Matusita distance
matusita
- Squared-chord distance
squared_chord
- Pearson chi-square distance
pearson_chi_square
- Squared chi-square distance
squared_chi_square
The library also provides other methods necessary for multi-criteria decision analysis, which are as follows: The TOPSIS method for
multi-criteria decision analysis TOPSIS in module mcda_methods
. The TOPSIS method is based on measuring the distance of alternatives from
Positive Ideal Solution and Negative Ideal Solution using distance_metrics
mentioned above.
Normalization techniques:
- Linear
linear_normalization
- Minimum-Maximum
minmax_normalization
- Maximum
max_normalization
- Sum
sum_normalization
- Vector
vector_normalization
Correlation coefficients:
- Spearman rank correlation coefficient rs
spearman
- Weighted Spearman rank correlation coefficient rw
weighted_spearman
- Pearson coefficent
pearson_coeff
Objective weighting methods:
- Entropy weighting method
entropy_weighting
- CRITIC weighting method
critic_weighting
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for distance-metrics-mcda-0.0.2.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 01de2f5e338e9b1a4bc3f0a4dfae471b085cdb769bf82515be7f8b2c14948ca8 |
|
MD5 | 6d88de231c2935cea8bbdeb709ccede1 |
|
BLAKE2b-256 | a5ef92896835d5b9dffb78c5eeea7d11d3390d42cf11c65e8c436b516b682149 |
Hashes for distance_metrics_mcda-0.0.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 74a83e2dd67922c3d1c958d90b090be140d39b77dbef7e2eb059db360f2d4d4f |
|
MD5 | 971cdbd44010647f80a42a1f288c89f0 |
|
BLAKE2b-256 | 2d85f1d888475cd56df5ced946dd03bf0d20c17d86531a42aacd034faaa6f204 |