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package applications.ml;

import algorithms.utils.DefaultIterativeAlgorithmController;

import base.CommonConstants;

import datastructs.interfaces.IVector;

import datastructs.maths.DenseMatrixSet;

import datastructs.maths.RowBuilder;

import datastructs.utils.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;

/** Category: Machine Learning

* ID: Example11

* Description: Clustering with ```KMeans```

* Taken From:

* Taken From:

* Details:

* TODO

*/

public class Example11 {

public static void main(String[] args){

//some synthetic data

DenseMatrixSet<Double> 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<Double, Double> kmeans = new KMeans<>(input);

DistanceCalculator<IVector<Double>, Double> similarity = new EuclideanVectorCalculator<Double>();

IRandomGenerator randomGenerator = new UniformRandomGenerator();

kmeans.cluster(matrix, similarity, randomGenerator);

}

}