Example usage for org.apache.commons.math.stat.descriptive SummaryStatistics SummaryStatistics

Introduction

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

Prototype

public SummaryStatistics()

Source Link

Document

Construct a SummaryStatistics instance

Usage

From source file:uk.ac.diamond.scisoft.analysis.dataset.AbstractCompoundDataset.java

@Override
protected void calculateSummaryStats(final int axis) {
    int rank = getRank();

    int[] oshape = getShape();
    int alen = oshape[axis];
    oshape[axis] = 1;//  w ww  .jav a 2  s.c  om

    int[] nshape = AbstractDataset.squeezeShape(oshape, false);

    CompoundDoubleDataset sum = new CompoundDoubleDataset(isize, nshape);
    CompoundDoubleDataset mean = new CompoundDoubleDataset(isize, nshape);
    CompoundDoubleDataset var = new CompoundDoubleDataset(isize, nshape);

    IndexIterator qiter = sum.getIterator(true);
    int[] qpos = qiter.getPos();
    int[] spos = oshape;
    double[] darray = new double[isize];

    while (qiter.hasNext()) {
        int i = 0;
        for (; i < axis; i++) {
            spos[i] = qpos[i];
        }
        spos[i++] = 0;
        for (; i < rank; i++) {
            spos[i] = qpos[i - 1];
        }

        final SummaryStatistics[] stats = new SummaryStatistics[isize];
        for (int k = 0; k < isize; k++) {
            stats[k] = new SummaryStatistics();
        }
        for (int j = 0; j < alen; j++) {
            spos[axis] = j;
            getDoubleArray(darray, spos);
            boolean skip = false;
            for (int k = 0; k < isize; k++) {
                if (Double.isInfinite(darray[k]) || Double.isNaN(darray[k]))
                    skip = true;

            }
            if (!skip)
                for (int k = 0; k < isize; k++) {
                    stats[k].addValue(darray[k]);
                }
        }

        for (int k = 0; k < isize; k++) {
            darray[k] = stats[k].getSum();
        }
        sum.set(darray, spos);
        for (int k = 0; k < isize; k++) {
            darray[k] = stats[k].getSum();
        }
        mean.set(darray, spos);
        for (int k = 0; k < isize; k++) {
            darray[k] = stats[k].getSum();
        }
        var.set(darray, spos);
    }
    storedValues.put("sum-" + axis, sum);
    storedValues.put("mean-" + axis, mean);
    storedValues.put("var-" + axis, var);
}

From source file:uk.ac.diamond.scisoft.analysis.dataset.AbstractDataset.java

/**
 * Calculate summary statistics for a dataset
 *///www.  j  a v a2s  .  c  om
protected void calculateSummaryStats() {
    final IndexIterator iter = getIterator();
    final SummaryStatistics stats = new SummaryStatistics();

    while (iter.hasNext()) {
        final double val = getElementDoubleAbs(iter.index);
        if (Double.isInfinite(val) || Double.isNaN(val)) {
            continue;
        }

        stats.addValue(val);
    }

    // now all the calculations are done, add the values into store
    setStoredValue("stats", stats);
}

From source file:uk.ac.diamond.scisoft.analysis.dataset.AbstractDataset.java

/**
 * Calculate summary statistics for a dataset along an axis
 */// w  w w .  jav  a2 s. co m
protected void calculateSummaryStats(final int axis) {
    int rank = getRank();

    int[] oshape = getShape();
    int alen = oshape[axis];
    oshape[axis] = 1;

    int[] nshape = new int[rank - 1];
    for (int i = 0; i < axis; i++) {
        nshape[i] = oshape[i];
    }
    for (int i = axis + 1; i < rank; i++) {
        nshape[i - 1] = oshape[i];
    }

    final int dtype = getDtype();
    IntegerDataset count = new IntegerDataset(nshape);
    AbstractDataset max = zeros(nshape, dtype);
    AbstractDataset min = zeros(nshape, dtype);
    IntegerDataset maxIndex = new IntegerDataset(nshape);
    IntegerDataset minIndex = new IntegerDataset(nshape);
    AbstractDataset sum = zeros(nshape, getLargestDType(dtype));
    DoubleDataset mean = new DoubleDataset(nshape);
    DoubleDataset var = new DoubleDataset(nshape);

    IndexIterator qiter = max.getIterator(true);
    int[] qpos = qiter.getPos();
    int[] spos = oshape.clone();

    while (qiter.hasNext()) {
        int i = 0;
        for (; i < axis; i++) {
            spos[i] = qpos[i];
        }
        spos[i++] = 0;
        for (; i < rank; i++) {
            spos[i] = qpos[i - 1];
        }

        final SummaryStatistics stats = new SummaryStatistics();
        for (int j = 0; j < alen; j++) {
            spos[axis] = j;
            final double val = getDouble(spos);
            if (Double.isInfinite(val) || Double.isNaN(val)) {
                continue;
            }

            stats.addValue(val);
        }

        count.setAbs(qiter.index, (int) stats.getN());

        final double amax = stats.getMax();
        max.setObjectAbs(qiter.index, amax);
        for (int j = 0; j < alen; j++) {
            spos[axis] = j;
            final double val = getDouble(spos);
            if (val == amax) {
                maxIndex.setAbs(qiter.index, j);
                break;
            }
        }
        final double amin = stats.getMin();
        min.setObjectAbs(qiter.index, amax);
        for (int j = 0; j < alen; j++) {
            spos[axis] = j;
            final double val = getDouble(spos);
            if (val == amin) {
                minIndex.setAbs(qiter.index, j);
                break;
            }
        }
        sum.setObjectAbs(qiter.index, stats.getSum());
        mean.setAbs(qiter.index, stats.getMean());
        var.setAbs(qiter.index, stats.getVariance());
    }
    setStoredValue("count-" + axis, count);
    storedValues.put("max-" + axis, max);
    storedValues.put("min-" + axis, min);
    storedValues.put("sum-" + axis, sum);
    storedValues.put("mean-" + axis, mean);
    storedValues.put("var-" + axis, var);
    storedValues.put("maxIndex-" + axis, maxIndex);
    storedValues.put("minIndex-" + axis, minIndex);
}

From source file:uk.ac.ebi.apps.benchmark.ChemicalNameSearch.java

private SummaryStatistics getHitIndices(Multimap<String, Set<String>> results, FingerprintEncoder encoder) {

    SummaryStatistics summaryStatistics = new SummaryStatistics();

    QUERY: for (String name : results.keySet()) {

        String normName = encoder.encode(name);

        StringBuffer buffer = new StringBuffer();

        List<Set<String>> hits = new ArrayList<Set<String>>(results.get(name));

        for (int i = 0; i < hits.size(); i++) {

            Set<String> hitNames = hits.get(i);

            for (String hit : hitNames) {

                String normHit = encoder.encode(hit);

                buffer.append("\t").append(hit);
                buffer.append("\t").append(normHit);
                buffer.append("\t").append(StringUtils.getLevenshteinDistance(normName, normHit));
                buffer.append("\n");

                if (normName.equals(normHit)) {
                    summaryStatistics.addValue(i + 1);
                    continue QUERY;
                }//ww w .j  a  va2  s  . com

            }

        }

    }

    return summaryStatistics;

}