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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.MSEVectorFunction;

import maths.functions.LinearVectorPolynomial;

import maths.functions.regularizers.LassoRegularizer;

import maths.functions.regularizers.RidgeRegularizer;

import ml.regression.LinearRegressor;

import tech.tablesaw.api.DoubleColumn;

import tech.tablesaw.api.Table;

import utils.Pair;

import utils.PairBuilder;

import utils.TableDataSetLoader;

import java.io.File;

import java.io.IOException;

/** Category: Machine Learning

* ID: Example9

* Description: Linear regression with Lasso

* Taken From:

* Details:

* TODO

*/

public class Example10 {

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

// load the data

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

DoubleColumn y = dataSetTable.doubleColumn("y");

Vector labels = new Vector(y);

Table reducedDataSet = dataSetTable.removeColumns("y").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));

return PairBuilder.makePair(dataSet, labels);

}

public static void linearRegression(DenseMatrixSet data, Vector labels){

System.out.println("Doing LinearRegression");

LinearVectorPolynomial hypothesis = new LinearVectorPolynomial(1);

GDInput gdInput = new GDInput();

gdInput.showIterations = false;

gdInput.eta = 0.01;

gdInput.errF = new MSEVectorFunction(hypothesis);

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

// the optimizer

BatchGradientDescent gdSolver = new BatchGradientDescent(gdInput);

// the classifier

LinearRegressor<DenseMatrixSet<Double>> regressor = new LinearRegressor(hypothesis);

// train the model

IterativeAlgorithmResult result = (IterativeAlgorithmResult) regressor.train(data, labels, gdSolver);

System.out.println(" ");

System.out.println(result);

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

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

}

public static void ridgeRegression(DenseMatrixSet data, Vector labels){

System.out.println("Doing Ridge LinearRegression");

LinearVectorPolynomial hypothesis = new LinearVectorPolynomial(1);

RidgeRegularizer ridgeRegularizer = new RidgeRegularizer(0.001, 1, hypothesis);

GDInput gdInput = new GDInput();

gdInput.showIterations = false;

gdInput.eta = 0.01;

gdInput.errF = new MSEVectorFunction(hypothesis, ridgeRegularizer);

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

// the optimizer

BatchGradientDescent gdSolver = new BatchGradientDescent(gdInput);

// the classifier

LinearRegressor<DenseMatrixSet<Double>> regressor = new LinearRegressor(hypothesis);

// train the model

IterativeAlgorithmResult result = (IterativeAlgorithmResult) regressor.train(data, labels, gdSolver);

System.out.println(" ");

System.out.println(result);

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

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

}

public static void lassoRegression(DenseMatrixSet data, Vector labels){

System.out.println("Doing Lasso LinearRegression");

LinearVectorPolynomial hypothesis = new LinearVectorPolynomial(1);

LassoRegularizer lassoRegularizer = new LassoRegularizer(0.0001, 1, hypothesis);

GDInput gdInput = new GDInput();

gdInput.showIterations = false;

gdInput.eta = 0.01;

gdInput.errF = new MSEVectorFunction(hypothesis, lassoRegularizer);

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

// the optimizer

BatchGradientDescent gdSolver = new BatchGradientDescent(gdInput);

// the classifier

LinearRegressor<DenseMatrixSet<Double>> regressor = new LinearRegressor(hypothesis);

// train the model

IterativeAlgorithmResult result = (IterativeAlgorithmResult) regressor.train(data, labels, gdSolver);

System.out.println(" ");

System.out.println(result);

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

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

}

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

Pair<DenseMatrixSet, Vector> data = Example10.createDataSet();

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

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

Example10.linearRegression(data.first, data.second);

Example10.ridgeRegression(data.first, data.second);

Example10.lassoRegression(data.first, data.second);

}

}