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package applications.ml;

import datastructs.interfaces.IVector;

import datastructs.maths.DenseMatrixSet;

import maths.ConfusionMatrix;

import maths.functions.distances.EuclideanVectorCalculator;

import ml.classifiers.KNNClassifier;

import utils.*;

import java.io.IOException;

import java.util.ArrayList;

import java.util.List;

/** Category: Machine Learning

* ID: Example11

* Description: Using the ```ConfusionMatrix``` class

* Taken From:

* Taken From:

* Details:

* TODO

*/

public class Example12 {

public static void main(String[] args) throws IOException, IllegalArgumentException{

// load the data set

Pair<DenseMatrixSet<Double>, List<Integer>> data = DataSetLoader.loadIrisDataSet();

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(data.first, data.second);

List<Pair<Integer, Integer>> results = new ArrayList<>();

for(int rowIdx=0; rowIdx<data.first.m(); ++rowIdx){

IVector<Double> row = data.first.getRow(rowIdx);

Integer classIdx = data.second.get(rowIdx);

Integer predictedIdx = classifier.predict(row);

results.add(PairBuilder.makePair(classIdx, predictedIdx ));

}

List<String> names = new ArrayList<>();

names.add("Iris-setosa");

names.add("Iris-versicolor");

names.add("Iris-virginica");

ConfusionMatrix confusionMatrix = new ConfusionMatrix(results, names);

System.out.println(confusionMatrix.toString());

}

}