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Java Library for Statistical Analysis.

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# Example 5: Clustering with KMeans

## Contents
* [Overview](#overview) 
    * [KMeans algorithm](#kmeans_algorithm)
* [Import files](#include_files)
* [The main function](#m_func)
* [Results](#results)
* [Source Code](#source_code)

##  Overview

###  KMeans algorithm



##  Import files

```
package examples.ml.example5;

import utils.DefaultIterativeAlgorithmController;
import base.CommonConstants;
import datastructs.IVector;
import datasets.DenseMatrixSet;
import datastructs.RowBuilder;
import datastructs.RowType;
import maths.functions.distances.DistanceCalculator;
import maths.functions.distances.EuclideanVectorCalculator;
import maths.functions.generators.IRandomGenerator;
import maths.functions.generators.UniformRandomGenerator;
import ml.clustering.KMeans;
import ml.clustering.KMeansInput;

```

##  The main function

```
public class Example5 {

    public static void main(String[] args){

        //some synthetic data
        DenseMatrixSet matrix = new DenseMatrixSet(RowType.Type.DOUBLE_VECTOR, new RowBuilder(), 6, 2, 0.0);
        matrix.set(0, 1.0, 2.0);
        matrix.set(1, 1.0, 4.0);
        matrix.set(2, 1.0, 0.0);
        matrix.set(3, 10.0, 2.0);
        matrix.set(4, 10.0, 2.0);
        matrix.set(5, 10.0, 0.0);

        KMeansInput input = new KMeansInput();
        input.k = 2;
        input.iterationContorller = new DefaultIterativeAlgorithmController(10, CommonConstants.getTol());

        KMeans kmeans = new KMeans<>(input);

        DistanceCalculator, Double> similarity = new EuclideanVectorCalculator();
        IRandomGenerator randomGenerator = new UniformRandomGenerator();

        kmeans.cluster(matrix, similarity, randomGenerator);
    }
}
    
```

##  Results

```

```

##  Source Code

Example5.java