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.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. j a va 2 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:com.caseystella.analytics.outlier.batch.rpca.RPCAOutlierAlgorithm.java

public double outlierScore(List<DataPoint> dataPoints, DataPoint value) {
    double[] inputData = new double[dataPoints.size() + 1];
    int numNonZero = 0;
    if (scaling != ScalingFunctions.NONE) {
        int i = 0;
        final DescriptiveStatistics stats = new DescriptiveStatistics();
        for (DataPoint dp : dataPoints) {
            inputData[i++] = dp.getValue();

            stats.addValue(dp.getValue());
            numNonZero += dp.getValue() > EPSILON ? 1 : 0;
        }/* www .j a v a2s . co  m*/
        inputData[i] = value.getValue();
        GlobalStatistics globalStats = new GlobalStatistics() {
            {
                setMax(stats.getMax());
                setMin(stats.getMin());
                setMax(stats.getMean());
                setStddev(stats.getStandardDeviation());
            }
        };
        for (i = 0; i < inputData.length; ++i) {
            inputData[i] = scaling.scale(inputData[i], globalStats);
        }
    } else {
        int i = 0;
        for (DataPoint dp : dataPoints) {
            inputData[i++] = dp.getValue();
            numNonZero += dp.getValue() > EPSILON ? 1 : 0;
        }
        inputData[i] = value.getValue();
    }
    int nCols = 1;
    int nRows = inputData.length;
    if (numNonZero > minRecords) {
        AugmentedDickeyFuller dickeyFullerTest = new AugmentedDickeyFuller(inputData);
        double[] inputArrayTransformed = inputData;
        if (!this.isForceDiff && dickeyFullerTest.isNeedsDiff()) {
            // Auto Diff
            inputArrayTransformed = dickeyFullerTest.getZeroPaddedDiff();
        } else if (this.isForceDiff) {
            // Force Diff
            inputArrayTransformed = dickeyFullerTest.getZeroPaddedDiff();
        }

        if (this.spenalty == null) {
            this.lpenalty = this.LPENALTY_DEFAULT;
            this.spenalty = this.SPENALTY_DEFAULT / Math.sqrt(Math.max(nCols, nRows));
        }

        // Calc Mean
        double mean = 0;
        for (int n = 0; n < inputArrayTransformed.length; n++) {
            mean += inputArrayTransformed[n];
        }
        mean /= inputArrayTransformed.length;

        // Calc STDEV
        double stdev = 0;
        for (int n = 0; n < inputArrayTransformed.length; n++) {
            stdev += Math.pow(inputArrayTransformed[n] - mean, 2);
        }
        stdev = Math.sqrt(stdev / (inputArrayTransformed.length - 1));

        // Transformation: Zero Mean, Unit Variance
        for (int n = 0; n < inputArrayTransformed.length; n++) {
            inputArrayTransformed[n] = (inputArrayTransformed[n] - mean) / stdev;
        }

        // Read Input Data into Array
        // Read Input Data into Array
        double[][] input2DArray = new double[nRows][nCols];
        input2DArray = VectorToMatrix(inputArrayTransformed, nRows, nCols);

        RPCA rSVD = new RPCA(input2DArray, this.lpenalty, this.spenalty);

        double[][] outputE = rSVD.getE().getData();
        double[][] outputS = rSVD.getS().getData();
        double[][] outputL = rSVD.getL().getData();
        return outputS[nRows - 1][0];
    } else {
        return Double.NaN;
    }
}

---

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));
    }//ww w . j  a va 2 s  . co 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;
}

---

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/*from  ww  w .  jav  a2 s  .c om*/
        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:mase.app.allocation.AllocationProblem.java

@Override
public EvaluationResult[] evaluateSolution(GroupController gc, long seed) {
    AgentController[] acs = gc.getAgentControllers(numAgents);
    RealMatrix distanceMatrix = new Array2DRowRealMatrix(numAgents, types.length);
    for (int i = 0; i < numAgents; i++) {
        AllocationAgent aa = (AllocationAgent) acs[i];
        for (int j = 0; j < types.length; j++) {
            distanceMatrix.setEntry(i, j, DIST.compute(aa.getLocation(), types[j]));
        }//from ww  w.  java  2  s .c o  m
    }

