Example usage for org.apache.commons.math3.stat.descriptive DescriptiveStatistics getMean

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.descriptive DescriptiveStatistics getMean.

Prototype

public double getMean() 

Source Link

Document

Returns the <a href="http://www.xycoon.com/arithmetic_mean.htm"> arithmetic mean </a> of the available values

Usage

From source file:classifiers.Simpleclassifier.java

@Override
public void BewertunginProzent() throws Exception {
    System.out.println("Parameter:" + "Anzahldurchlauf:" + this.anzahldurchlauf);
    //System.out.println("----------------------------------------------------------------------------------------");
    double count = 0;
    double max = 0;
    double[][] hilf = new double[1][2];
    double[][] hilf2 = new double[1][2];

    for (int i = 0; i < anzahldurchlauf; i++) {

        Bootstrap(Modelmenge);// w ww.  j  av a2 s.com
        train(this.traindaten);
        // System.out.println("training NR" + " " + i + ":");
        // System.out.println("trainingsdeaten:");

        hilf = test(traindaten);

        this.trainergebnisse[i][0] = hilf[0][0];
        this.trainergebnisse[i][1] = hilf[0][1];
        //System.out.println("Fehlerquote TrainingNR" + " " + i + ":" + " " + (double)(int) (hilf[0][0]*100)/100 + "%" + "  " + "Dauer:" + " " + (int) hilf[0][1]  + "ms");

        hilf2 = test(testdaten);

        this.testergebnisse[i][0] = hilf2[0][0];
        this.testergebnisse[i][1] = hilf2[0][1];
        // System.out.println("Validierung NR" + " " + i + ":");
        //System.out.println("Validierungsngsdaten:");

        // System.out.println("Fehlerquote Validierungs NR" + " " + i + ":" + " " + (double)(int) (hilf2[0][0]*100)/100 + "%" + "  " + "Dauer:" + " " + (int) hilf2[0][1]  + "ms");
        //System.out.println("----------------------------------------------------------------------------------------");

    }

    DescriptiveStatistics stats1 = new DescriptiveStatistics();
    DescriptiveStatistics stat1 = new DescriptiveStatistics();

    // Add the data from the array
    for (int i = 0; i < trainergebnisse.length; i++) {

        stats1.addValue(trainergebnisse[i][0]);
        stat1.addValue(trainergebnisse[i][1]);
    }

    double mean1 = stats1.getMean();
    double std1 = stats1.getStandardDeviation();
    double meanzeit1 = stat1.getMean();
    double stdzeit1 = stat1.getStandardDeviation();

    // System.out.println("Mittlere Felehrquote des Tainings:" + " " + (double)(int) (mean1*100)/100 + "%" + "(" + (double)(int) ((std1 / Math.sqrt(anzahldurchlauf))*100)/100 + "%)");
    //System.out.println("Mittlere Dauer des trainings:" + " " + (int) meanzeit1  + " " + "ms" + "(" + (int) ((stdzeit1 / Math.sqrt(anzahldurchlauf)) ) + "ms)");
    //System.out.println("--------------------------------------------------------------------------------------");

    DescriptiveStatistics stats = new DescriptiveStatistics();
    DescriptiveStatistics stat = new DescriptiveStatistics();

    // Add the data from the array
    for (int i = 0; i < testergebnisse.length; i++) {

        stats.addValue(testergebnisse[i][0]);
        stat.addValue(testergebnisse[i][1]);
    }

    this.Mittlerevalidierungsquote = stats.getMean();
    this.stadartdeviationvalidierung = (int) (stats.getStandardDeviation() / Math.sqrt(anzahldurchlauf));
    this.Mittlerezeit = stat.getMean();
    this.standartdeviationtime = (int) (stat.getStandardDeviation() / Math.sqrt(anzahldurchlauf));
    ergb[1] = Mittlerevalidierungsquote;
    ergb[2] = Mittlerezeit;
    ergb[3] = (double) (int) ((stadartdeviationvalidierung / Math.sqrt(anzahldurchlauf)) * 100) / 100;
    ergb[4] = (int) ((standartdeviationtime / Math.sqrt(anzahldurchlauf)));

    struct.setErgebnisse(ergb);
    /*  System.out.println("Durchnittliche Fehlerquote der Validierungsmengen:" + " " + (int) Mittlerevalidierungsquote + "%" + "(" + (int) (stadartdeviationvalidierung / Math.sqrt(anzahldurchlauf)) + "%)");
      System.out.println("durchnittliche Dauer der Validierung :" + " " + (int) (Mittlerezeit)  + " " + "ms" + "(" + (int) ((standartdeviationtime / Math.sqrt(anzahldurchlauf)) ) + "ms)");*/

    train(this.Modelmenge);
    hilf = test(Modelmenge);
    this.Modelergebnisse[0][0] = hilf[0][0];
    this.Modelergebnisse[0][1] = hilf[0][1];
    hilf = test(validierungsmenge);
    validierungsergebnisse[0][0] = hilf[0][0];
    validierungsergebnisse[0][1] = hilf[0][1];
    /* System.out.println("---------------------------------------------------------------------------------------");*/

