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:com.github.jessemull.microflexdouble.stat.MeanTest.java

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

    for (Plate plate : array) {

        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 = Precision.round(stat.getMean(), precision);
            double returned = Precision.round(mean.well(well), precision);

            assertTrue(result == returned);
        }
    }
}

---

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

/**
 * Tests well calculation./*from   ww w. ja  v  a 2 s.  c o m*/
 */
@Test
public void testWell() {

    for (PlateDouble plate : array) {

        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 = Precision.round(stat.getMean(), precision);
            double returned = Precision.round(mean.well(well), precision);

            assertTrue(result == returned);
        }
    }
}

---

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

/**
 * Tests well calculation.//  w w w .  ja  v  a2s . co m
 */
@Test
public void testWell() {

    for (PlateInteger plate : array) {

        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 = Precision.round(stat.getMean(), precision);
            double returned = Precision.round(mean.well(well), precision);

            assertTrue(result == returned);
        }
    }
}

---

From source file:classifiers.ComplexClassifierZufall.java

@Override
public void BewertunginProzent() throws Exception {
    /*  if (Model.getAnzahlkanten() != 0) {
    System.out.println("Parameter:" + "Vernetzungsprozent:" + this.vernetzung + "(" + (Model.getMoeglischeAnzahlKanten() * vernetzung) / Model.getAnzahlkanten()
            + ")," + " " + "Anzahldurchlauf:" + this.anzahldurchlauf + "    " + "max Anzahlkanten:" + Model.getAnzahlkanten() + "(" + Model.getMoeglischeAnzahlKanten() + " )");
      } else {/*from  w w w  .  jav a  2s. c  om*/
    System.out.println("Parameter:" + "Vernetzungsprozent:" + this.vernetzung + "(" + (Model.getMoeglischeAnzahlKanten() * vernetzung)
            + ")," + " " + "Anzahldurchlauf:" + this.anzahldurchlauf + "    " + "max Anzahlkanten:" + Model.getAnzahlkanten() + "(" + Model.getMoeglischeAnzahlKanten() + " )");
      }
      System.out.println("----------------------------------------------------------------------------------------");*/

    double count = 0;
    double[][] bestergeb = new double[1][2];
    double max = 100;
    double[][] hilf;
    double[][] hilf2;

    double[] erg = new double[5];

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

        Bootstrap(Modelmenge);
        train(this.traindaten);
        // System.out.println("training NR" + " " + i + ":");
        // System.out.println("trainingsdeaten:");
        hilf = new double[1][2];
        hilf2 = new double[1][2];

        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) (this.trainergebnisse[i][0] * 100) / 100 + "%" + "  " + "Dauer:" + " " + (int) (this.trainergebnisse[i][1]) + "ms");

        hilf2 = test(testdaten);

        this.testergebnisse[i][0] = hilf2[0][0];
        this.testergebnisse[i][1] = hilf2[0][1];
        /* if(testergebnisse[i][0]<=max)
         {
         max=testergebnisse[i][0];
         bestemodel=Model;
         bestergeb[0][0]=testergebnisse[i][0];
         bestergeb[0][1]=testergebnisse[i][1];
                
         }*/

        //System.out.println("Validierung NR" + " " + i + ":");
        //System.out.println("Validierungsngsdaten:");

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

    // System.out.println("Mittlere Fehlerquote der Validierungsmengen:" + " " + (double) (int) (Mittlerevalidierungsquote * 100) / 100 + "%" + "(" + (double) (int) ((stadartdeviationvalidierung / Math.sqrt(anzahldurchlauf)) * 100) / 100 + "%)");
    //System.out.println("Mittlere Dauer der Validierung :" + " " + (int) (Mittlerezeit) + " " + "ms" + "(" + (int) ((standartdeviationtime / Math.sqrt(anzahldurchlauf))) + "ms)");
    erg[0] = vernetzung;
    erg[1] = (double) (int) (Mittlerevalidierungsquote * 100) / 100;
    erg[2] = Mittlerezeit;
    erg[3] = (double) (int) ((stadartdeviationvalidierung / Math.sqrt(anzahldurchlauf)) * 100) / 100;
    erg[4] = (int) ((standartdeviationtime / Math.sqrt(anzahldurchlauf)));

    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];
    struct.setErgebnisse(erg);
    /* 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");
    /* System.out.println();
      System.out.println("Beste Struktur:"+"  "+"Validierung:"+" "+(int)bestergeb[0][0]+"%"+"  "+"Zeit:"+" "+(int)bestergeb[0][1]+"ms");
      System.out.println("---------------------------------------------------------------------------------------");
      bestemodel=Model; if(bestemodel!=null) bestemodel.ToString();*/

}

---

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

/**
 * Tests the aggregated plate statistics method using a collection.
 *///from www .  j a va 2  s  . c  om
@Test
public void testAggregatedPlateCollection() {

    List<Plate> collection = Arrays.asList(array);
    Map<Plate, Double> aggregatedReturnedMap = mean.platesAggregated(collection);
    Map<Plate, Double> aggregatedResultMap = new TreeMap<Plate, Double>();

    for (Plate plate : collection) {

        List<Double> resultList = new ArrayList<Double>();

        for (Well well : plate) {
            resultList.addAll(well.toDouble());
        }

        double[] inputAggregated = new double[resultList.size()];

        for (int i = 0; i < resultList.size(); i++) {
            inputAggregated[i] = resultList.get(i);
        }

