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

Introduction

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

Prototype

public double getMin() 

Source Link

Document

Returns the minimum of the available values

Usage

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

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

    for (PlateDouble plate : array) {

        List<Double> resultList = new ArrayList<Double>();
        double aggregatedReturned = Precision.round(min.platesAggregated(plate), precision);

        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 resultAggregated = Precision.round(statAggregated.getMin(), precision);

        assertTrue(resultAggregated == aggregatedReturned);
    }
}

---

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

/**
 * Tests the aggregated plate statistics method using the values between the indices of
 * the collection./*from w  w  w.  jav a2 s . com*/
 */
@Test
public void testAggregatedPlateCollectionIndices() {

    int size = arrayIndices[0].first().size();
    int begin = random.nextInt(size - 5);
    int end = (begin + 4) + random.nextInt(size - (begin + 4) + 1);

    List<PlateDouble> collection = Arrays.asList(arrayIndices);
    Map<PlateDouble, Double> aggregatedReturnedMap = min.platesAggregated(collection, begin, end - begin);

    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().subList(begin, end));
        }

        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 resultAggregated = statAggregated.getMin();

        aggregatedResultMap.put(plate, resultAggregated);
    }

    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.MinDoubleTest.java

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

    for (PlateDouble plate : array) {

        List<Double> resultList = new ArrayList<Double>();
        double aggregatedReturned = Precision.round(min.setsAggregated(plate.dataSet()), precision);

        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 resultAggregated = Precision.round(statAggregated.getMin(), precision);

        assertTrue(resultAggregated == aggregatedReturned);
    }
}

---

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

/**
 * Tests the aggregated plate statistics method using a collection.
 *///from www .  j  a v  a 2  s. c o m
@Test
public void testAggregatedSetCollection() {

    List<WellSetDouble> collection = new ArrayList<WellSetDouble>();

    for (PlateDouble plate : array) {
        collection.add(plate.dataSet());
    }

    Map<WellSetDouble, Double> aggregatedReturnedMap = min.setsAggregated(collection);
    Map<WellSetDouble, Double> aggregatedResultMap = new TreeMap<WellSetDouble, Double>();

    for (WellSetDouble set : collection) {

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

        for (WellDouble well : set) {
            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.getMin();

        aggregatedResultMap.put(set, aggregatedResult);
    }

    for (WellSetDouble set : collection) {

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

        assertTrue(result == returned);
    }
}

---

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

/**
 * Tests the aggregated plate statistics method using an array.
 *///from w w  w.j  a v  a  2 s . c o  m
@Test
public void testAggregatedSetArray() {

    WellSetInteger[] setArray = new WellSetInteger[array.length];

    for (int i = 0; i < setArray.length; i++) {
        setArray[i] = array[i].dataSet();
    }

    Map<WellSetInteger, Double> aggregatedReturnedMap = min.setsAggregated(setArray);
    Map<WellSetInteger, Double> aggregatedResultMap = new TreeMap<WellSetInteger, Double>();

    for (WellSetInteger set : setArray) {

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

        for (WellInteger well : set) {
            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.getMin();

        aggregatedResultMap.put(set, aggregatedResult);
    }

    for (WellSetInteger set : setArray) {

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

        assertTrue(result == returned);
    }

}

---

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

/**
 * Tests the aggregated plate statistics method using an array.
 *//*w w w . j  a v  a  2  s  .c  o m*/
@Test
public void testAggregatedSetArray() {

    WellSet[] setArray = new WellSet[array.length];

    for (int i = 0; i < setArray.length; i++) {
        setArray[i] = array[i].dataSet();
    }

    Map<WellSet, Double> aggregatedReturnedMap = min.setsAggregated(setArray);
    Map<WellSet, Double> aggregatedResultMap = new TreeMap<WellSet, Double>();

    for (WellSet set : setArray) {

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

        for (Well well : set) {
            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.getMin();

        aggregatedResultMap.put(set, aggregatedResult);
    }

    for (WellSet set : setArray) {

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

        assertTrue(result == returned);
    }

}

---

From source file:com.fpuna.preproceso.PreprocesoTS.java

private static void calculoFeatures(Registro[] muestras, String activity) {

    DescriptiveStatistics stats_x = new DescriptiveStatistics();
    DescriptiveStatistics stats_y = new DescriptiveStatistics();
    DescriptiveStatistics stats_z = new DescriptiveStatistics();
    //DescriptiveStatistics stats_m1 = new DescriptiveStatistics();
    //DescriptiveStatistics stats_m2 = new DescriptiveStatistics();
    double[] fft_x;
    double[] fft_y;
    double[] fft_z;
    double[] AR_4;

    for (int i = 0; i < muestras.length; i++) {
        stats_x.addValue(muestras[i].getValor_x());
        stats_y.addValue(muestras[i].getValor_y());
        stats_z.addValue(muestras[i].getValor_z());
    }//w w w . j  a  v a2 s . c  o m

    //********* FFT *********
    fft_x = Util.transform(stats_x.getValues());
    fft_y = Util.transform(stats_y.getValues());
    fft_z = Util.transform(stats_z.getValues());

