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package applications.ml;

import datastructs.maths.DenseMatrixSet;

import datastructs.maths.RowBuilder;

import datastructs.maths.Vector;

import datastructs.utils.RowType;

import maths.functions.distances.EuclideanVectorCalculator;

import ml.classifiers.KNNClassifier;

import utils.ClassificationVoter;

import java.util.ArrayList;

import java.util.List;

/** Category: Machine Learning

* ID: Example1

* Description: Classification with vanilla KNN algorithm

* Taken From:

* Details:

* TODO

*/

public class Example1 {

public static void main(String[] args){

DenseMatrixSet<Double> dataSet = new DenseMatrixSet(RowType.Type.DOUBLE_VECTOR, new RowBuilder());

dataSet.create(12, 2);

dataSet.set(0, 1.0, 3.0);

dataSet.set(1, 1.5, 2.0);

dataSet.set(2, 2.0, 1.0);

dataSet.set(3, 2.5, 4.0 );

dataSet.set(4, 3.0, 1.5);

dataSet.set(5, 3.5, 2.5);

dataSet.set(6, 5.0, 5.0);

dataSet.set(7, 5.5, 4.0);

dataSet.set(8, 6.0, 6.0);

dataSet.set(9, 6.5, 4.5 );

dataSet.set(10, 7.0, 1.5);

dataSet.set(11, 8.0, 2.5);

List<Integer> labels = new ArrayList<>(dataSet.m());

for(int i=0; i<6; ++i) {

labels.add(0);

}

for(int i = 6; i<dataSet.m(); ++i) {

labels.add(1);

}

KNNClassifier<Double, DenseMatrixSet<Double>,

EuclideanVectorCalculator<Double>,

ClassificationVoter> classifier = new KNNClassifier<Double, DenseMatrixSet<Double>,

EuclideanVectorCalculator<Double>, ClassificationVoter>(2, false);

classifier.setDistanceCalculator(new EuclideanVectorCalculator<Double>());

classifier.setMajorityVoter(new ClassificationVoter());

classifier.train(dataSet, labels);

Vector r = new Vector(3.1, 2.2);

Integer classIdx = classifier.predict(r);

System.out.println("Point "+r+" has class index "+ classIdx);

r = new Vector(9.1, 6.2);

classIdx = classifier.predict(r);

System.out.println("Point "+r+" has class index "+ classIdx);

}

}