Example usage for org.apache.commons.math3.stat.correlation SpearmansCorrelation correlation

List of usage examples for org.apache.commons.math3.stat.correlation SpearmansCorrelation correlation

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Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.correlation SpearmansCorrelation correlation.

Prototype

public double correlation(final double[] xArray, final double[] yArray)

Source Link

Document

Computes the Spearman's rank correlation coefficient between the two arrays.

Usage

From source file:edu.illinois.cs.cogcomp.saul.test.TestReal.java

public TestReal(Learner classifier, Classifier oracle, Parser parser) {
    int examples = 0;
    double totalDifference = 0;
    double[] actuals = {};
    double[] predictions = {};

    classifier.write(System.out);

    for (Object example = parser.next(); example != null; example = parser.next()) {
        double prediction = classifier.realValue(example);

        predictions = Arrays.copyOf(predictions, predictions.length + 1);
        predictions[predictions.length - 1] = prediction;

        double value = oracle.realValue(example);

        actuals = Arrays.copyOf(actuals, actuals.length + 1);
        actuals[actuals.length - 1] = value;

        double difference = Math.abs(prediction - value);
        totalDifference += difference;//  w  w  w  .  j  av  a2s . com
        classifier.classify(example);
        ++examples;

        System.out.println(
                "Example " + examples + " difference: " + difference + " (prediction: " + prediction + ")");
    }
    System.out.println("test examples number: " + examples);
    double avg = totalDifference / examples;
    System.out.println("Average difference: " + avg);
    double p = getPearsonCorrelation(predictions, actuals);
    System.out.println("Pearson correlation:" + p);
    SpearmansCorrelation e = new SpearmansCorrelation();
    double sp = e.correlation(predictions, actuals);
    System.out.println("Spearman correlation:" + sp);

}

From source file:eu.eexcess.diversityasurement.CorrelationTest.java

@Test
public void spearmansRho_givenAKartesianQuadrandIIandIVSymmetralePrallelLine() {
    CorrelationValues c = newCorrelationValue2();
    SpearmansCorrelation sCorrelation = new SpearmansCorrelation();
    System.out.println("spearman correlation r(x, y)=" + sCorrelation.correlation(c.x, c.y));
    assertEquals(sCorrelation.correlation(c.x, c.y), c.spearmanRho, c.epsilon);
}

From source file:eu.eexcess.diversityasurement.CorrelationTest.java

@Test
public void spearmansRho_givenAKartesianQuadrandIandIIISymmetraleParallelLine() {
    CorrelationValues c = newCorrelationValue3();
    SpearmansCorrelation sCorrelation = new SpearmansCorrelation();
    System.out.println("spearman correlation r(x, y)=" + sCorrelation.correlation(c.x, c.y));
    assertEquals(sCorrelation.correlation(c.x, c.y), c.spearmanRho, c.epsilon);
}

From source file:eu.eexcess.diversityasurement.CorrelationTest.java

@Test
public void spearmansRho_givenPosXAxis() {
    CorrelationValues c = newCorrelationValue4();
    SpearmansCorrelation sCorrelation = new SpearmansCorrelation();
    System.out.println("spearman correlation r(x, y)=" + sCorrelation.correlation(c.x, c.y));
    assertTrue(Double.isNaN(sCorrelation.correlation(c.x, c.y)));
}

From source file:eu.eexcess.diversityasurement.CorrelationTest.java

@Test
public void spearmansRho_givenDefaultValues_expectCorrectResult() {
    CorrelationValues c = newCorrelationValue1();
    SpearmansCorrelation sCorrelation = new SpearmansCorrelation();
    PearsonsCorrelation pCorrelatoin = new PearsonsCorrelation();

    System.out.println("spearman correlation r(x, y)=" + sCorrelation.correlation(c.x, c.y));
    assertEquals(sCorrelation.correlation(c.x, c.y), c.spearmanRho, c.epsilon);
    System.out.println("pearson correlation  r(x, y)=" + pCorrelatoin.correlation(c.x, c.y));
    assertEquals(sCorrelation.correlation(c.y, c.x), c.spearmanRho, c.epsilon);
}

From source file:com.thoughtworks.studios.journey.models.ActionCorrelationCalculation.java

public List<CorrelationResult> calculate() {
    Map<String, List<List<Integer>>> data = rawData();
    List<CorrelationResult> results = new ArrayList<>(data.size());

    for (String action : data.keySet()) {
        SpearmansCorrelation correlation = new SpearmansCorrelation();

