Example usage for org.apache.commons.math.stat.inference OneWayAnovaImpl OneWayAnovaImpl

Introduction

In this page you can find the example usage for org.apache.commons.math.stat.inference OneWayAnovaImpl OneWayAnovaImpl.

Prototype

public OneWayAnovaImpl() 

Source Link

Document

Default constructor.

Usage

From source file:geogebra.kernel.statistics.AlgoANOVA.java

protected final void compute() {

    int size = geoList.size();
    //System.out.println(geoList.toOutputValueString());
    // exit if less than two data lists
    if (size < 2) {
        result.setUndefined();// w  w  w .j  a  v  a  2s  . c  o  m
        return;
    }

    // exit if data lists are not defined or have less than two values
    for (int index = 0; index < size; index++) {

        if (!geoList.get(index).isDefined() || !geoList.get(index).isGeoList()
                || ((GeoList) geoList.get(index)).size() < 2) {
            result.setUndefined();
            return;
        }
    }

    // create an array list of data arrays
    if (categoryData == null) {
        categoryData = new ArrayList<double[]>();
    } else {
        categoryData.clear();
    }

    // load the data arrays from the input GeoList
    GeoList list;
    for (int index = 0; index < size; index++) {

        list = (GeoList) geoList.get(index);
        double[] val = new double[list.size()];

        for (int i = 0; i < list.size(); i++) {
            GeoElement geo = list.get(i);
            if (geo.isNumberValue()) {
                NumberValue num = (NumberValue) geo;
                val[i] = num.getDouble();
            } else {
                result.setUndefined();
                return;
            }
        }
        categoryData.add(val);
    }

    try {

        // get the test statistic and p value
        if (anovaImpl == null)
            anovaImpl = new OneWayAnovaImpl();
        p = anovaImpl.anovaPValue(categoryData);
        testStat = anovaImpl.anovaFValue(categoryData);

        // put these results into the output list
        result.clear();
        result.add(new GeoNumeric(cons, p));
        result.add(new GeoNumeric(cons, testStat));

    } catch (Exception e) {
        result.setUndefined();
        e.printStackTrace();
    }

}

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From source file:geogebra.common.kernel.statistics.AlgoANOVA.java

@Override
public final void compute() {

    int size = geoList.size();
    // System.out.println(geoList.toOutputValueString());
    // exit if less than two data lists
    if (size < 2) {
        result.setUndefined();/*from  ww w . jav  a 2s  .  c  o m*/
        return;
    }

    // exit if data lists are not defined or have less than two values
    for (int index = 0; index < size; index++) {

        if (!geoList.get(index).isDefined() || !geoList.get(index).isGeoList()
                || ((GeoList) geoList.get(index)).size() < 2) {
            result.setUndefined();
            return;
        }
    }

    // create an array list of data arrays
    if (categoryData == null) {
        categoryData = new ArrayList<double[]>();
    } else {
        categoryData.clear();
    }

    // load the data arrays from the input GeoList
    GeoList list;
    for (int index = 0; index < size; index++) {

        list = (GeoList) geoList.get(index);
        double[] val = new double[list.size()];

        for (int i = 0; i < list.size(); i++) {
            GeoElement geo = list.get(i);
            if (geo instanceof NumberValue) {
                NumberValue num = (NumberValue) geo;
                val[i] = num.getDouble();
            } else {
                result.setUndefined();
                return;
            }
        }
        categoryData.add(val);
    }

    try {

        // get the test statistic and p value
        if (anovaImpl == null)
            anovaImpl = new OneWayAnovaImpl();
        p = anovaImpl.anovaPValue(categoryData);
        testStat = anovaImpl.anovaFValue(categoryData);

        // put these results into the output list
        result.clear();
        result.add(new GeoNumeric(cons, p));
        result.add(new GeoNumeric(cons, testStat));

    } catch (Exception e) {
        result.setUndefined();
        e.printStackTrace();
    }

}

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From source file:org.rascalmpl.library.analysis.statistics.Inferences.java

public IValue anovaFValue(IList categoryData) {
    try {/*from  www  . ja  v a 2  s.c  om*/
        return values.real(new OneWayAnovaImpl().anovaFValue(makeAnova(categoryData)));
    } catch (IllegalArgumentException e) {
        throw RuntimeExceptionFactory.illegalArgument(categoryData, null, null, e.getMessage());
    } catch (MathException e) {
        throw RuntimeExceptionFactory.illegalArgument(categoryData, null, null, e.getMessage());
    }
}