    DescriptiveStatistics pd = pairDistances(distanceMatrix);

    // fitness
    FitnessResult fr = new FitnessResult(1 - pd.getMean() / FastMath.sqrt(dimensions));

    // individual characterisation -- distance to each type
    List<EvaluationResult> vbrs = new ArrayList<>();
    for (double[] dists : distanceMatrix.getData()) {
        vbrs.add(new VectorBehaviourResult(dists));
    }
    CompoundEvaluationResult ser = new CompoundEvaluationResult(vbrs);

    // aux characterisation -- min, mean, max, sd pair distances
    VectorBehaviourResult aux = new VectorBehaviourResult(pd.getMin(), pd.getMean(), pd.getMax(),
            pd.getStandardDeviation());

    return new EvaluationResult[] { fr, aux, ser };
}

---

From source file:knop.psfj.BeadAverager.java

/**
 * Average bead./*from  w w w. ja v  a2s .c o m*/
 *
 * @param frameList the frame list
 * @return the image plus
 */
public ImagePlus averageBead(ArrayList<BeadFrame> frameList) {

    ImagePlus result;
    DescriptiveStatistics centerX = new DescriptiveStatistics();
    DescriptiveStatistics centerY = new DescriptiveStatistics();
    DescriptiveStatistics centerZ = new DescriptiveStatistics();

    int originalStackSize = frameList.get(0).getSliceNumber();

    setTotalBeads(frameList.size());

    //frameList = filter(frameList);

    ArrayList<ImageStack> centeredStacks = new ArrayList<ImageStack>();

    for (BeadFrame frame : frameList) {

        System.out.println(frame.getCentroidXAsInt());
        centerX.addValue(frame.getCentroidXAsInt());
        centerY.addValue(frame.getCentroidYAsInt());
        centerZ.addValue(frame.getCentroidZAsInt());

    }

    int chosenCenter = MathUtils.round(centerZ.getPercentile(50));
    int maxUp = chosenCenter - MathUtils.round(chosenCenter - centerZ.getMin());
    int maxDown = originalStackSize - MathUtils.round(centerZ.getMax());

    System.out.println(maxUp);
    System.out.println(maxDown);

    for (BeadFrame frame : frameList) {
        ImageStack subStack = new ImageStack(frame.getSubstack().getWidth(), frame.getSubstack().getHeight());
        int center = frame.getCentroidZAsInt();
        int begin = center - maxUp;
        int end = center + maxDown;
        System.out.println(String.format(" Groing from %d to %d with a center at %d", begin, end, center));
        for (int i = begin; i != end; i++) {
            subStack.addSlice(frame.getSubstack().getImageStack().getProcessor(i + 1));
        }

        centeredStacks.add(subStack);
        //new ImagePlus("",subStack).show();

    }

    System.out.println(centerX);
    System.out.println(centerY);
    System.out.println(centerZ);

    return new ImagePlus("", averageStacks(centeredStacks));

}

---

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

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

    for (PlateInteger plate : array) {

        Map<WellInteger, Double> resultMap = new TreeMap<WellInteger, Double>();
        Map<WellInteger, Double> returnedMap = min.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.getMin();

            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.MinTest.java

/**
 * Tests the plate statistics method./*from   w  w w . jav  a2  s.co  m*/
 */
@Test
public void testPlate() {

    for (Plate plate : array) {

        Map<Well, Double> resultMap = new TreeMap<Well, Double>();
        Map<Well, Double> returnedMap = min.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.getMin();

            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.MinDoubleTest.java

/**
 * Tests the plate statistics method.//from w w  w  .ja  v a2 s  .  co  m
 */
@Test
public void testPlate() {

    for (PlateDouble plate : array) {

        Map<WellDouble, Double> resultMap = new TreeMap<WellDouble, Double>();
        Map<WellDouble, Double> returnedMap = min.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.getMin();

            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.MinIntegerTest.java

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

    for (PlateInteger plate : array) {

        Map<WellInteger, Double> resultMap = new TreeMap<WellInteger, Double>();
        Map<WellInteger, Double> returnedMap = min.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.getMin();

            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);
        }
    }

}

---

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