        List<List<Integer>> variables = data.get(action);
        double[] x = toDoubleArray(variables.get(0));
        double[] y = toDoubleArray(variables.get(1));

        double r = correlation.correlation(x, y);
        TDistribution tDistribution = new TDistribution(x.length - 2);
        double t = FastMath.abs(r * FastMath.sqrt((x.length - 2) / (1 - r * r)));
        double pValue = 2 * tDistribution.cumulativeProbability(-t);

        SummaryStatistics successSt = new SummaryStatistics();
        SummaryStatistics failSt = new SummaryStatistics();
        for (int i = 0; i < x.length; i++) {
            if (y[i] == 1) {
                successSt.addValue(x[i]);
            } else {
                failSt.addValue(x[i]);/*from  w ww .  j a  v a 2  s  . c o  m*/
            }
        }

        results.add(new CorrelationResult(action, r, pValue, successSt, failSt));
    }

    Collections.sort(results, new Comparator<CorrelationResult>() {
        @Override
        public int compare(CorrelationResult r1, CorrelationResult r2) {
            Double abs1 = Math.abs(r2.getCorrelation());
            Double abs2 = Math.abs(r1.getCorrelation());
            return abs1.compareTo(abs2);
        }
    });
    return results;
}

From source file:hms.hwestra.interactionrebuttal.InteractionRebuttal.java

public void spearmanSE(String normal1, String robust1) throws IOException {
    HashMap<String, Double> eQTLToSE = loadSE(normal1);
    HashMap<String, Double> eQTLToSERobust = loadSE(robust1);

    Set<String> eqtls = eQTLToSE.keySet();
    ArrayList<Double> x = new ArrayList<Double>();
    ArrayList<Double> y = new ArrayList<Double>();
    for (String s : eqtls) {

        x.add(eQTLToSE.get(s));//from w w w. ja va2  s  .  c  o  m
        y.add(eQTLToSERobust.get(s));

    }

    SpearmansCorrelation corr = new SpearmansCorrelation();
    double c = corr.correlation(Primitives.toPrimitiveArr(x.toArray(new Double[0])),
            Primitives.toPrimitiveArr(y.toArray(new Double[0])));
    System.out.println(c);
}

From source file:com.medlog.webservice.vo.DiaryAnalysisSummaryVO.java

private void populateCorrelation() {
    SpearmansCorrelation c = new SpearmansCorrelation();

    getCorr()[IDX_MOOD] = c.correlation(getMood(), getMood());
    getCorr()[IDX_AGREEABLENESS_BIG5] = c.correlation(getMood(), getAgreeablenessBig5());
    getCorr()[IDX_MOOD] = c.correlation(getMood(), getMood());
    getCorr()[IDX_AGREEABLENESS_BIG5] = c.correlation(getMood(), getAgreeablenessBig5());
    getCorr()[IDX_ANALYTICAL] = c.correlation(getMood(), getAnalytical());
    getCorr()[IDX_ANGER] = c.correlation(getMood(), getAnger());
    getCorr()[IDX_CONFIDENT] = c.correlation(getMood(), getConfident());
    getCorr()[IDX_CONSCIENTIOUSNESS_BIG5] = c.correlation(getMood(), getConscientiousnessBig5());
    getCorr()[IDX_DISGUST] = c.correlation(getMood(), getDisgust());
    getCorr()[IDX_EMOTIONALRANGE_BIG5] = c.correlation(getMood(), getEmotionalRangeBig5());
    getCorr()[IDX_EXTRAVERSION_BIG5] = c.correlation(getMood(), getExtraversionBig5());
    getCorr()[IDX_FEAR] = c.correlation(getMood(), getFear());
    getCorr()[IDX_JOY] = c.correlation(getMood(), getJoy());
    getCorr()[IDX_OPENNESS_BIG5] = c.correlation(getMood(), getOpennessBig5());
    getCorr()[IDX_SADNESS] = c.correlation(getMood(), getSadness());
    getCorr()[IDX_TENTATIVE] = c.correlation(getMood(), getTentative());
    double corrTemp;
    for (int k = 0; k < getCorr().length; k++) {
        corrTemp = Math.pow(getCorr()[k], 2);
        if (StrUtl.matchOR(k, IDX_ANGER, IDX_DISGUST, IDX_FEAR, IDX_SADNESS)) {
            corrTemp = .99 - corrTemp;//w w  w  .j a v a  2 s .  c  om
        }
        getrSquared()[k] = corrTemp;
    }
    double q3 = org.apache.commons.math3.stat.StatUtils.percentile(getrSquared(), .75);