    //******************* Eje X *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_x.getMean() + ",");
    //std(s) - Standard deviation
    System.out.print(stats_x.getStandardDeviation() + ",");
    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_x.getMax() + ",");
    //min(s) - Smallest value in array
    System.out.print(stats_x.getMin() + ",");
    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_x.getSkewness() + ",");
    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_x.getKurtosis() + ",");
    //energy(s) - Average sum of the squares
    System.out.print(stats_x.getSumsq() / stats_x.getN() + ",");
    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_x) + ",");
    //iqr (s) Interquartile range
    System.out.print(stats_x.getPercentile(75) - stats_x.getPercentile(25) + ",");
    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_x.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_x, stats_x.getValues()) + ",");

    //******************* Eje Y *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_y.getMean() + ",");
    //std(s) - Standard deviation
    System.out.print(stats_y.getStandardDeviation() + ",");
    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_y.getMax() + ",");
    //min(s) - Smallest value in array
    System.out.print(stats_y.getMin() + ",");
    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_y.getSkewness() + ",");
    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_y.getKurtosis() + ",");
    //energy(s) - Average sum of the squares
    System.out.print(stats_y.getSumsq() / stats_y.getN() + ",");
    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_y) + ",");
    //iqr (s) Interquartile range
    System.out.print(stats_y.getPercentile(75) - stats_y.getPercentile(25) + ",");
    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_y.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_y, stats_y.getValues()) + ",");

    //******************* Eje Z *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_z.getMean() + ",");
    //std(s) - Standard deviation
    System.out.print(stats_z.getStandardDeviation() + ",");
    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_z.getMax() + ",");
    //min(s) - Smallest value in array
    System.out.print(stats_z.getMin() + ",");
    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_z.getSkewness() + ",");
    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_z.getKurtosis() + ",");
    //energy(s) - Average sum of the squares
    System.out.print(stats_z.getSumsq() / stats_z.getN() + ",");
    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_z) + ",");
    //iqr (s) Interquartile range
    System.out.print(stats_z.getPercentile(75) - stats_z.getPercentile(25) + ",");
    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_z.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_z, stats_z.getValues()) + ",");

    //******************* Feature combinados *******************/
    //sma(s1; s2; s3) - Signal magnitude area
    System.out.print(Util.sma(stats_x.getValues(), stats_y.getValues(), stats_z.getValues()) + ",");
    //correlation(s1; s2) - Pearson Correlation coefficient
    System.out.print(new PearsonsCorrelation().correlation(stats_x.getValues(), stats_y.getValues()) + ",");
    System.out.print(new PearsonsCorrelation().correlation(stats_x.getValues(), stats_z.getValues()) + ",");
    System.out.print(new PearsonsCorrelation().correlation(stats_y.getValues(), stats_z.getValues()) + ",");

    //******************* Actividad *******************/
    System.out.print(activity);
    System.out.print("\n");
}

---

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

/**
 * Tests the aggregated plate statistics method using an array.
 *//*w  w w.j  a  va 2 s.co m*/
@Test
public void testAggregatedSetArray() {

    WellSetDouble[] setArray = new WellSetDouble[array.length];

    for (int i = 0; i < setArray.length; i++) {
        setArray[i] = array[i].dataSet();
    }

    Map<WellSetDouble, Double> aggregatedReturnedMap = min.setsAggregated(setArray);
    Map<WellSetDouble, Double> aggregatedResultMap = new TreeMap<WellSetDouble, Double>();

    for (WellSetDouble set : setArray) {

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

        for (WellDouble well : set) {
            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.getMin();

        aggregatedResultMap.put(set, aggregatedResult);
    }

    for (WellSetDouble set : setArray) {

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

        assertTrue(result == returned);
    }

}

---

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

/**
 * Tests the aggregated plate statistics method using the values between the indices.
 *//*ww  w. j  a va  2  s.c  o  m*/
@Test
public void testAggregatedPlateIndices() {

    for (Plate plate : arrayIndices) {

        int size = arrayIndices[0].first().size();
        int begin = random.nextInt(size - 5);
        int end = (begin + 4) + random.nextInt(size - (begin + 4) + 1);

        List<Double> resultList = new ArrayList<Double>();
        double aggregatedReturned = Precision.round(min.platesAggregated(plate, begin, end - begin), precision);

        for (Well well : plate) {
            resultList.addAll(well.toDouble().subList(begin, end));
        }

        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 = Precision.round(statAggregated.getMin(), precision);

        assertTrue(aggregatedResult == aggregatedReturned);
    }
}

---

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

/**
 * Tests the aggregated plate statistics method using the values between the indices of
 * the collection./*  ww w .ja v  a  2s .co m*/
 */
@Test
public void testAggregatedSetCollectionIndices() {

    int size = arrayIndices[0].first().size();
    int begin = random.nextInt(size - 5);
    int end = (begin + 4) + random.nextInt(size - (begin + 4) + 1);

    List<WellSet> collection = new ArrayList<WellSet>();

    for (Plate plate : arrayIndices) {
        collection.add(plate.dataSet());
    }

    Map<WellSet, Double> aggregatedReturnedMap = min.setsAggregated(collection, begin, end - begin);
    Map<WellSet, Double> aggregatedResultMap = new TreeMap<WellSet, Double>();

    for (WellSet set : collection) {

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

        for (Well well : set) {
            resultList.addAll(well.toDouble().subList(begin, end));
        }

        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.getMin();

        aggregatedResultMap.put(set, aggregatedResult);
    }

    for (WellSet set : collection) {

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

        assertTrue(result == returned);
    }
}

---

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