Examples of org.apache.commons.math3.stat.regression.SimpleRegression.addData()

Package org.apache.commons.math3.stat.regression

Class org.apache.commons.math3.stat.regression.SimpleRegression

Examples of org.apache.commons.math3.stat.regression.SimpleRegression.addData()

org.apache.commons.math3.stat.regression.SimpleRegression.addData()
Adds the observation (x,y) to the regression data set.
Uses updating formulas for means and sums of squares defined in "Algorithms for Computing the Sample Variance: Analysis and Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J. 1983, American Statistician, vol. 37, pp. 242-247, referenced in Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985.
@param x independent variable value @param y dependent variable value

        } else if (xArray.length < 2) {
            throw new MathIllegalArgumentException(LocalizedFormats.INSUFFICIENT_DIMENSION,
                                                   xArray.length, 2);
        } else {
            for(int i=0; i<xArray.length; i++) {
                regression.addData(xArray[i], yArray[i]);
            }
            return regression.getR();
        }
    }

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     * class.
     */
    public double[] solve() {
        final SimpleRegression regress = new SimpleRegression(true);
        for (double[] d : points) {
            regress.addData(d[0], d[1]);
        }


        final double[] result = { regress.getSlope(), regress.getIntercept() };
        return result;
    }

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     * class.
     */
    public double[] solve() {
        final SimpleRegression regress = new SimpleRegression(true);
        for (double[] d : points) {
            regress.addData(d[0], d[1]);
        }


        final double[] result = { regress.getSlope(), regress.getIntercept() };
        return result;
    }

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        } else if (xArray.length < 2) {
            throw new MathIllegalArgumentException(LocalizedFormats.INSUFFICIENT_DIMENSION,
                                                   xArray.length, 2);
        } else {
            for(int i=0; i<xArray.length; i++) {
                regression.addData(xArray[i], yArray[i]);
            }
            return regression.getR();
        }
    }

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     * class.
     */
    public double[] solve() {
        final SimpleRegression regress = new SimpleRegression(true);
        for (double[] d : points) {
            regress.addData(d[0], d[1]);
        }


        final double[] result = { regress.getSlope(), regress.getIntercept() };
        return result;
    }

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        } else if (xArray.length < 2) {
            throw new MathIllegalArgumentException(LocalizedFormats.INSUFFICIENT_DIMENSION,
                                                   xArray.length, 2);
        } else {
            for(int i=0; i<xArray.length; i++) {
                regression.addData(xArray[i], yArray[i]);
            }
            return regression.getR();
        }
    }

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        } else if (xArray.length < 2) {
            throw new MathIllegalArgumentException(LocalizedFormats.INSUFFICIENT_DIMENSION,
                                                   xArray.length, 2);
        } else {
            for(int i=0; i<xArray.length; i++) {
                regression.addData(xArray[i], yArray[i]);
            }
            return regression.getR();
        }
    }

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     * class.
     */
    public double[] solve() {
        final SimpleRegression regress = new SimpleRegression(true);
        for (double[] d : points) {
            regress.addData(d[0], d[1]);
        }


        final double[] result = { regress.getSlope(), regress.getIntercept() };
        return result;
    }

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     * class.
     */
    public double[] solve() {
        final SimpleRegression regress = new SimpleRegression(true);
        for (double[] d : points) {
            regress.addData(d[0], d[1]);
        }


        final double[] result = { regress.getSlope(), regress.getIntercept() };
        return result;
    }

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    for (int m=1; m<=41; m+=4) {
      
        double cost = getWorkerCost(m, priors, 1000);
        if (cost == 0) break;
        regression.addData(Math.log(cost), m);


    }
    
    double d = regression.predict(Math.log(costThreshold));

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