    double q1 = org.apache.commons.math3.stat.StatUtils.percentile(getrSquared(), .25);
    double var = StatUtils.variance(getrSquared());
    System.out.println("q1" + q1);
    System.out.println("q3" + q3);
    System.out.println("var" + var);
    double max = StatUtils.max(getrSquared());

    double min = StatUtils.max(getrSquared());

    setSum(StatUtils.sum(getrSquared()) - 1.0);
    System.out.println("sum" + getSum());
    for (int j = 1; j < 14; j++) {
        getCorrRanked()[j] = getrSquared()[j] / getSum();
    }
    System.out.println("pmf sum" + StatUtils.sum(getCorrRanked()));
    double[] cRCopy = ArrayUtils.clone(getCorrRanked());
    Arrays.sort(cRCopy);
    ArrayUtils.reverse(cRCopy);
    setToneList(new ArrayList<ToneKeyValuePair>());
    for (int j = 1; j < 14; j++) {
        ArrayUtils.indexOf(cRCopy, getCorrRanked()[j]);
        //ToneKeyValuePair t = ToneKeyValuePair.builder().key(CORR_STR[j]).value(getrSquared()[j]).weightedValue(getCorrRanked()[j]).rank(ArrayUtils.indexOf(cRCopy, getCorrRanked()[j])).build();
        getToneList().add(ToneKeyValuePair.builder().key(CORR_STR[j]).value(getrSquared()[j])
                .weightedValue(getCorrRanked()[j]).rank(ArrayUtils.indexOf(cRCopy, getCorrRanked()[j]) + 1)
                .historicalRawAvg(getAverageScores()[j]).build());
        //            corrRanked[j] = rSquared[j] / sum;
    }

    double guess = 0;

    for (ToneKeyValuePair t : getToneList()) {
        guess += (t.getWeightedValue() * 10 * getValFromKeyPct(t.getKey()));//t.getWeightedValue());

        System.out.println(t.toString());
        System.out.println("com.medlog.webservice.vo.DiaryAnalysisSummaryVO.populateCorrelation() Guess with "
                + t.getRank() + "== " + guess);
    }
    SimpleRegression sg = new SimpleRegression(false);
    populateLineGuessPoints(sg);

    getGuesses()[1] = sg.predict(getToneCurrentAvgX());
    System.out
            .println("\n\n\ncom.medlog.webservice.vo.DiaryAnalysisSummaryVO.populateCorrelation() GUESS === >");

    System.out.printf("Weighted (history) Guess ------> %.3f%n", (guess));
    System.out.printf("Best fit line Guess -----------> %.3f%n", sg.predict(getToneCurrentAvgX()));
    guess /= 10;
    guess = Math.round(guess);
    System.out.printf("Weighted (history) Guess adj.-----> %.3f%n", (guess));
    System.out.println("-------------------------------------------\n");
    getGuesses()[0] = guess;
    getLineEq()[SLOPE] = sg.getSlope();
    getLineEq()[YINT] = sg.getIntercept();
    getLineEq()[R] = sg.getRSquare();
    double[] norm = StatUtils.normalize(getCorrRanked());

    if (DEBUG) {
        System.out.println("com.medlog.webservice.vo.DiaryAnalysisSummaryVO.populateCorrelation()"
                + Arrays.toString(norm));
    }
    getFiveSummary()[0] = StatUtils.min(norm);
    getFiveSummary()[1] = StatUtils.percentile(norm, 25);

    getFiveSummary()[2] = StatUtils.percentile(norm, 50);

    getFiveSummary()[3] = StatUtils.percentile(norm, 75);

    getFiveSummary()[4] = StatUtils.max(norm);

