Examples with DoubleMinMax de.lmu.ifi.dbs.elki.math.DoubleMinMax used on opensource projects

Example 76 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class OutlierMinusLogScaling method prepare.

@Override
public <A> void prepare(A array, NumberArrayAdapter<?, A> adapter) {
    DoubleMinMax mm = new DoubleMinMax();
    final int size = adapter.size(array);
    for (int i = 0; i < size; i++) {
        double val = adapter.getDouble(array, i);
        if (!Double.isNaN(val) && !Double.isInfinite(val)) {
            mm.put(val);
        }
    }
    max = mm.getMax();
    mlogmax = -FastMath.log(mm.getMin() / max);
}

Example 77 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class OutlierSqrtScaling method prepare.

@Override
public void prepare(OutlierResult or) {
    if (pmin == null || pmax == null) {
        DoubleMinMax mm = new DoubleMinMax();
        DoubleRelation scores = or.getScores();
        for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
            double val = scores.doubleValue(id);
            if (!Double.isInfinite(val)) {
                mm.put(val);
            }
        }
        min = (pmin == null) ? mm.getMin() : pmin;
        max = (pmax == null) ? mm.getMax() : pmax;
    }
    factor = FastMath.sqrt(max - min);
}

Example 78 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class HeDESNormalizationOutlierScaling method prepare.

@Override
public <A> void prepare(A array, NumberArrayAdapter<?, A> adapter) {
    MeanVariance mv = new MeanVariance();
    DoubleMinMax minmax = new DoubleMinMax();
    final int size = adapter.size(array);
    for (int i = 0; i < size; i++) {
        double val = adapter.getDouble(array, i);
        if (!Double.isNaN(val) && !Double.isInfinite(val)) {
            mv.put(val);
            minmax.put(val);
        }
    }
    mean = mv.getMean();
    stddev = mv.getSampleStddev();
    scaledmax = getScaled(minmax.getMax());
    scaledmin = getScaled(minmax.getMin());
}

Example 79 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class HeDESNormalizationOutlierScaling method prepare.

@Override
public void prepare(OutlierResult or) {
    MeanVariance mv = new MeanVariance();
    DoubleMinMax minmax = new DoubleMinMax();
    DoubleRelation scores = or.getScores();
    for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
        double val = scores.doubleValue(id);
        if (!Double.isNaN(val) && !Double.isInfinite(val)) {
            mv.put(val);
            minmax.put(val);
        }
    }
    mean = mv.getMean();
    stddev = mv.getSampleStddev();
    scaledmax = getScaled(minmax.getMax());
    scaledmin = getScaled(minmax.getMin());
}

Example 80 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class MinusLogGammaScaling method prepare.

@Override
public void prepare(OutlierResult or) {
    meta = or.getOutlierMeta();
    // Determine Minimum and Maximum.
    DoubleMinMax mm = new DoubleMinMax();
    DoubleRelation scores = or.getScores();
    for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
        double score = scores.doubleValue(id);
        if (!Double.isNaN(score) && !Double.isInfinite(score)) {
            mm.put(score);
        }
    }
    max = mm.getMax();
    mlogmax = -FastMath.log(mm.getMin() / max);
    // with the prescaling, do Gamma Scaling.
    MeanVariance mv = new MeanVariance();
    for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
        double score = scores.doubleValue(id);
        score = preScale(score);
        if (!Double.isNaN(score) && !Double.isInfinite(score)) {
            mv.put(score);
        }
    }
    final double mean = mv.getMean();
    final double var = mv.getSampleVariance();
    k = (mean * mean) / var;
    theta = var / mean;
    atmean = GammaDistribution.regularizedGammaP(k, mean / theta);
// logger.warning("Mean:"+mean+" Var:"+var+" Theta: "+theta+" k: "+k+"
// valatmean"+atmean);
}