    // System.out.println("Fehlerquote der  training auf dem Datensatz:" + "  " + (double)(int) (Modelergebnisse[0][0]*100)/100 + "%");
    // System.out.println("Zeit des trainings (Datensatz):" + " " + (int) (Modelergebnisse[0][1] ) + " " + "ms");
    // System.out.println("---------------------------------------------------------------------------------------");
    // System.out.println("Fehlerquote der Test:" + "  " + (double)(int) (validierungsergebnisse[0][0]*100)/100 + "%");
    // System.out.println("Zeit der Test:" + " " + (int) (validierungsergebnisse[0][1] ) + " " + "ms");

    /* Instances bestmodel=new Instances(Modelmenge,bestmodelindexen.length);
     double result;
     for(int i=0;i<bestmodelindexen.length;i++)
     {
     bestmodel.add(Modelmenge.instance(bestmodelindexen[i]));
     }
     train(bestmodel);
            
            
            
     result= test(validierungsmenge);
     System.out.println();
     System.out.println("der Beste Model  ist:");
     System.out.println("-----------------------------------------");
     System.out.println(bestmodel);
     System.out.println("mit eine Leistung von:"+"   "+result+"%");
               
               
               
               
               
     return result;
            
     }*/
}

---

From source file:com.loadtesting.core.data.TimeSerieData.java

public TimeSerieData(String name, List<TimeSample> samples, CapturerConfig config) {
    this.name = name;
    this.unit = config.getUnit();
    this.volume = samples.size();
    if (volume > 0) {
        TimeSample first = samples.get(0);
        this.unit = first.getTimeUnit();
        this.opening = first.getTime(unit);
        TimeSample last = samples.get(volume - 1);
        this.closing = last.getTime(unit);
        this.samples = config.getFilter().filter(samples);

        DescriptiveStatistics stats = new DescriptiveStatistics(volume);
        for (TimeSample timeSample : samples) {
            stats.addValue(timeSample.getTime(unit));
        }//w w  w .ja va2 s. c o  m
        this.high = stats.getMax();
        this.low = stats.getMin();
        this.median = (high + low) / 2;
        this.typical = (high + low + closing) / 3;
        this.weightedClose = (high + low + closing + closing) / 4;
        this.sma = stats.getMean();
        this.variance = stats.getVariance();
        this.sd = stats.getStandardDeviation();
        this.sum = stats.getSum();
        this.sumsq = stats.getSumsq();
        this.skewness = stats.getSkewness();
        this.kurtosis = stats.getKurtosis();
        this.geometricMean = stats.getGeometricMean();
        this.populationVariance = stats.getPopulationVariance();
    } else {
        this.samples = samples;
    }
}

---

From source file:gdsc.smlm.ij.plugins.PSFEstimator.java

private void setParams(int i, double[] params, double[] params_dev, DescriptiveStatistics sample) {
    if (sample.getN() > 0) {
        params[i] = sample.getMean();
        params_dev[i] = sample.getStandardDeviation();
    }/*  w w  w .j  av  a  2  s  . c om*/
}

---

From source file:com.github.jessemull.microflexdouble.stat.MeanTest.java

/**
 * Tests the plate statistics method./* w w  w .j av  a  2 s  . c  om*/
 */
@Test
public void testPlate() {

    for (Plate plate : array) {

        Map<Well, Double> resultMap = new TreeMap<Well, Double>();
        Map<Well, Double> returnedMap = mean.plate(plate);

        for (Well well : plate) {

            double[] input = new double[well.size()];
            int index = 0;

            for (double bd : well) {
                input[index++] = bd;
                ;
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(input);
            double result = stat.getMean();

            resultMap.put(well, result);
        }

        for (Well well : plate) {

            double result = Precision.round(resultMap.get(well), precision);
            double returned = Precision.round(returnedMap.get(well), precision);

            assertTrue(result == returned);
        }
    }
}

---

From source file:com.github.jessemull.microflex.stat.statdouble.MeanDoubleTest.java

/**
 * Tests the plate statistics method./*from  w w w .  j  ava 2  s . c  o m*/
 */
@Test
public void testPlate() {

    for (PlateDouble plate : array) {

        Map<WellDouble, Double> resultMap = new TreeMap<WellDouble, Double>();
        Map<WellDouble, Double> returnedMap = mean.plate(plate);

        for (WellDouble well : plate) {

            double[] input = new double[well.size()];
            int index = 0;

            for (double bd : well) {
                input[index++] = bd;
                ;
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(input);
            double result = stat.getMean();

            resultMap.put(well, result);
        }

        for (WellDouble well : plate) {

            double result = Precision.round(resultMap.get(well), precision);
            double returned = Precision.round(returnedMap.get(well), precision);

            assertTrue(result == returned);
        }
    }
}

---

From source file:com.github.jessemull.microflex.stat.statinteger.MeanIntegerTest.java

