Example usage for org.apache.commons.math3.stat.regression SimpleRegression getMeanSquareError

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.regression SimpleRegression getMeanSquareError.

Prototype

public double getMeanSquareError()

Source Link

Document

Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE.

Usage

From source file:de.qaware.chronix.timeseries.RegressionTest.java

@Test
public void testRegression() {

    SimpleRegression regression = new SimpleRegression();
    regression.addData(0.0, 1.0);/* www .  ja  v a2 s. c  om*/
    regression.addData(1.0, 2.5);
    regression.addData(2.0, 3.0);

    double slope = regression.getSlope();
    double intercept = regression.getIntercept();
    long n = regression.getN();
    double err = regression.getMeanSquareError();
}

From source file:eu.tango.energymodeller.energypredictor.CpuOnlyEnergyPredictor.java

/**
 * This calculates the mathematical function that predicts the power
 * consumption given the cpu utilisation.
 *
 * @param host The host to get the function for
 * @return The mathematical function that predicts the power consumption
 * given the cpu utilisation.//from   www  . j  a  v a2  s . c  o m
 */
private PredictorFunction<LinearFunction> retrieveModel(Host host) {
    if (modelCache.containsKey(host)) {
        /**
         * A small cache avoids recalculating the regression so often.
         */
        return modelCache.get(host);
    }
    LinearFunction model = new LinearFunction();
    SimpleRegression regressor = new SimpleRegression(true);
    for (HostEnergyCalibrationData data : host.getCalibrationData()) {
        regressor.addData(data.getCpuUsage(), data.getWattsUsed());
    }
    model.setIntercept(regressor.getIntercept());
    model.setCoefficient(regressor.getSlope());
    PredictorFunction<LinearFunction> answer = new PredictorFunction<>(model, regressor.getSumSquaredErrors(),
            Math.sqrt(regressor.getMeanSquareError()));
    modelCache.put(host, answer);
    return answer;
}

From source file:org.apache.solr.client.solrj.io.eval.RegressionEvaluator.java

@Override
public Object doWork(Object first, Object second) throws IOException {
    if (null == first) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - null found for the first value", toExpression(constructingFactory)));
    }//from  w ww. java  2 s  .  co m
    if (null == second) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - null found for the second value", toExpression(constructingFactory)));
    }
    if (!(first instanceof List<?>)) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - found type %s for the first value, expecting a list of numbers",
                toExpression(constructingFactory), first.getClass().getSimpleName()));
    }
    if (!(second instanceof List<?>)) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - found type %s for the second value, expecting a list of numbers",
                toExpression(constructingFactory), first.getClass().getSimpleName()));
    }

    List<?> l1 = (List<?>) first;
    List<?> l2 = (List<?>) second;

    if (l2.size() < l1.size()) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - first list (%d) has more values than the second list (%d)",
                toExpression(constructingFactory), l1.size(), l2.size()));
    }

    SimpleRegression regression = new SimpleRegression();
    for (int idx = 0; idx < l1.size(); ++idx) {
        regression.addData(((BigDecimal) l1.get(idx)).doubleValue(), ((BigDecimal) l2.get(idx)).doubleValue());
    }

    Map<String, Number> map = new HashMap<>();
    map.put("slope", regression.getSlope());
    map.put("intercept", regression.getIntercept());
    map.put("R", regression.getR());
    map.put("N", regression.getN());
    map.put("RSquare", regression.getRSquare());
    map.put("regressionSumSquares", regression.getRegressionSumSquares());
    map.put("slopeConfidenceInterval", regression.getSlopeConfidenceInterval());
    map.put("interceptStdErr", regression.getInterceptStdErr());
    map.put("totalSumSquares", regression.getTotalSumSquares());
    map.put("significance", regression.getSignificance());
    map.put("meanSquareError", regression.getMeanSquareError());

    return new RegressionTuple(regression, map);
}

From source file:org.apache.solr.client.solrj.io.stream.RegressionEvaluator.java

public Tuple evaluate(Tuple tuple) throws IOException {

    if (subEvaluators.size() != 2) {
        throw new IOException("Regress expects 2 columns as parameters");
    }/*ww  w.  j a  v a 2  s  . c o  m*/

    StreamEvaluator colEval1 = subEvaluators.get(0);
    StreamEvaluator colEval2 = subEvaluators.get(1);

    List<Number> numbers1 = (List<Number>) colEval1.evaluate(tuple);
    List<Number> numbers2 = (List<Number>) colEval2.evaluate(tuple);
    double[] column1 = new double[numbers1.size()];
    double[] column2 = new double[numbers2.size()];

    for (int i = 0; i < numbers1.size(); i++) {
        column1[i] = numbers1.get(i).doubleValue();
    }

    for (int i = 0; i < numbers2.size(); i++) {
        column2[i] = numbers2.get(i).doubleValue();
    }

    SimpleRegression regression = new SimpleRegression();
    for (int i = 0; i < column1.length; i++) {
        regression.addData(column1[i], column2[i]);
    }

    Map map = new HashMap();
    map.put("slope", regression.getSlope());
    map.put("intercept", regression.getIntercept());
    map.put("R", regression.getR());
    map.put("N", regression.getN());
    map.put("regressionSumSquares", regression.getRegressionSumSquares());
    map.put("slopeConfidenceInterval", regression.getSlopeConfidenceInterval());
    map.put("interceptStdErr", regression.getInterceptStdErr());
    map.put("totalSumSquares", regression.getTotalSumSquares());
    map.put("significance", regression.getSignificance());
    map.put("meanSquareError", regression.getMeanSquareError());
    return new RegressionTuple(regression, map);
}