Examples of org.apache.commons.math3.stat.descriptive.DescriptiveStatistics.addValue()

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

Class org.apache.commons.math3.stat.descriptive.DescriptiveStatistics

Examples of org.apache.commons.math3.stat.descriptive.DescriptiveStatistics.addValue()

org.apache.commons.math3.stat.descriptive.DescriptiveStatistics.addValue()
Adds the value to the dataset. If the dataset is at the maximum size (i.e., the number of stored elements equals the currently configured windowSize), the first (oldest) element in the dataset is discarded to make room for the new value. @param v the value to be added

            } else if (val < 0.75) {
                value = 2;
            } else {
                value = 3;
            }
            freq.addValue(value);
        }
        long[] observed = new long[4];
        for (int i=0; i<4; i++) {
            observed[i] = freq.getCount(i);
        }

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    public static double[] normalize(final double[] sample) {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        // Add the data from the series to stats
        for (int i = 0; i < sample.length; i++) {
            stats.addValue(sample[i]);
        }


        // Compute mean and standard deviation
        double mean = stats.getMean();
        double standardDeviation = stats.getStandardDeviation();

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        double standardizedSample[] = StatUtils.normalize(sample);


        DescriptiveStatistics stats = new DescriptiveStatistics();
        // Add the data from the array
        for (int i = 0; i < length; i++) {
            stats.addValue(standardizedSample[i]);
        }
        // the calculations do have a limited precision    
        double distance = 1E-10;
        // check the mean an standard deviation
        Assert.assertEquals(0.0, stats.getMean(), distance);

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        line = in.readLine();


        while (line != null) {
            if (d != null) {
                d.addValue(Double.parseDouble(line.trim()));
            }  else {
                s.addValue(Double.parseDouble(line.trim()));
            }
            line = in.readLine();
        }

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        StableRandomGenerator generator = new StableRandomGenerator(rg, 1d, 0.0);
        DescriptiveStatistics summary = new DescriptiveStatistics();


        for (int i = 0; i < sampleSize; ++i) {
            double sample = generator.nextNormalizedDouble();
            summary.addValue(sample);
        }


        // Standard Cauchy distribution should have zero median and mode
        double median = summary.getPercentile(50);
        Assert.assertEquals(0.0, median, 0.2);

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    public static double[] normalize(final double[] sample) {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        // Add the data from the series to stats
        for (int i = 0; i < sample.length; i++) {
            stats.addValue(sample[i]);
        }


        // Compute mean and standard deviation
        double mean = stats.getMean();
        double standardDeviation = stats.getStandardDeviation();

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        line = in.readLine();


        while (line != null) {
            if (d != null) {
                d.addValue(Double.parseDouble(line.trim()));
            }  else {
                s.addValue(Double.parseDouble(line.trim()));
            }
            line = in.readLine();
        }

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        double standardizedSample[] = StatUtils.normalize(sample);


        DescriptiveStatistics stats = new DescriptiveStatistics();
        // Add the data from the array
        for (int i = 0; i < length; i++) {
            stats.addValue(standardizedSample[i]);
        }
        // the calculations do have a limited precision    
        double distance = 1E-10;
        // check the mean an standard deviation
        Assert.assertEquals(0.0, stats.getMean(), distance);

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    public static double[] normalize(final double[] sample) {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        // Add the data from the series to stats
        for (int i = 0; i < sample.length; i++) {
            stats.addValue(sample[i]);
        }


        // Compute mean and standard deviation
        double mean = stats.getMean();
        double standardDeviation = stats.getStandardDeviation();

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        {
            DescriptiveStatistics statistics = new DescriptiveStatistics();
            for (int i = 0; i < accumulators.size(); i++) {
                BlockCursor cursor = accumulators.get(i).evaluateFinal().cursor();
                checkArgument(cursor.advanceNextPosition(), "accumulator returned no results");
                statistics.addValue(getNumeric(cursor));
            }


            BlockBuilder builder = new BlockBuilder(SINGLE_VARBINARY);
            builder.append(formatApproximateOutput(statistics, confidence));
            return builder.build();

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