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package maths.errorfunctions;

import base.CommonConstants;
import datasets.VectorDouble;
import datastructs.I2DDataSet;
import datastructs.IVector;
import maths.functions.IVectorRealFunction;

public class LogisticMSEVectorFunction implements IVectorErrorRealFunction {

    /**
     * Constructor
     */
    public LogisticMSEVectorFunction(IVectorRealFunction> hypothesis ){

        if(hypothesis == null){
            throw new IllegalArgumentException("Hypothesis function cannot be null");
        }
        this.hypothesis = hypothesis;
    }

    /**
     * Evaluate the error function using the given data, labels
     */
    @Override
    public  double evaluate(DataSetType data, VectorDouble labels){

        if(data.m() != labels.size()){
            throw new IllegalArgumentException("Invalid number of data points and labels vector size");
        }

        double result = 0.0;

        for(int rowIdx=0; rowIdx CommonConstants.getTol()){
                    result += 1.0;
                }
            }
            else{

                //do it normally
                //calculate the logarithms and check if they are
                //infinite or nana
                double log_one_minus_h = Math.log(1. - hypothesisValue);
                double log_h = Math.log(hypothesisValue);
                result += y*log_h +(1.-y)*log_one_minus_h;
            }
        }
        return -result/data.m();
    }

    /**
     * Returns the gradients on the given data
     */
    @Override
    public  VectorDouble gradients(DataSetType data, VectorDouble labels){


        VectorDouble gradients = new VectorDouble(this.hypothesis.numCoeffs(), 0.0);

        for(int rowIdx=0; rowIdx hypothesisGrads = this.hypothesis.coeffGradients(row);

            for(int coeff=0; coeff> hypothesis;
}