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Initial commit for the algorithm and data
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‎data/iris.csv

+150
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5.1,3.5,1.4,0.2,Iris-setosa
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4.9,3.0,1.4,0.2,Iris-setosa
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4.7,3.2,1.3,0.2,Iris-setosa
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4.6,3.1,1.5,0.2,Iris-setosa
5+
5.0,3.6,1.4,0.2,Iris-setosa
6+
5.4,3.9,1.7,0.4,Iris-setosa
7+
4.6,3.4,1.4,0.3,Iris-setosa
8+
5.0,3.4,1.5,0.2,Iris-setosa
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4.4,2.9,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
11+
5.4,3.7,1.5,0.2,Iris-setosa
12+
4.8,3.4,1.6,0.2,Iris-setosa
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4.8,3.0,1.4,0.1,Iris-setosa
14+
4.3,3.0,1.1,0.1,Iris-setosa
15+
5.8,4.0,1.2,0.2,Iris-setosa
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5.7,4.4,1.5,0.4,Iris-setosa
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5.4,3.9,1.3,0.4,Iris-setosa
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5.1,3.5,1.4,0.3,Iris-setosa
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5.7,3.8,1.7,0.3,Iris-setosa
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5.1,3.8,1.5,0.3,Iris-setosa
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5.4,3.4,1.7,0.2,Iris-setosa
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5.1,3.7,1.5,0.4,Iris-setosa
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4.6,3.6,1.0,0.2,Iris-setosa
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5.1,3.3,1.7,0.5,Iris-setosa
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4.8,3.4,1.9,0.2,Iris-setosa
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5.0,3.0,1.6,0.2,Iris-setosa
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5.0,3.4,1.6,0.4,Iris-setosa
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5.2,3.5,1.5,0.2,Iris-setosa
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5.2,3.4,1.4,0.2,Iris-setosa
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4.7,3.2,1.6,0.2,Iris-setosa
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4.8,3.1,1.6,0.2,Iris-setosa
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5.4,3.4,1.5,0.4,Iris-setosa
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5.2,4.1,1.5,0.1,Iris-setosa
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5.5,4.2,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.0,3.2,1.2,0.2,Iris-setosa
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5.5,3.5,1.3,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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4.4,3.0,1.3,0.2,Iris-setosa
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5.1,3.4,1.5,0.2,Iris-setosa
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5.0,3.5,1.3,0.3,Iris-setosa
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4.5,2.3,1.3,0.3,Iris-setosa
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4.4,3.2,1.3,0.2,Iris-setosa
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5.0,3.5,1.6,0.6,Iris-setosa
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5.1,3.8,1.9,0.4,Iris-setosa
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4.8,3.0,1.4,0.3,Iris-setosa
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5.1,3.8,1.6,0.2,Iris-setosa
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4.6,3.2,1.4,0.2,Iris-setosa
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5.3,3.7,1.5,0.2,Iris-setosa
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5.0,3.3,1.4,0.2,Iris-setosa
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7.0,3.2,4.7,1.4,Iris-versicolor
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6.4,3.2,4.5,1.5,Iris-versicolor
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6.9,3.1,4.9,1.5,Iris-versicolor
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5.5,2.3,4.0,1.3,Iris-versicolor
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6.5,2.8,4.6,1.5,Iris-versicolor
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5.7,2.8,4.5,1.3,Iris-versicolor
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6.3,3.3,4.7,1.6,Iris-versicolor
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4.9,2.4,3.3,1.0,Iris-versicolor
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6.6,2.9,4.6,1.3,Iris-versicolor
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5.2,2.7,3.9,1.4,Iris-versicolor
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5.0,2.0,3.5,1.0,Iris-versicolor
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5.9,3.0,4.2,1.5,Iris-versicolor
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6.0,2.2,4.0,1.0,Iris-versicolor
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6.1,2.9,4.7,1.4,Iris-versicolor
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5.6,2.9,3.6,1.3,Iris-versicolor
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6.7,3.1,4.4,1.4,Iris-versicolor
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5.6,3.0,4.5,1.5,Iris-versicolor
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5.8,2.7,4.1,1.0,Iris-versicolor
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6.2,2.2,4.5,1.5,Iris-versicolor
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5.6,2.5,3.9,1.1,Iris-versicolor
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5.9,3.2,4.8,1.8,Iris-versicolor
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6.1,2.8,4.0,1.3,Iris-versicolor
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6.3,2.5,4.9,1.5,Iris-versicolor
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6.1,2.8,4.7,1.2,Iris-versicolor
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6.4,2.9,4.3,1.3,Iris-versicolor
