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package examples.ml.example3;

import datasets.DenseMatrixSet;
import datasets.VectorDouble;
import datastructs.RowBuilder;
import datastructs.RowType;
import maths.functions.distances.EuclideanVectorCalculator;
import ml.classifiers.KNNClassifier;
import ml.classifiers.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 Example3 {

    public static void main(String[] args) {

        DenseMatrixSet 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 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,
                EuclideanVectorCalculator,
                ClassificationVoter> classifier = new KNNClassifier,
                EuclideanVectorCalculator, ClassificationVoter>(2, false);

        classifier.setDistanceCalculator(new EuclideanVectorCalculator());
        classifier.setMajorityVoter(new ClassificationVoter());


        classifier.train(dataSet, labels);

        VectorDouble r = new VectorDouble(3.1, 2.2);
        Integer classIdx = classifier.predict(r);

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

        r = new VectorDouble(9.1, 6.2);
        classIdx = classifier.predict(r);
    }
}