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From source file:org.rascalmpl.library.analysis.statistics.Inferences.java

public IValue anovaPValue(IList categoryData) {
    try {//ww  w. j  ava 2  s  .  c om
        return values.real(new OneWayAnovaImpl().anovaPValue(makeAnova(categoryData)));
    } catch (IllegalArgumentException e) {
        throw RuntimeExceptionFactory.illegalArgument(categoryData, null, null, e.getMessage());
    } catch (MathException e) {
        throw RuntimeExceptionFactory.illegalArgument(categoryData, null, null, e.getMessage());
    }
}

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From source file:org.rascalmpl.library.analysis.statistics.Inferences.java

public IValue anovaTest(IList categoryData, INumber alpha) {
    try {//from   w  ww.  ja  va2 s  .co m
        return values
                .bool(new OneWayAnovaImpl().anovaTest(makeAnova(categoryData), alpha.toReal().doubleValue()));
    } catch (IllegalArgumentException e) {
        throw RuntimeExceptionFactory.illegalArgument(categoryData, null, null, e.getMessage());
    } catch (MathException e) {
        throw RuntimeExceptionFactory.illegalArgument(categoryData, null, null, e.getMessage());
    }
}

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From source file:rs.fon.whibo.GDT.component.removeInsignificantAttributes.FTestNumerical.java

public LinkedList<Attribute> removeAttributes(ExampleSet exampleSet,
        LinkedList<Attribute> attributesForSplitting) {
    // checks if the example set is pure, and if it is, it exits the method
    Attribute label = exampleSet.getAttributes().getLabel();
    if (Tools.getAllCategories(exampleSet, label).size() < 2)
        return attributesForSplitting;

    // selects the attributes to be evaluated for removal (by calculating
    // F-test probability for each attribute)
    ArrayList<Attribute> attributesToRemove = new ArrayList<Attribute>();
    ArrayList<Double> attributeProbabilities = new ArrayList<Double>();
    for (Attribute attr : attributesForSplitting)
        if (attr.isNumerical()) {
            // calculate F-test probability of the attribute
            double probability = 0;
            try {

                OneWayAnova fTest = new OneWayAnovaImpl();
                List<double[]> paramForFTest = getArraysByLabel(exampleSet, attr);

                // tests if no arrays for f-test has fewer that 2 elements
                boolean fTestImpossible = false;
                for (double[] i : paramForFTest)
                    if (i.length < 2)
                        fTestImpossible = true;

                // calculates ftest probability
                if (!fTestImpossible)
                    probability = fTest.anovaPValue(paramForFTest);

            } catch (Exception e) {
                // System.out.println("Error in calculating math formula (FTest)");
            }/*  ww  w  .  j ava 2s . com*/
            // add the attribute to the list
            attributesToRemove.add(attr);
            attributeProbabilities.add(new Double(probability));
        }

    if (attributesToRemove.size() == 0)
        return attributesForSplitting;

    // calculates the percentile of the required percentage. Percentile
    // variable in code represents the percentage of attributes to be kept
    // (not removed)
    double percentile;
    DescriptiveStatistics stat = new DescriptiveStatistics();
    for (Double d : attributeProbabilities)
        stat.addValue(d.doubleValue());
    percentile = stat.getPercentile((1 - Percentage_Remove) * 100);

    // evaluates attributes and chooses the ones for removal (actually saves
    // the ones not for removal)
    Iterator<Attribute> iattr = attributesToRemove.iterator();
    Iterator<Double> iprob = attributeProbabilities.iterator();
    while (iattr.hasNext()) {
        iattr.next();
        Double prob = iprob.next();
        if (Use_Percentage_Instead == 0) {
            if (prob <= Alpha_Value) {
                iattr.remove();
                iprob.remove();
            }
        } else {
            if (prob <= percentile) {
                iattr.remove();
                iprob.remove();
            }
        }
    }

    // removes the attributes
    for (Attribute attr : attributesToRemove)
        attributesForSplitting.remove(attr);
    return attributesForSplitting;

}

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