Examples of org.apache.commons.math3.stat.descriptive.SummaryStatistics

Package org.apache.commons.math3.stat.descriptive

Examples of org.apache.commons.math3.stat.descriptive.SummaryStatistics

org.apache.commons.math3.stat.descriptive.SummaryStatistics

Computes summary statistics for a stream of data values added using the {@link #addValue(double) addValue} method. The data values are not stored inmemory, so this class can be used to compute statistics for very large data streams.

The {@link StorelessUnivariateStatistic} instances used to maintain summarystate and compute statistics are configurable via setters. For example, the default implementation for the variance can be overridden by calling {@link #setVarianceImpl(StorelessUnivariateStatistic)}. Actual parameters to these methods must implement the {@link StorelessUnivariateStatistic}interface and configuration must be completed before addValue is called. No configuration is necessary to use the default, commons-math provided implementations.

Note: This class is not thread-safe. Use {@link SynchronizedSummaryStatistics} if concurrent access from multiplethreads is required.

        // Initialize binStats ArrayList
        if (!binStats.isEmpty()) {
            binStats.clear();
        }
        for (int i = 0; i < binCount; i++) {
            SummaryStatistics stats = new SummaryStatistics();
            binStats.add(i,stats);
        }


        // Filling data in binStats Array
        da.computeBinStats();

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        final double x = randomData.nextUniform(0,1);


        // Use this to select the bin and generate a Gaussian within the bin
        for (int i = 0; i < binCount; i++) {
           if (x <= upperBounds[i]) {
               SummaryStatistics stats = binStats.get(i);
               if (stats.getN() > 0) {
                   if (stats.getStandardDeviation() > 0) {  // more than one obs
                       return getKernel(stats).sample();
                   } else {
                       return stats.getMean(); // only one obs in bin
                   }
               }
           }
        }
        throw new MathIllegalStateException(LocalizedFormats.NO_BIN_SELECTED);

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        public void computeBinStats() throws IOException {
            String str = null;
            double val = 0.0d;
            while ((str = inputStream.readLine()) != null) {
                val = Double.parseDouble(str);
                SummaryStatistics stats = binStats.get(findBin(val));
                stats.addValue(val);
            }


            inputStream.close();
            inputStream = null;
        }

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        /** {@inheritDoc} */
        @Override
        public void computeStats() throws IOException {
            String str = null;
            double val = 0.0;
            sampleStats = new SummaryStatistics();
            while ((str = inputStream.readLine()) != null) {
                val = Double.valueOf(str).doubleValue();
                sampleStats.addValue(val);
            }
            inputStream.close();

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        }


        /** {@inheritDoc} */
        @Override
        public void computeStats() throws IOException {
            sampleStats = new SummaryStatistics();
            for (int i = 0; i < inputArray.length; i++) {
                sampleStats.addValue(inputArray[i]);
            }
        }

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        /** {@inheritDoc} */
        @Override
        public void computeBinStats() throws IOException {
            for (int i = 0; i < inputArray.length; i++) {
                SummaryStatistics stats =
                    binStats.get(findBin(inputArray[i]));
                stats.addValue(inputArray[i]);
            }
        }

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import static org.junit.Assert.assertEquals;


public class OptimizedStatisticsTest {
    @Test
    public void checkConsistency() {
        final SummaryStatistics normal = new SummaryStatistics(); // considered as reference
        final OptimizedStatistics optimized = new OptimizedStatistics();


        for (int i = 0; i < 100; i++) {
            final double value = Math.random() * 100;
            normal.addValue(value);
            optimized.addValue(value);


            doAssert(normal, optimized); // important to assert for 0, 1 and n > 1
        }
    }

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    private Conditions conditions;
    private List<BigDecimal> profitCurve;
    private SummaryStatistics stats;


    public Session(Account account, final Conditions conditions) {
        stats = new SummaryStatistics();


        profitCurve = new ArrayList<BigDecimal>();


        trades = new ArrayList<Trade>();
        this.account = account;

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        double next = 0.0;
        double tolerance = 0.1;
        vs.computeDistribution();
        Assert.assertTrue("empirical distribution property",
            vs.getEmpiricalDistribution() != null);
        SummaryStatistics stats = new SummaryStatistics();
        for (int i = 1; i < 1000; i++) {
            next = vs.getNext();
            stats.addValue(next);
        }
        Assert.assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance);
        Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(),
            tolerance);


        vs.computeDistribution(500);
        stats = new SummaryStatistics();
        for (int i = 1; i < 1000; i++) {
            next = vs.getNext();
            stats.addValue(next);
        }
        Assert.assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance);
        Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(),
            tolerance);
    }

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        public void computeBinStats() throws IOException {
            String str = null;
            double val = 0.0d;
            while ((str = inputStream.readLine()) != null) {
                val = Double.parseDouble(str);
                SummaryStatistics stats = binStats.get(findBin(val));
                stats.addValue(val);
            }


            inputStream.close();
            inputStream = null;
        }

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org.apache.commons.math3.analysis.function.Sin

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