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

List of usage examples for org.apache.commons.math3.stat.descriptive DescriptiveStatistics getPercentile

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Introduction

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

Prototype

public double getPercentile(double p) throws MathIllegalStateException, MathIllegalArgumentException

Source Link

Document

Returns an estimate for the pth percentile of the stored values.

Usage

From source file:com.github.jessemull.microflex.stat.statbiginteger.PercentileBigIntegerTest.java

/**
 * Tests the aggregated plate statistics method using an array.
 *//*from w  w  w  .ja  v  a2 s  .  c  o m*/
@Test
public void testAggregatedSetArray() {

    int inputPercentile = 1 + random.nextInt(100);

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

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

    Map<WellSetBigInteger, BigDecimal> aggregatedReturnedMap = percentile.setsAggregated(setArray,
            inputPercentile);
    Map<WellSetBigInteger, BigDecimal> aggregatedResultMap = new TreeMap<WellSetBigInteger, BigDecimal>();

    for (WellSetBigInteger set : setArray) {

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

        for (WellBigInteger well : set) {
            resultList.addAll(well.toBigDecimal());
        }

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

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

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double resultAggregatedDouble = statAggregated.getPercentile(inputPercentile);

        BigDecimal aggregatedResult = new BigDecimal(resultAggregatedDouble);
        aggregatedResultMap.put(set, aggregatedResult);
    }

    for (WellSetBigInteger set : setArray) {

        BigDecimal result = aggregatedResultMap.get(set);
        BigDecimal returned = aggregatedReturnedMap.get(set);

        BigDecimal[] corrected = correctRoundingErrors(result, returned);

        assertEquals(corrected[0], corrected[1]);
    }

}

From source file:com.github.jessemull.microflex.stat.statbigdecimal.PercentileBigDecimalTest.java

/**
 * Tests the plate statistics method using the values between the indices.
 *///from ww w  .java2  s  .  c om
@Test
public void testPlateIndices() {

    for (PlateBigDecimal plate : arrayIndices) {

        int inputPercentile = 1 + random.nextInt(100);

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

        Map<WellBigDecimal, BigDecimal> resultMap = new TreeMap<WellBigDecimal, BigDecimal>();
        Map<WellBigDecimal, BigDecimal> returnedMap = percentile.plate(plate, begin, end - begin,
                inputPercentile);

        for (WellBigDecimal well : plate) {

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

            for (BigDecimal bd : well) {
                input[index++] = bd.doubleValue();
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(ArrayUtils.subarray(input, begin, end));
            double resultDouble = stat.getPercentile(inputPercentile);

            BigDecimal result = new BigDecimal(resultDouble);

            resultMap.put(well, result);
        }

        for (WellBigDecimal well : plate) {

            BigDecimal result = resultMap.get(well);
            BigDecimal returned = returnedMap.get(well);

            BigDecimal[] corrected = correctRoundingErrors(result, returned);

            assertEquals(corrected[0], corrected[1]);
        }
    }
}

From source file:com.github.jessemull.microflex.stat.statbigdecimal.PercentileBigDecimalTest.java

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

    for (PlateBigDecimal plate : arrayIndices) {

        int inputPercentile = 1 + random.nextInt(100);

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

        Map<WellBigDecimal, BigDecimal> resultMap = new TreeMap<WellBigDecimal, BigDecimal>();
        Map<WellBigDecimal, BigDecimal> returnedMap = percentile.set(plate.dataSet(), begin, end - begin,
                inputPercentile);

        for (WellBigDecimal well : plate) {

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

            for (BigDecimal bd : well) {
                input[index++] = bd.doubleValue();
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(ArrayUtils.subarray(input, begin, end));
            double resultDouble = stat.getPercentile(inputPercentile);

            BigDecimal result = new BigDecimal(resultDouble);

            resultMap.put(well, result);
        }

        for (WellBigDecimal well : plate) {

            BigDecimal result = resultMap.get(well);
            BigDecimal returned = returnedMap.get(well);

            BigDecimal[] corrected = correctRoundingErrors(result, returned);

            assertEquals(corrected[0], corrected[1]);
        }
    }
}

From source file:com.github.jessemull.microflex.stat.statbigdecimal.PercentileBigDecimalTest.java

/**
 * Tests the aggregated plate statistics method using the values between the indices of
 * the array./*from   w ww .  ja v a2 s.  c o  m*/
 */
@Test
public void testAggregatedSetArrayIndices() {

    int inputPercentile = 1 + random.nextInt(100);

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

    WellSetBigDecimal[] setArrayIndices = new WellSetBigDecimal[arrayIndices.length];

