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package examples.optimization.example1;

import datasets.VectorDouble;
import utils.DefaultIterativeAlgorithmController;
import utils.IterativeAlgorithmResult;
import optimization.GradientDescent;
import optimization.GDInput;
import datasets.DenseMatrixSet;
import datastructs.RowBuilder;
import datastructs.RowType;
import maths.functions.LinearVectorPolynomial;
import maths.errorfunctions.MSEVectorFunction;
import tech.tablesaw.api.Table;
import utils.TableDataSetLoader;

import java.io.File;
import java.io.IOException;

/** Category: Machine Learning
 * ID: Example5
 * Description: Using Batch Gradient Descent with only one feature
 * Taken From:
 * Details:
 * TODO
 */

public class Example1 {


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

        // load the data
        Table dataSet = TableDataSetLoader.loadDataSet(new File("src/main/resources/datasets/car_plant.csv"));

        VectorDouble labels = new VectorDouble(dataSet, "Electricity Usage");
        Table reducedDataSet = dataSet.removeColumns("Electricity Usage").first(dataSet.rowCount());

        DenseMatrixSet denseMatrixSet = new DenseMatrixSet(RowType.Type.DOUBLE_VECTOR, new RowBuilder(), reducedDataSet.rowCount(), 2, 1.0);
        denseMatrixSet.setColumn(1, reducedDataSet.doubleColumn(0));

        LinearVectorPolynomial hypothesis = new LinearVectorPolynomial(1);

        GDInput gdInput = new GDInput();
        gdInput.showIterations = true;
        gdInput.eta=0.01;
        gdInput.errF = new MSEVectorFunction(hypothesis);
        gdInput.iterationContorller = new DefaultIterativeAlgorithmController(10000,1.0e-8);

        GradientDescent gdSolver = new GradientDescent(gdInput);
        IterativeAlgorithmResult result = gdSolver.optimize(denseMatrixSet, labels, hypothesis);

        System.out.println(result);
        System.out.println("Intercept: "+hypothesis.getCoeff(0)+" slope: "+hypothesis.getCoeff(1));

    }
}