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package applications.ml;

import algorithms.optimizers.BatchGradientDescent;

import algorithms.optimizers.GDInput;

import algorithms.utils.DefaultIterativeAlgorithmController;

import algorithms.utils.IterativeAlgorithmResult;

import datastructs.maths.DenseMatrixSet;

import datastructs.maths.RowBuilder;

import datastructs.maths.Vector;

import datastructs.utils.RowType;

import maths.errorfunctions.LogisticMSEVectorFunction;

import maths.functions.LinearVectorPolynomial;

import maths.functions.SigmoidFunction;

import ml.classifiers.LogisticRegressionClassifier;

import tech.tablesaw.api.Table;

import tech.tablesaw.columns.Column;

import utils.Pair;

import utils.PairBuilder;

import utils.TableDataSetLoader;

import java.io.File;

import java.io.IOException;

/** Category: Machine Learning

* ID: Example9

* Description: Classification with vanilla LogisticRegression

* Taken From:

* Details:

* TODO

*/

public class Example9 {

public static Pair<DenseMatrixSet, Vector> createDataSet() throws IOException, IllegalArgumentException {

// load the data

Table dataSetTable = TableDataSetLoader.loadDataSet(new File("src/main/resources/datasets/iris_dataset_reduced.csv"));

Column species = dataSetTable.column("species");

Vector labels = new Vector(species.size());

for (int i = 0; i < species.size(); i++) {

String label = (String) species.get(i);

if(label.equals("Iris-setosa")){

labels.set(i, 0.0);

}

else if(label.equals("Iris-versicolor")){

labels.set(i, 1);

}

else{

throw new IllegalArgumentException("Unknown class");

}

}

Table reducedDataSet = dataSetTable.removeColumns("species").first(dataSetTable.rowCount());

DenseMatrixSet dataSet = new DenseMatrixSet(RowType.Type.DOUBLE_VECTOR, new RowBuilder(), reducedDataSet.rowCount(), reducedDataSet.columnCount() + 1, 1.0);

dataSet.setColumn(1, reducedDataSet.doubleColumn(0));

dataSet.setColumn(2, reducedDataSet.doubleColumn(1));

dataSet.setColumn(3, reducedDataSet.doubleColumn(2));

dataSet.setColumn(4, reducedDataSet.doubleColumn(3));

return PairBuilder.makePair(dataSet, labels);

}

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

Pair<DenseMatrixSet, Vector> data = Example9.createDataSet();

System.out.println("Number of rows: "+data.first.m());

System.out.println("Number of labels: "+data.second.size());

SigmoidFunction hypothesis = new SigmoidFunction(new LinearVectorPolynomial(4));

GDInput gdInput = new GDInput();

gdInput.showIterations = true;

gdInput.eta = 0.01;

gdInput.errF = new LogisticMSEVectorFunction(hypothesis);

gdInput.iterationContorller = new DefaultIterativeAlgorithmController(100000,1.0e-8);

// the optimizer

BatchGradientDescent gdSolver = new BatchGradientDescent(gdInput);

// the classifier

LogisticRegressionClassifier<DenseMatrixSet<Double>, LinearVectorPolynomial> classifier = new LogisticRegressionClassifier(hypothesis, gdSolver );

// train the model

IterativeAlgorithmResult result = (IterativeAlgorithmResult) classifier.train(data.first, data.second);

System.out.println(" ");

System.out.println(result);

System.out.println("Intercept: "+hypothesis.getCoeff(0)+

" slope1: "+hypothesis.getCoeff(1) +

" slope2: "+hypothesis.getCoeff(2) +

" slope3: "+hypothesis.getCoeff(3));

// use 1.0 to account for the intercept term

Vector point = new Vector(1.0, 5.7,2.8,4.1,1.3);

Integer classIdx = classifier.predict(point);

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

}

}