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

    Map<WellSetBigDecimal, BigDecimal> aggregatedReturnedMap = percentile.setsAggregated(setArrayIndices, begin,
            end - begin, inputPercentile);
    Map<WellSetBigDecimal, BigDecimal> aggregatedResultMap = new TreeMap<WellSetBigDecimal, BigDecimal>();

    for (WellSetBigDecimal set : setArrayIndices) {

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

        for (WellBigDecimal well : set) {
            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).doubleValue();
        }

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double resultAggregatedDouble = statAggregated.getPercentile(inputPercentile);

        BigDecimal aggregatedResult = new BigDecimal(resultAggregatedDouble);

        aggregatedResultMap.put(set, aggregatedResult);
    }

    for (WellSetBigDecimal plate : setArrayIndices) {

        BigDecimal result = aggregatedResultMap.get(plate);
        BigDecimal returned = aggregatedReturnedMap.get(plate);
        BigDecimal[] corrected = correctRoundingErrors(result, returned);

        assertEquals(corrected[0], corrected[1]);
    }
}

From source file:com.github.jessemull.microflex.stat.statbiginteger.PercentileBigIntegerTest.java

/**
 * Tests the aggregated plate statistics method using the values between the indices of
 * the collection.//from  ww  w  . j av a2s  . c  o  m
 */
@Test
public void testAggregatedSetCollectionIndices() {

    int inputPercentile = 1 + random.nextInt(100);

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

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

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

    Map<WellSetBigInteger, BigDecimal> aggregatedReturnedMap = percentile.setsAggregated(collection, begin,
            end - begin, inputPercentile);
    Map<WellSetBigInteger, BigDecimal> aggregatedResultMap = new TreeMap<WellSetBigInteger, BigDecimal>();

    for (WellSetBigInteger set : collection) {

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

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

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

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

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double resultAggregatedDouble = statAggregated.getPercentile(inputPercentile);

        BigDecimal aggregatedResult = new BigDecimal(resultAggregatedDouble);
        aggregatedResultMap.put(set, aggregatedResult);
    }

    for (WellSetBigInteger set : collection) {

        BigDecimal result = aggregatedResultMap.get(set);
        BigDecimal returned = aggregatedReturnedMap.get(set);
        BigDecimal[] corrected = correctRoundingErrors(result, returned);

        assertEquals(corrected[0], corrected[1]);
    }
}

From source file:com.iorga.webappwatcher.analyzer.ws.statistics.DailyStatisticsWS.java

@GET
@Path("/compute")
public StreamingOutput compute() throws ClassNotFoundException, IOException {
    final List<DayStatistic> computeDayStatistics = durationPerPrincipalStats.computeDayStatistics();
    return new StreamingOutput() {
        @Override/*www .  j a  va  2s  .com*/
        public void write(final OutputStream output) throws IOException, WebApplicationException {
            final JsonGenerator generator = OBJECT_MAPPER.getJsonFactory().createJsonGenerator(output,
                    JsonEncoding.UTF8);
            generator.writeStartArray();
            for (final DayStatistic dayStatistic : computeDayStatistics) {
                generator.writeStartObject();
                generator.writeFieldName("startDate");
                OBJECT_MAPPER.writeValue(generator, dayStatistic.getStartDate());
                generator.writeFieldName("endDate");
                OBJECT_MAPPER.writeValue(generator, dayStatistic.getEndDate());
                generator.writeArrayFieldStart("statistics");
                // write each statistic
                for (final String statisticType : new String[] { "distinctUsers", "numberOfRequests",
                        "durationsFor1clickSum", "durationsFor1clickMean", "durationsFor1clickMedian",
                        "durationsFor1click90c", "durationsFor1clickMin", "durationsFor1clickMax" }) {
                    //                  generator.writeFieldName(statisticType);
                    generator.writeStartObject();
                    generator.writeStringField("type", statisticType);
                    // get the statistics from the dayStatistic
                    DescriptiveStatistics descriptiveStatistics;
                    try {
                        descriptiveStatistics = (DescriptiveStatistics) dayStatistic.getClass()
                                .getMethod("get" + StringUtils.capitalize(statisticType)).invoke(dayStatistic);
                    } catch (IllegalAccessException | IllegalArgumentException | InvocationTargetException
                            | NoSuchMethodException | SecurityException e) {
                        throw new IOException(
                                "Problem while invoking getter for statisticType " + statisticType, e);
                    }
                    generator.writeNumberField("min", descriptiveStatistics.getMin());
                    generator.writeNumberField("max", descriptiveStatistics.getMax());
                    generator.writeNumberField("mean", descriptiveStatistics.getMean());
                    generator.writeNumberField("median", descriptiveStatistics.getPercentile(50));
                    generator.writeEndObject();
                }
                generator.writeEndArray();
                generator.writeFieldName("distinctPrincipalsSize");
                OBJECT_MAPPER.writeValue(generator, dayStatistic.getPrincipals().size());
                generator.writeEndObject();
            }
            generator.writeEndArray();

            generator.flush(); // required else all the stream is not sent
        }
    };
}