}

From source file:mase.generic.WeightedClusterSCPostEval.java

private HashMap<Integer, Double> stateCorrelations(EvolutionState state) {
    SpearmansCorrelation spearman = new SpearmansCorrelation();
    HashMap<Integer, Double> pointWeight = new HashMap<Integer, Double>(buffer.size() * 2);
    double[] fitScores = new double[super.currentPop.size()];
    int index = 0;
    for (Subpopulation sub : state.population.subpops) {
        for (Individual ind : sub.individuals) {
            NoveltyFitness nf = (NoveltyFitness) ind.fitness;
            fitScores[index++] = nf.getFitnessScore();
        }/*from   www.  j  a va 2 s. c om*/
    }
    for (Integer k : buffer.keySet()) {
        double[] cs = new double[super.currentPop.size()];
        for (int i = 0; i < super.currentPop.size(); i++) {
            Float count = currentPop.get(i).getCounts().get(k);
            cs[i] = count == null ? 0 : count;
        }
        double c = Math.abs(spearman.correlation(fitScores, cs));
        pointWeight.put(k, c);
    }
    return pointWeight;
}

From source file:hms.hwestra.interactionrebuttal2.InteractionRebuttal2.java

private void determineInteractionPvalueAndMerge(String normal1, String robust1, String out) throws IOException {

    InteractionRebuttal a = new InteractionRebuttal();
    HashMap<String, Double> normalSE = a.loadSE(normal1);
    HashMap<String, Double> robustSE = a.loadSE(robust1);

    HashMap<String, Double> normalZ = loadZ(normal1);
    HashMap<String, Double> robustZ = loadZ(robust1);

    Set<String> eqtls = normalZ.keySet();
    TextFile tf = new TextFile(out, TextFile.W);
    SpearmansCorrelation corr = new SpearmansCorrelation();
    tf.writeln("SNP-Probe\tZNorm\tZRobust\tPNorm\tPRobust\tSENorm\tSERobust");
    ArrayList<Double> x = new ArrayList<Double>();
    ArrayList<Double> y = new ArrayList<Double>();
    ArrayList<Double> x2 = new ArrayList<Double>();
    ArrayList<Double> y2 = new ArrayList<Double>();

    ArrayList<Double> x3 = new ArrayList<Double>();
    ArrayList<Double> y3 = new ArrayList<Double>();
    for (String s : eqtls) {
        if (robustZ.containsKey(s)) {
            if (!Double.isNaN(normalZ.get(s))) {
                x.add(normalZ.get(s));/*  w  w  w.  j ava2s  .c o  m*/
                y.add(robustZ.get(s));
                x2.add(-Math.log10(ZScores.zToP(normalZ.get(s))));
                y2.add(-Math.log10(ZScores.zToP(robustZ.get(s))));
                x3.add(normalSE.get(s));
                y3.add(robustSE.get(s));
            }
            String ln = s + "\t" + normalZ.get(s) + "\t" + robustZ.get(s) + "\t"
                    + -Math.log10(ZScores.zToP(normalZ.get(s))) + "\t"
                    + -Math.log10(ZScores.zToP(robustZ.get(s))) + "\t" + normalSE.get(s) + "\t"
                    + robustSE.get(s);
            tf.writeln(ln);
        }
    }
    tf.close();
    System.out.println(x.size());
    double c = corr.correlation(Primitives.toPrimitiveArr(x.toArray(new Double[0])),
            Primitives.toPrimitiveArr(y.toArray(new Double[0])));
    double c2 = corr.correlation(Primitives.toPrimitiveArr(x2.toArray(new Double[0])),
            Primitives.toPrimitiveArr(y2.toArray(new Double[0])));

    double c3 = Correlation.correlate(Primitives.toPrimitiveArr(x.toArray(new Double[0])),
            Primitives.toPrimitiveArr(y.toArray(new Double[0])));
    double c4 = Correlation.correlate(Primitives.toPrimitiveArr(x2.toArray(new Double[0])),
            Primitives.toPrimitiveArr(y2.toArray(new Double[0])));

    double c5 = corr.correlation(Primitives.toPrimitiveArr(x3.toArray(new Double[0])),
            Primitives.toPrimitiveArr(y3.toArray(new Double[0])));
    double c6 = Correlation.correlate(Primitives.toPrimitiveArr(x3.toArray(new Double[0])),
            Primitives.toPrimitiveArr(y3.toArray(new Double[0])));

    System.out.println("Z Spearman: " + c + " Pearson: " + c3);
    System.out.println("P Spearman: " + c2 + " Pearson: " + c4);
    System.out.println("SE Spearman: " + c5 + " Pearson: " + c6);
}