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double aggregatedResult = statAggregated.getMean();

        aggregatedResultMap.put(plate, aggregatedResult);
    }

    for (Plate plate : collection) {

        double result = Precision.round(aggregatedResultMap.get(plate), precision);
        double returned = Precision.round(aggregatedReturnedMap.get(plate), precision);

        assertTrue(result == returned);
    }
}

---

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

/**
 * Tests the aggregated plate statistics method using an array.
 *///from  w w  w .  j a  v  a2  s  . co  m
@Test
public void testAggregatedPlateArray() {

    Map<Plate, Double> aggregatedReturnedMap = mean.platesAggregated(array);
    Map<Plate, Double> aggregatedResultMap = new TreeMap<Plate, Double>();

    for (Plate plate : array) {

        List<Double> resultList = new ArrayList<Double>();

        for (Well well : plate) {
            resultList.addAll(well.toDouble());
        }

        double[] inputAggregated = new double[resultList.size()];

        for (int i = 0; i < resultList.size(); i++) {
            inputAggregated[i] = resultList.get(i);
        }

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double aggregatedResult = statAggregated.getMean();

        aggregatedResultMap.put(plate, aggregatedResult);
    }

    for (Plate plate : array) {

        double result = Precision.round(aggregatedResultMap.get(plate), precision);
        double returned = Precision.round(aggregatedReturnedMap.get(plate), precision);

        assertTrue(result == returned);
    }

}

---

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

/**
 * Tests the aggregated plate statistics method using a collection.
 *//*from w  w  w  .j av  a  2  s.co m*/
@Test
public void testAggregatedPlateCollection() {

    List<Plate> collection = Arrays.asList(array);
    Map<Plate, Double> aggregatedReturnedMap = mean.platesAggregated(collection);
    Map<Plate, Double> aggregatedResultMap = new TreeMap<Plate, Double>();

    for (Plate plate : collection) {

        List<Double> resultList = new ArrayList<Double>();

        for (Well well : plate) {
            resultList.addAll(well.data());
        }

        double[] inputAggregated = new double[resultList.size()];

        for (int i = 0; i < resultList.size(); i++) {
            inputAggregated[i] = resultList.get(i);
        }

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double aggregatedResult = statAggregated.getMean();

        aggregatedResultMap.put(plate, aggregatedResult);
    }

    for (Plate plate : collection) {

        double result = Precision.round(aggregatedResultMap.get(plate), precision);
        double returned = Precision.round(aggregatedReturnedMap.get(plate), precision);

        assertTrue(result == returned);
    }
}

---

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

/**
 * Tests the aggregated plate statistics method using an array.
 *///from ww w . j  av a2s  .c  o m
@Test
public void testAggregatedPlateArray() {

    Map<Plate, Double> aggregatedReturnedMap = mean.platesAggregated(array);
    Map<Plate, Double> aggregatedResultMap = new TreeMap<Plate, Double>();

    for (Plate plate : array) {

        List<Double> resultList = new ArrayList<Double>();

        for (Well well : plate) {
            resultList.addAll(well.data());
        }

        double[] inputAggregated = new double[resultList.size()];

        for (int i = 0; i < resultList.size(); i++) {
            inputAggregated[i] = resultList.get(i);
        }

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double aggregatedResult = statAggregated.getMean();

        aggregatedResultMap.put(plate, aggregatedResult);
    }

    for (Plate plate : array) {

        double result = Precision.round(aggregatedResultMap.get(plate), precision);
        double returned = Precision.round(aggregatedReturnedMap.get(plate), precision);

        assertTrue(result == returned);
    }

}

---

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

/**
 * Tests the aggregated plate statistics method using a collection.
 *//*ww w . j  a v a2  s  .c o m*/
@Test
public void testAggregatedPlateCollection() {

    List<PlateDouble> collection = Arrays.asList(array);
    Map<PlateDouble, Double> aggregatedReturnedMap = mean.platesAggregated(collection);
    Map<PlateDouble, Double> aggregatedResultMap = new TreeMap<PlateDouble, Double>();

    for (PlateDouble plate : collection) {

        List<Double> resultList = new ArrayList<Double>();

        for (WellDouble well : plate) {
            resultList.addAll(well.data());
        }

        double[] inputAggregated = new double[resultList.size()];

        for (int i = 0; i < resultList.size(); i++) {
            inputAggregated[i] = resultList.get(i);
        }

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double aggregatedResult = statAggregated.getMean();

        aggregatedResultMap.put(plate, aggregatedResult);
    }

    for (PlateDouble plate : collection) {

        double result = Precision.round(aggregatedResultMap.get(plate), precision);
        double returned = Precision.round(aggregatedReturnedMap.get(plate), precision);

        assertTrue(result == returned);
    }
}

---

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

/**
 * Tests the aggregated plate statistics method using an array.
 *///  w ww.jav a2  s.  c om
@Test
public void testAggregatedPlateArray() {

    Map<PlateDouble, Double> aggregatedReturnedMap = mean.platesAggregated(array);
    Map<PlateDouble, Double> aggregatedResultMap = new TreeMap<PlateDouble, Double>();

    for (PlateDouble plate : array) {

        List<Double> resultList = new ArrayList<Double>();

        for (WellDouble well : plate) {
            resultList.addAll(well.data());
        }

        double[] inputAggregated = new double[resultList.size()];

        for (int i = 0; i < resultList.size(); i++) {
            inputAggregated[i] = resultList.get(i);
        }

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double aggregatedResult = statAggregated.getMean();

        aggregatedResultMap.put(plate, aggregatedResult);
    }

    for (PlateDouble plate : array) {

        double result = Precision.round(aggregatedResultMap.get(plate), precision);
        double returned = Precision.round(aggregatedReturnedMap.get(plate), precision);

        assertTrue(result == returned);
    }

}

---

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