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6.6,3.0,4.4,1.4,Iris-versicolor
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6.8,2.8,4.8,1.4,Iris-versicolor
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6.7,3.0,5.0,1.7,Iris-versicolor
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6.0,2.9,4.5,1.5,Iris-versicolor
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5.7,2.6,3.5,1.0,Iris-versicolor
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5.5,2.4,3.8,1.1,Iris-versicolor
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5.5,2.4,3.7,1.0,Iris-versicolor
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5.8,2.7,3.9,1.2,Iris-versicolor
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6.0,2.7,5.1,1.6,Iris-versicolor
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5.4,3.0,4.5,1.5,Iris-versicolor
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6.0,3.4,4.5,1.6,Iris-versicolor
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6.7,3.1,4.7,1.5,Iris-versicolor
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6.3,2.3,4.4,1.3,Iris-versicolor
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5.6,3.0,4.1,1.3,Iris-versicolor
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5.5,2.5,4.0,1.3,Iris-versicolor
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5.5,2.6,4.4,1.2,Iris-versicolor
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6.1,3.0,4.6,1.4,Iris-versicolor
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5.8,2.6,4.0,1.2,Iris-versicolor
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5.0,2.3,3.3,1.0,Iris-versicolor
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5.6,2.7,4.2,1.3,Iris-versicolor
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5.7,3.0,4.2,1.2,Iris-versicolor
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5.7,2.9,4.2,1.3,Iris-versicolor
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6.2,2.9,4.3,1.3,Iris-versicolor
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5.1,2.5,3.0,1.1,Iris-versicolor
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5.7,2.8,4.1,1.3,Iris-versicolor
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6.3,3.3,6.0,2.5,Iris-virginica
102+
5.8,2.7,5.1,1.9,Iris-virginica
103+
7.1,3.0,5.9,2.1,Iris-virginica
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6.3,2.9,5.6,1.8,Iris-virginica
105+
6.5,3.0,5.8,2.2,Iris-virginica
106+
7.6,3.0,6.6,2.1,Iris-virginica
107+
4.9,2.5,4.5,1.7,Iris-virginica
108+
7.3,2.9,6.3,1.8,Iris-virginica
109+
6.7,2.5,5.8,1.8,Iris-virginica
110+
7.2,3.6,6.1,2.5,Iris-virginica
111+
6.5,3.2,5.1,2.0,Iris-virginica
112+
6.4,2.7,5.3,1.9,Iris-virginica
113+
6.8,3.0,5.5,2.1,Iris-virginica
114+
5.7,2.5,5.0,2.0,Iris-virginica
115+
5.8,2.8,5.1,2.4,Iris-virginica
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6.4,3.2,5.3,2.3,Iris-virginica
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6.5,3.0,5.5,1.8,Iris-virginica
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7.7,3.8,6.7,2.2,Iris-virginica
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7.7,2.6,6.9,2.3,Iris-virginica
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6.0,2.2,5.0,1.5,Iris-virginica
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6.9,3.2,5.7,2.3,Iris-virginica
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5.6,2.8,4.9,2.0,Iris-virginica
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7.7,2.8,6.7,2.0,Iris-virginica
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6.3,2.7,4.9,1.8,Iris-virginica
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6.7,3.3,5.7,2.1,Iris-virginica
126+
7.2,3.2,6.0,1.8,Iris-virginica
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6.2,2.8,4.8,1.8,Iris-virginica
128+
6.1,3.0,4.9,1.8,Iris-virginica
129+
6.4,2.8,5.6,2.1,Iris-virginica
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7.2,3.0,5.8,1.6,Iris-virginica
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7.4,2.8,6.1,1.9,Iris-virginica
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7.9,3.8,6.4,2.0,Iris-virginica
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6.4,2.8,5.6,2.2,Iris-virginica
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6.3,2.8,5.1,1.5,Iris-virginica
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6.1,2.6,5.6,1.4,Iris-virginica
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7.7,3.0,6.1,2.3,Iris-virginica
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6.3,3.4,5.6,2.4,Iris-virginica
138+
6.4,3.1,5.5,1.8,Iris-virginica
139+
6.0,3.0,4.8,1.8,Iris-virginica
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6.9,3.1,5.4,2.1,Iris-virginica
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6.7,3.1,5.6,2.4,Iris-virginica
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6.9,3.1,5.1,2.3,Iris-virginica
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5.8,2.7,5.1,1.9,Iris-virginica
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6.8,3.2,5.9,2.3,Iris-virginica
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6.7,3.3,5.7,2.5,Iris-virginica
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6.7,3.0,5.2,2.3,Iris-virginica
147+
6.3,2.5,5.0,1.9,Iris-virginica
148+
6.5,3.0,5.2,2.0,Iris-virginica
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6.2,3.4,5.4,2.3,Iris-virginica
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5.9,3.0,5.1,1.8,Iris-virginica

‎src/github.com/bugra/kmeans/distance.go

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package kmeans
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/*
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This module provides common distance functions for measuring distance
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between observations.
