0.1.2

Similarity Py

Installation

Install the package

    $ pip install similarityPy

Dependencies

enum

###Distance Algorithms

#### Numerical Data

#####  Norm

      Data: [{x, y, z}]

      Formula:

#####  Manhattan Distance

      Data: [{a, b, c}, {x, y, z}]

      Formula:

#####  Euclidean Distance

      Data: [{a, b, c}, {x, y, z}]

      Formula:

#####  Squared Euclidean Distance

      Data: [{a, b, c}, {x, y, z}]

      Formula:

#####  Normalized Squared Euclidean Distance

      Data: [{a, b}, {x, y}]

      Formula:

#####  Chessboard Distance

      Data: [{a, b, c}, {x, y, z}]

      Formula:

#####  Bray Curtis Distance

      Data: [{a, b, c}, {x, y, z}]

      Formula:

#####  Canberra Distance

      Data: [{a, b, c}, {x, y, z}]

      Formula:

#####  Cosine Distance

      Data: [{a, b, c}, {x, y, z}]

      Formula:

#####  Correlation Distance

      Data: [{a, b, c}, {x, y, z}]

      Formula:

#### Boolean Data

#####  Jaccard Dissimilarity

      Data: [{True,False,True}, {True,True,False}]

      Explanation:[u,v] is equivalent to , where n_ij is the number of corresponding pairs of elements in u and v respectively equal to i and j.

#####  Matching Dissimilarity

      Data: [{True,False,True}, {True,True,False}]

      Explanation:[u,v] is equivalent to (n₁₀+n₀₁)/Length[u], where n_ij is the number of corresponding pairs of elements in u and v respectively equal to i and j.

#####  Dice Dissimilarity

      Data: [{True,False,True}, {True,True,False}]

      Explanation:[u,v] is equivalent to , where n_ij is the number of corresponding pairs of elements in u and v respectively equal to i and j.

#####  Rogers Tanimoto Dissimilarity

      Data: [{True,False,True}, {True,True,False}]

      Explanation:[u,v] is equivalent to , where n_ij is the number of corresponding pairs of elements in u and v respectively equal to i and j.

#####  Russell Rao Dissimilarity

      Data: [{True,False,True}, {True,True,False}]

      Explanation:[u,v] is equivalent to (n₁₀+n₀₁+n₀₀)/Length[u], where n_ij is the number of corresponding pairs of elements in u and v respectively equal to i and j.

#####  Sokal Sneath Dissimilarity

      Data: [{True,False,True}, {True,True,False}]

      Explanation:[u,v] is equivalent to , where n_ij is the number of corresponding pairs of elements in and respectively equal to i and j.

#####  Yule Dissimilarity

      Data: [{True,False,True}, {True,True,False}]

      Explanation:[u,v] is equivalent to , where n_ij is the number of corresponding pairs of elements in and respectively equal to i and j.

#### String Data

#####  Hamming Distance

      Data: [{a, b, c}, {x, y, z}]

      Explanation:[u,v] gives the number of elements whose values disagree in u and v.

#####  Edit Distance

      Data: [{a, b, c}, {x, y, z}]

      Explanation:[u,v] gives the number of one-element deletions, insertions, and substitutions required to transform u to v.

#####  Damerau Levenshtein Distance

      Data: [{a, b, c}, {x, y, z}]

      Explanation:[u,v] gives the number of one-element deletions, insertions, substitutions, and transpositions required to transform u to v.

#####  Needleman Wunsch Similarity (Not Implemented Yet)

      Data: [{a, b, c}, {x, y, z}]

      Explanation:[u,v] finds an optimal global alignment between the elements of u and v, and returns the number of one-element matches.

#####  Smith Waterman Similarity (Not Implemented Yet)

      Data: [{a, b, c}, {x, y, z}]

      Explanation:[u,v] finds an optimal local alignment between the elements of u and v, and returns the number of one-element matches.

##Testing

Run all tests:

    $ python -m unittest discover -s tests -p '*_test.py'

Start test with nose and code coverage:

    $ nosetests --with-cov  --cov-report html  --cov  similarityPy tests/