similarityPy 0.1.1

Similarity Algorithm (Data Mining) implementation in Python

Similarity Py [](https://travis-ci.org/cenkbircanoglu/similarityPy) [](https://coveralls.io/r/cenkbircanoglu/similarityPy?branch=master)
===================

###Distance Algorithms

#### Numerical Data


#####&nbsp;&nbsp;<em>Norm</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 

#####&nbsp;&nbsp;<em>Manhattan Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 


#####&nbsp;&nbsp;<em>Euclidean Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 

#####&nbsp;&nbsp;<em>Squared Euclidean Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 

#####&nbsp;&nbsp;<em>Normalized Squared Euclidean Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b}, {x, y}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 

#####&nbsp;&nbsp;<em>Chessboard Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 

#####&nbsp;&nbsp;<em>Bray Curtis Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 

#####&nbsp;&nbsp;<em>Canberra Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 

#####&nbsp;&nbsp;<em>Cosine Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 

#####&nbsp;&nbsp;<em>Correlation Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Formula**: 


####&nbsp;Boolean Data


#####&nbsp;&nbsp;<em>Jaccard Dissimilarity</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{True,False,True}, {True,True,False}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**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.

#####&nbsp;&nbsp;<em>Matching Dissimilarity</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{True,False,True}, {True,True,False}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**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.

#####&nbsp;&nbsp;<em>Dice Dissimilarity</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{True,False,True}, {True,True,False}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**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.

#####&nbsp;&nbsp;<em>Rogers Tanimoto Dissimilarity</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{True,False,True}, {True,True,False}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**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.

#####&nbsp;&nbsp;<em>Russell Rao Dissimilarity</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{True,False,True}, {True,True,False}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**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.

#####&nbsp;&nbsp;<em>Sokal Sneath Dissimilarity</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{True,False,True}, {True,True,False}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**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.

#####&nbsp;&nbsp;<em>Yule Dissimilarity</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{True,False,True}, {True,True,False}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**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.

####&nbsp;String Data


#####&nbsp;&nbsp;<em>Hamming Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Explanation**:[u,v] gives the number of elements whose values disagree in u and v.

#####&nbsp;&nbsp;<em>Edit Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Explanation**:[u,v] gives the number of one-element deletions, insertions, and substitutions required to transform u to v.

#####&nbsp;&nbsp;<em>Damerau Levenshtein Distance</em>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Explanation**:[u,v] gives the number of one-element deletions, insertions, substitutions, and transpositions required to transform u to v.

#####&nbsp;&nbsp;<em>Needleman Wunsch Similarity</em> (Not Implemented Yet)

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Explanation**:[u,v] finds an optimal global alignment between the elements of u and v, and returns the number of one-element matches.

#####&nbsp;&nbsp;<em>Smith Waterman Similarity</em> (Not Implemented Yet)

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Data**: [{a, b, c}, {x, y, z}] <br/>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**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:
```bash
$ python -m unittest discover -s tests -p '*_test.py'
```

Start test with nose and code coverage:
```bash
$ nosetests --with-cov --cov-report html --cov apps tests/
```