Package flanagan.math

Examples of flanagan.math.Maximization


        bcf.nData = this.nData;
        bcf.yTransform = new double[this.nData];
        bcf.gaussianOrderMedians = this.gaussianOrderMedians;

        // Create an instance of Maximization
        Maximization max = new Maximization();

        // Initial estimate of lambdaOne
        double[] start = {1.0};

        // Initial step size in maximization search
        double[] step = {0.3};

        // Tolerance for maximization search termination
        double maxzTol = 1e-9;

        // Maximiztaion of the Gaussian probabilty plot correlation coefficient varying lambdaOne
        max.nelderMead(bcf, start, step, maxzTol);

        // coeff[0] = value of lambdaOne for a maximum Gaussian probabilty plot correlation coefficient
        double[] coeff = max.getParamValues();
        double lambda1 = coeff[0];

        //maximum Gaussian probabilty plot correlation coefficient
        double sampleR1 = max.getMaximum();

        // Repeat maximization starting equidistant from the final value of lambdaOne on the opposite side from the starting estimate
        start[0] = lambda1 - (start[0] - lambda1);
        max.nelderMead(bcf, start, step, maxzTol);
        coeff = max.getParamValues();
        this.lambdaOne = coeff[0];
        this.transformedSampleR =  max.getMaximum();

        // Choose solution with the largest Gaussian probabilty plot correlation coefficient
        if(sampleR1>this.transformedSampleR){
            this.transformedSampleR = sampleR1;
            this.lambdaOne = lambda1;
View Full Code Here


        bcf.nData = this.nData;
        bcf.yTransform = new double[this.nData];
        bcf.gaussianOrderMedians = this.gaussianOrderMedians;

        // Create an instance of Maximization
        Maximization max = new Maximization();

        // Initial estimate of lambdaOne
        double[] start = {1.0};

        // Initial step size in maximization search
        double[] step = {0.3};

        // Tolerance for maximization search termination
        double maxzTol = 1e-9;

        // Maximiztaion of the Gaussian probabilty plot correlation coefficient varying lambdaOne
        max.nelderMead(bcf, start, step, maxzTol);

        // coeff[0] = value of lambdaOne for a maximum Gaussian probabilty plot correlation coefficient
        double[] coeff = max.getParamValues();
        double lambda1 = coeff[0];

        //maximum Gaussian probabilty plot correlation coefficient
        double sampleR1 = max.getMaximum();

        // Repeat maximization starting equidistant from the final value of lambdaOne on the opposite side from the starting estimate
        start[0] = lambda1 - (start[0] - lambda1);
        max.nelderMead(bcf, start, step, maxzTol);
        coeff = max.getParamValues();
        this.lambdaOne = coeff[0];
        this.transformedSampleR =  max.getMaximum();

        // Choose solution with the largest Gaussian probabilty plot correlation coefficient
        if(sampleR1>this.transformedSampleR){
            this.transformedSampleR = sampleR1;
            this.lambdaOne = lambda1;
View Full Code Here

        bcf.nData = this.nData;
        bcf.yTransform = new double[this.nData];
        bcf.gaussianOrderMedians = this.gaussianOrderMedians;

        // Create an instance of Maximization
        Maximization max = new Maximization();

        // Initial estimate of lambdaOne
        double[] start = {1.0};

        // Initial step size in maximization search
        double[] step = {0.3};

        // Tolerance for maximization search termination
        double maxzTol = 1e-9;

        // Maximiztaion of the Gaussian probabilty plot correlation coefficient varying lambdaOne
        max.nelderMead(bcf, start, step, maxzTol);

        // coeff[0] = value of lambdaOne for a maximum Gaussian probabilty plot correlation coefficient
        double[] coeff = max.getParamValues();
        double lambda1 = coeff[0];

        //maximum Gaussian probabilty plot correlation coefficient
        double sampleR1 = max.getMaximum();

        // Repeat maximization starting equidistant from the final value of lambdaOne on the opposite side from the starting estimate
        start[0] = lambda1 - (start[0] - lambda1);
        max.nelderMead(bcf, start, step, maxzTol);
        coeff = max.getParamValues();
        this.lambdaOne = coeff[0];
        this.transformedSampleR =  max.getMaximum();

        // Choose solution with the largest Gaussian probabilty plot correlation coefficient
        if(sampleR1>this.transformedSampleR){
            this.transformedSampleR = sampleR1;
            this.lambdaOne = lambda1;
View Full Code Here

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