Example usage for org.apache.commons.math.stat.regression OLSMultipleLinearRegression setNoIntercept

Introduction

In this page you can find the example usage for org.apache.commons.math.stat.regression OLSMultipleLinearRegression setNoIntercept.

Prototype

public void setNoIntercept(boolean noIntercept) 

Source Link

Usage

From source file:ca.uwaterloo.iss4e.algorithm.PARX.java

public static double[] computePARModel(final List<Double> readings, final List<Double> temperatures,
        int currentIndex, int trainingSize, int order, int seasons) throws SMASException {
    Pair pair = PARX.prepareAllVariables(readings, temperatures, currentIndex, trainingSize, order, seasons);
    double[] Y = (double[]) pair.getKey();
    double[][] X = (double[][]) pair.getValue();

    OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
    regression.setNoIntercept(true);
    regression.newSampleData(Y, X);//  w ww .j a  va2  s.  c  om

    double[] beta = regression.estimateRegressionParameters();
    return beta;
}

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From source file:ca.uwaterloo.iss4e.algorithm.PARX.java

public static double[] getLoadByTemperature(final List<Double> readings, final List<Double> temperatures,
        int order) throws SMASException {
    int seasons = 24;
    Pair pair = PARX.prepareAllVariables(readings, temperatures, readings.size() - 1, readings.size(), order,
            seasons);/*from   w w  w. j  a v a2  s . co  m*/
    double[] Y = (double[]) pair.getKey();
    double[][] X = (double[][]) pair.getValue();

    OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
    regression.setNoIntercept(true);
    regression.newSampleData(Y, X);

    double[] beta = regression.estimateRegressionParameters();
    double[] loadByTemp = new double[Y.length * seasons];
    int startIndex = readings.size() - Y.length * seasons;
    for (int i = 0; i < loadByTemp.length; ++i) {
        double t = temperatures.get(i + startIndex);
        loadByTemp[i] = beta[order + 0] * (t > 20 ? (t - 20) : 0)
                + beta[order + 1] * ((5 <= t && t < 16) ? (16 - t) : 0)
                + beta[order + 2] * (t < 5 ? (5 - t) : 0);
    }
    return loadByTemp;
}

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From source file:dfs.OLSTrendLine.java

@Override
public void setValues(double[] y, double[] x) {
    if (x.length != y.length) {
        throw new IllegalArgumentException(
                String.format("The numbers of y and x values must be equal (%d != %d)", y.length, x.length));
    }//www  .  j  a va  2 s .  c o  m
    double[][] xData = new double[x.length][];
    for (int i = 0; i < x.length; i++) {
        // the implementation determines how to produce a vector of predictors from a single x
        xData[i] = xVector(x[i]);
    }
    if (logY()) { // in some models we are predicting ln y, so we replace each y with ln y
        y = Arrays.copyOf(y, y.length); // user might not be finished with the array we were given
        for (int i = 0; i < x.length; i++) {
            y[i] = Math.log(y[i]);
        }
    }
    OLSMultipleLinearRegression ols = new OLSMultipleLinearRegression();
    ols.setNoIntercept(true); // let the implementation include a constant in xVector if desired
    ols.newSampleData(y, xData); // provide the data to the model
    coef = MatrixUtils.createColumnRealMatrix(ols.estimateRegressionParameters()); // get our coefs
}

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