/**
 * Tests the plate statistics method./*from  ww  w  . j  a v a 2 s.  c  o m*/
 */
@Test
public void testPlate() {

    for (PlateInteger plate : array) {

        Map<WellInteger, Double> resultMap = new TreeMap<WellInteger, Double>();
        Map<WellInteger, Double> returnedMap = mean.plate(plate);

        for (WellInteger well : plate) {

            double[] input = new double[well.size()];
            int index = 0;

            for (double bd : well) {
                input[index++] = bd;
                ;
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(input);
            double result = stat.getMean();

            resultMap.put(well, result);
        }

        for (WellInteger well : plate) {

            double result = Precision.round(resultMap.get(well), precision);
            double returned = Precision.round(returnedMap.get(well), precision);

            assertTrue(result == returned);
        }
    }
}

---

From source file:com.github.jessemull.microflexdouble.stat.MeanTest.java

/**
 * Tests set calculation.//from   w w  w  .  j a v  a  2s.c  o m
 */
@Test
public void testSet() {

    for (Plate plate : array) {

        Map<Well, Double> resultMap = new TreeMap<Well, Double>();
        Map<Well, Double> returnedMap = mean.set(plate.dataSet());

        for (Well well : plate) {

            double[] input = new double[well.size()];
            int index = 0;

            for (double bd : well) {
                input[index++] = bd;
                ;
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(input);
            double result = stat.getMean();

            resultMap.put(well, result);
        }

        for (Well well : plate) {

            double result = Precision.round(resultMap.get(well), precision);
            double returned = Precision.round(returnedMap.get(well), precision);

            assertTrue(result == returned);
        }
    }

}

---

From source file:com.github.jessemull.microflex.stat.statdouble.MeanDoubleTest.java

/**
 * Tests set calculation./*from   ww  w  . j ava 2  s .c  o m*/
 */
@Test
public void testSet() {

    for (PlateDouble plate : array) {

        Map<WellDouble, Double> resultMap = new TreeMap<WellDouble, Double>();
        Map<WellDouble, Double> returnedMap = mean.set(plate.dataSet());

        for (WellDouble well : plate) {

            double[] input = new double[well.size()];
            int index = 0;

            for (double bd : well) {
                input[index++] = bd;
                ;
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(input);
            double result = stat.getMean();

            resultMap.put(well, result);
        }

        for (WellDouble well : plate) {

            double result = Precision.round(resultMap.get(well), precision);
            double returned = Precision.round(returnedMap.get(well), precision);

            assertTrue(result == returned);
        }
    }

}

---

From source file:com.github.jessemull.microflex.stat.statinteger.MeanIntegerTest.java

/**
 * Tests set calculation.//from w  w  w  .  j  av  a2s  . c  om
 */
@Test
public void testSet() {

    for (PlateInteger plate : array) {

        Map<WellInteger, Double> resultMap = new TreeMap<WellInteger, Double>();
        Map<WellInteger, Double> returnedMap = mean.set(plate.dataSet());

        for (WellInteger well : plate) {

            double[] input = new double[well.size()];
            int index = 0;

            for (double bd : well) {
                input[index++] = bd;
                ;
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(input);
            double result = stat.getMean();

            resultMap.put(well, result);
        }

        for (WellInteger well : plate) {

            double result = Precision.round(resultMap.get(well), precision);
            double returned = Precision.round(returnedMap.get(well), precision);

            assertTrue(result == returned);
        }
    }

}

---

From source file:de.iisys.schub.processMining.similarity.AlgoController.java

private String showDocMetaData(List<Double> cosineSimValues) {
    DescriptiveStatistics stat = new DescriptiveStatistics();

    for (int i = 0; i < cosineSimValues.size(); i++) {
        stat.addValue(cosineSimValues.get(i));
    }/*from   ww w .  j  av  a 2s  .c o m*/

    double min = Math.round(stat.getMin() * 1000) / 1000.0;
    double max = Math.round(stat.getMax() * 1000) / 1000.0;
    double arithMean = Math.round(stat.getMean() * 10000) / 10000.0;
    double percentile = Math.round(stat.getPercentile(PERCENTILE) * 1000) / 1000.0;

    DecimalFormat df = new DecimalFormat("#00.00");
    String meta = "Min: " + df.format(min * 100) + " %" + ", Max: " + df.format(max * 100) + " %"
            + ", Arith. Mean: " + df.format(arithMean * 100) + " %" + ", Percentile (" + PERCENTILE + " %): "
            + df.format(percentile * 100) + " %";

    return meta;
}

---

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