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Minkowski Distance is one the most inclusive among one as other distances are only
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a specific case of Minkowski Distance(Chebyshev Distance is not straightforward, though).
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when p=1 in MinkowskiDistance, it becomes ManhattanDistance,
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when p=2 in MinkowskiDistance, it becomes EuclideanDIstance,
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when p goes infinity, it becomes ChebyshevDistance.
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Since the ManhattanDistance and EuclideanDistance are very frequently used, they are
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implemented separately.
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*/
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import (
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"math"
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)
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// Lp Norm of an array, given p >= 1
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func LPNorm(vector []float64, p float64) (float64, error) {
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distance := 0.
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for _, jj := range vector {
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distance += math.Pow(math.Abs(jj), p)
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}
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return math.Pow(distance, 1/p), nil
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}
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// 1-norm distance (l_1 distance)
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func ManhattanDistance(firstVector, secondVector []float64) (float64, error) {
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distance := 0.
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for ii := range firstVector {
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distance += math.Abs(firstVector[ii] - secondVector[ii])
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}
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return distance, nil
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}
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// 2-norm distance (l_2 distance)
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func EuclideanDistance(firstVector, secondVector []float64) (float64, error) {
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distance := 0.
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for ii := range firstVector {
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distance += (firstVector[ii] - secondVector[ii]) * (firstVector[ii] - secondVector[ii])
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}
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return math.Sqrt(distance), nil
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}
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// Higher weight for the points that are far apart
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// Not a real metric as it does not obey triangle inequality
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func SquaredEuclideanDistance(firstVector, secondVector []float64) (float64, error) {
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distance, err := EuclideanDistance(firstVector, secondVector)
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return distance * distance, err
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}
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// p-norm distance (l_p distance)
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func MinkowskiDistance(firstVector, secondVector []float64, p float64) (float64, error) {
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distance := 0.
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for ii := range firstVector {
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distance += math.Pow(math.Abs(firstVector[ii]-secondVector[ii]), p)
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}
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return math.Pow(distance, 1/p), nil
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}
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// p-norm distance with weights (weighted l_p distance)
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func WeightedMinkowskiDistance(firstVector, secondVector, weightVector []float64, p float64) (float64, error) {
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distance := 0.
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for ii := range firstVector {
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distance += weightVector[ii] * math.Pow(math.Abs(firstVector[ii]-secondVector[ii]), p)
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}
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return math.Pow(distance, 1/p), nil
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}
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// infinity norm distance (l_inf distance)
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func ChebyshevDistance(firstVector, secondVector []float64) (float64, error) {
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distance := 0.
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for ii := range firstVector {
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if math.Abs(firstVector[ii]-secondVector[ii]) >= distance {
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distance = math.Abs(firstVector[ii] - secondVector[ii])
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}
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}
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return distance, nil
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}
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func HammingDistance(firstVector, secondVector []float64) (float64, error) {
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distance := 0.
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for ii := range firstVector {
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if firstVector[ii] != secondVector[ii] {
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distance++
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}
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}
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return distance, nil
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}
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func BrayCurtisDistance(firstVector, secondVector []float64) (float64, error) {
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numerator, denominator := 0., 0.
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for ii := range firstVector {
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numerator += math.Abs(firstVector[ii] - secondVector[ii])
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denominator += math.Abs(firstVector[ii] + secondVector[ii])
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}
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return numerator / denominator, nil
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}
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func CanberraDistance(firstVector, secondVector []float64) (float64, error) {
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distance := 0.
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for ii := range firstVector {
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distance += (math.Abs(firstVector[ii]-secondVector[ii]) / (math.Abs(firstVector[ii]) + math.Abs(secondVector[ii])))
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}
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return distance, nil
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}

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