From source file:com.github.jessemull.microflex.stat.statbiginteger.PercentileBigIntegerTest.java

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

    for (PlateBigInteger plate : arrayIndices) {

        int inputPercentile = 1 + random.nextInt(100);

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

        Map<WellBigInteger, BigDecimal> resultMap = new TreeMap<WellBigInteger, BigDecimal>();
        Map<WellBigInteger, BigDecimal> returnedMap = percentile.plate(plate, begin, end - begin,
                inputPercentile);

        for (WellBigInteger well : plate) {

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

            for (BigInteger bi : well) {
                input[index++] = bi.doubleValue();
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(ArrayUtils.subarray(input, begin, end));
            double resultDouble = stat.getPercentile(inputPercentile);

            BigDecimal result = new BigDecimal(resultDouble);

            resultMap.put(well, result);
        }

        for (WellBigInteger well : plate) {

            BigDecimal result = resultMap.get(well);
            BigDecimal returned = returnedMap.get(well);

            BigDecimal[] corrected = correctRoundingErrors(result, returned);

            assertEquals(corrected[0], corrected[1]);
        }
    }
}

From source file:com.github.jessemull.microflex.stat.statbiginteger.PercentileBigIntegerTest.java

/**
 * Tests set calculation using indices.//from  w w w.  ja  v a2  s .  co  m
 */
@Test
public void testSetIndices() {

    for (PlateBigInteger plate : arrayIndices) {

        int inputPercentile = 1 + random.nextInt(100);

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

        Map<WellBigInteger, BigDecimal> resultMap = new TreeMap<WellBigInteger, BigDecimal>();
        Map<WellBigInteger, BigDecimal> returnedMap = percentile.set(plate.dataSet(), begin, end - begin,
                inputPercentile);

        for (WellBigInteger well : plate) {

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

            for (BigInteger bi : well) {
                input[index++] = bi.doubleValue();
            }

            DescriptiveStatistics stat = new DescriptiveStatistics(ArrayUtils.subarray(input, begin, end));
            double resultDouble = stat.getPercentile(inputPercentile);

            BigDecimal result = new BigDecimal(resultDouble);

            resultMap.put(well, result);
        }

        for (WellBigInteger well : plate) {

            BigDecimal result = resultMap.get(well);
            BigDecimal returned = returnedMap.get(well);

            BigDecimal[] corrected = correctRoundingErrors(result, returned);

            assertEquals(corrected[0], corrected[1]);
        }
    }
}

From source file:com.github.jessemull.microflex.stat.statbiginteger.PercentileBigIntegerTest.java

/**
 * Tests the aggregated plate statistics method using the values between the indices of
 * the array.// ww  w .j a va 2s  . com
 */
@Test
public void testAggregatedSetArrayIndices() {

    int inputPercentile = 1 + random.nextInt(100);

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

    WellSetBigInteger[] setArrayIndices = new WellSetBigInteger[arrayIndices.length];

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

    Map<WellSetBigInteger, BigDecimal> aggregatedReturnedMap = percentile.setsAggregated(setArrayIndices, begin,
            end - begin, inputPercentile);
    Map<WellSetBigInteger, BigDecimal> aggregatedResultMap = new TreeMap<WellSetBigInteger, BigDecimal>();

    for (WellSetBigInteger set : setArrayIndices) {

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

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

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

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

        DescriptiveStatistics statAggregated = new DescriptiveStatistics(inputAggregated);
        double resultAggregatedDouble = statAggregated.getPercentile(inputPercentile);

        BigDecimal aggregatedResult = new BigDecimal(resultAggregatedDouble);

        aggregatedResultMap.put(set, aggregatedResult);
    }

    for (WellSetBigInteger plate : setArrayIndices) {

        BigDecimal result = aggregatedResultMap.get(plate);
        BigDecimal returned = aggregatedReturnedMap.get(plate);
        BigDecimal[] corrected = correctRoundingErrors(result, returned);

        assertEquals(corrected[0], corrected[1]);
    }
}

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.ja v  a 2  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");
}