Java DescriptiveStatistics.getSum方法代码示例

import org.apache.commons.math.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
public Feature buildFeatureObject(DescriptiveStatistics summary,String name){
    double geometricMean = summary.getGeometricMean();
    double kurtosis = summary.getKurtosis();
    double max = summary.getMax();
    double mean = summary.getMean();
    double min = summary.getMin();
    double skewness = summary.getSkewness();
    double standardDeviation = summary.getStandardDeviation();
    double sum = summary.getSum();
    double sumsq = summary.getSumsq();
    double variance = summary.getVariance();
    double[] values = summary.getValues();
    
    
    Feature feature=new Feature(name, name, null, mean, variance, skewness);
    
    
    
   LOG.log(Level.INFO, summary.toString());
   
    
    return feature;        
}
 

import org.apache.commons.math.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
public double[] getBin_spectrum(int shift) {
    ArrayList<Double> bin_spec_al = new ArrayList<Double>();
    double binSize = (fragment_tolerance * 2),
            upperLimit = max_value + 0.00001;
    for (double lowerLimit = min_value; lowerLimit < upperLimit; lowerLimit = lowerLimit + binSize) {
        double tmp_intensity_bin = 0;
        DescriptiveStatistics obj = new DescriptiveStatistics();
        for (Peak p : peakList) {
            double mz = p.getMz() + shift;
            if (mz >= lowerLimit && mz < lowerLimit + binSize) {
                obj.addValue(p.intensity);
            }
        }
        if (obj.getN() > 0) {
            if (intensities_sum_or_mean_or_median == 0) {
                tmp_intensity_bin = obj.getSum();
            } else if (intensities_sum_or_mean_or_median == 1) {
                tmp_intensity_bin = obj.getMean();
            } else if (intensities_sum_or_mean_or_median == 2) {
                tmp_intensity_bin = obj.getPercentile(50);
            }
        }
        // put every bin_pectrum
        bin_spec_al.add(tmp_intensity_bin);
    }
    // convert an arraylist to double array
    // initiate size of array
    bin_size = bin_spec_al.size();
    double[] bin_spectrum = new double[bin_spec_al.size()];
    for (int i = 0; i < bin_spec_al.size(); i++) {
        bin_spectrum[i] = bin_spec_al.get(i);
    }
    return bin_spectrum;
}
 

import org.apache.commons.math.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
/**
 * Function to compute a scaled cost using {@link DescriptiveStatistics}. It
 * assumes that this is a zero sum set of costs.  It assumes that the worst case
 * possible is all of the elements in one region server and the rest having 0.
 *
 * @param stats the costs
 * @return a scaled set of costs.
 */
double costFromStats(DescriptiveStatistics stats) {
  double totalCost = 0;
  double mean = stats.getMean();

  //Compute max as if all region servers had 0 and one had the sum of all costs.  This must be
  // a zero sum cost for this to make sense.
  double max = ((stats.getN() - 1) * stats.getMean()) + (stats.getSum() - stats.getMean());
  for (double n : stats.getValues()) {
    totalCost += Math.abs(mean - n);

  }

  return scale(0, max, totalCost);
}
 

import org.apache.commons.math.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
private ArrayList<double[]> prepareBinSpectra() {
    // first prepare bin-spectrum to be filled with zero
    int size = (2 * correctionFactor) + 1;

    ArrayList<double[]> shiftedSpectra = new ArrayList<double[]>(size);
    for (int i = 0; i < size; i++) {
        double[] shiftedSpectrum = new double[bin_size];
        shiftedSpectra.add(shiftedSpectrum);
    }
    // now fill each bin spectrum with correct mz values.
    double binSize = (fragment_tolerance * 2),
            upperLimit = max_value + 0.00001;
    int current_index = 0;
    for (double lowerLimit = min_value + correctionFactor; lowerLimit < upperLimit - correctionFactor; lowerLimit = lowerLimit + binSize) {
        double tmp_intensity_bin = 0;
        DescriptiveStatistics obj = new DescriptiveStatistics();
        for (Peak p : peakList) {
            double mz = p.getMz();
            if (mz >= lowerLimit && mz < lowerLimit + binSize) {
                obj.addValue(p.intensity);
            }
        }
        if (obj.getN() > 0) {
            if (intensities_sum_or_mean_or_median == 0) {
                tmp_intensity_bin = obj.getSum();
            } else if (intensities_sum_or_mean_or_median == 1) {
                tmp_intensity_bin = obj.getMean();
            } else if (intensities_sum_or_mean_or_median == 2) {
                tmp_intensity_bin = obj.getPercentile(50);
            }
        }
        // put every bin_pectrum            
        int filling_index = current_index;
        // check every bin spectrum            
        for (double[] shifted : shiftedSpectra) {
            shifted[filling_index] = tmp_intensity_bin;
            filling_index++;
        }
        current_index++;
    }
    return shiftedSpectra;
}