Java Code Examples for org.apache.commons.math3.stat.descriptive.DescriptiveStatistics#getStandardDeviation()

/**
 * Find local maxima in the spectrum and returns the indices of those maxima as integer
 * array.
 *
 * @param threshold Threshold for search. Values bellow that threshold won't be considered.
 * @return Array containing indices (zero-based) of local maxima.
 */
public List<Pair<Float, Double>> findLocalMaxima(double threshold, boolean significant) {
    List<Pair<Float,Double>> peaks = new ArrayList<>();
    for (int i=1;i<this.spectrum.length-1;i++) {
        if (this.spectrum[i] < threshold) {
          continue;
        }
        if (spectrum[i] > Math.max(spectrum[i+1], spectrum[i-1])) {
          peaks.add(this.get(i));
        }
    }

    if (significant) {
        DescriptiveStatistics statistics = new DescriptiveStatistics();
        for (Pair<Float, Double> peak : peaks) {
            statistics.addValue(peak.second);
        }
        final double mean = statistics.getMean();
        final double stddev = statistics.getStandardDeviation();
        peaks.removeIf(p -> p.second < (mean + stddev * 2));
    }

    return peaks;
}
 

private String printStat(DescriptiveStatistics stats) {
    double mean = stats.getMean();
    double std = stats.getStandardDeviation();
    double median = stats.getPercentile(50);
    double q1 = stats.getPercentile(25);
    double q3 = stats.getPercentile(75);
    double iqr = q3 - q1;
    double trimmedMean = (q1 + q3 + 2 * median) / 4;
    double skewness = stats.getSkewness();

    StringBuilder sb = new StringBuilder();
    sb.append(" *** Distribution Analysis ***").append("\n")
            .append("\tMean= ").append(mean).append("\n")
            .append("\tStd= ").append(std).append("\n")
            .append("\tMedian= ").append(median).append("\n")
            .append("\tQ1= ").append(q1).append("\tQ3=").append(q3).append("\tIQR=").append(iqr).append("\n")
            .append("\tTrimmed Mean= ").append(trimmedMean).append("\n")
            .append("\tSkewness= ").append(skewness).append("\n");
    return sb.toString();
}
 

/**
 * Normalize (standardize) the sample, so it is has a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
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();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

/**
 * Normalize (standardize) the series, so in the end it is having a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
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();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

/**
 * Normalize (standardize) the series, so in the end it is having a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
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();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

/**
 * Normalize (standardize) the sample, so it is has a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
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();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

/**
 * Normalize (standardize) the series, so in the end it is having a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
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();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

/**
 * Normalize (standardize) the sample, so it is has a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
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();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

/**
 * Normalize (standardize) the sample, so it is has a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
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();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

/**
 * Normalize (standardize) the sample, so it is has a mean of 0 and a standard deviation of 1.
 *
 * @param sample Sample to normalize.
 * @return normalized (standardized) sample.
 * @since 2.2
 */
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();

    // initialize the standardizedSample, which has the same length as the sample
    double[] standardizedSample = new double[sample.length];

    for (int i = 0; i < sample.length; i++) {
        // z = (x- mean)/standardDeviation
        standardizedSample[i] = (sample[i] - mean) / standardDeviation;
    }
    return standardizedSample;
}
 

public void getDescStats(double[] values){
	DescriptiveStatistics stats = new DescriptiveStatistics();
	for( int i = 0; i < values.length; i++) {
	        stats.addValue(values[i]);
	}
	double mean = stats.getMean();
	double std = stats.getStandardDeviation();
	double median = stats.getPercentile(50);
	System.out.println(mean + "\t" + std + "\t" + median);
}
 

/**
 * Returns z-scores for B-factors (normalized B-factors).
 * 
 * Critical z-score values: Confidence level Tail Area z critical 
 *                          90%              0.05      +-1.645 
 *                          95%              0.025     +-1.96 
 *                          99%              0.005     +-2.576
 * 
 * @return
 */
public float[] getNormalizedbFactors() {
    if (normalizedbFactors == null) {
        normalizedbFactors = new float[getNumAtoms()];

        float[] bFactors = getbFactors();
        String[] types = getEntityTypes();

        DescriptiveStatistics stats = new DescriptiveStatistics();
        for (int i = 0; i < getNumAtoms(); i++) {
            if (! (types[i].equals("WAT"))) {
                stats.addValue(bFactors[i]);
            }
        }
        double mean = stats.getMean();
        double stddev = stats.getStandardDeviation();

        if (stddev > EPSILON) {
            for (int i = 0; i < getNumAtoms(); i++) {
                normalizedbFactors[i] = (float) ((bFactors[i] - mean) / stddev);
            }
        } else {
            Arrays.fill(normalizedbFactors, Float.MAX_VALUE);
        }
    }

    return normalizedbFactors;
}
 

private double[] normalize(double[] array) {
    DescriptiveStatistics stats = new DescriptiveStatistics(array);
    double mean = stats.getMean();
    double stdDev = stats.getStandardDeviation();
    for (int i = 0; i < array.length; i++) {
        array[i] = (array[i] - mean)/stdDev;
    }
    return array;
}
 

@Override
protected void calculateActualResult(JmeterTransactions jmeterTransactions) {
    DescriptiveStatistics ds = new DescriptiveStatistics();
    jmeterTransactions.asStream()
            .map(t -> (double) t.getElapsed())
            .forEach(ds::addValue);
    actualResult = (int) ds.getStandardDeviation();
}
 

private double standardDeviation(double[][] x) {
	DescriptiveStatistics stats = new DescriptiveStatistics();
	for (int i = 0; i < x.length; i ++)
		for (int j = 0; j < x[i].length; j++)
			stats.addValue(x[i][j]);
	return stats.getStandardDeviation();
}