/*
* GRAL: GRAphing Library for Java(R)
*
* (C) Copyright 2009-2016 Erich Seifert ,
* Michael Seifert
*
* This file is part of GRAL.
*
* GRAL is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* GRAL is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with GRAL. If not, see statistics;
/**
* Initializes a new object with the specified data values.
* @param data Data to be analyzed.
*/
public Statistics(Iterable> data) {
statistics = new HashMap();
this.data = data;
}
/**
* Utility method that calculates basic statistics like element count, sum,
* or mean.
*
* Notes: Calculation of higher order statistics is based on formulas from
* http://people.xiph.org/~tterribe/notes/homs.html
*
* @param data Data values used to calculate statistics
* @param stats A {@code Map} that should store the new statistics.
*/
private void createBasicStats(Iterable> data, Map stats) {
double n = 0.0;
double sum = 0.0;
double sum2 = 0.0;
double sum3 = 0.0;
double sum4 = 0.0;
double mean = 0.0;
double sumOfDiffSquares = 0.0;
double sumOfDiffCubics = 0.0;
double sumOfDiffQuads = 0.0;
for (Comparable cell : data) {
if (!(cell instanceof Number)) {
continue;
}
Number numericCell = (Number) cell;
if (!MathUtils.isCalculatable(numericCell)) {
continue;
}
double val = numericCell.doubleValue();
if (!stats.containsKey(MIN) || val < stats.get(MIN)) {
stats.put(MIN, val);
}
if (!stats.containsKey(MAX) || val > stats.get(MAX)) {
stats.put(MAX, val);
}
n++;
double val2 = val*val;
sum += val;
sum2 += val2;
sum3 += val2*val;
sum4 += val2*val2;
double delta = val - mean;
double deltaN = delta/n;
double deltaN2 = deltaN*deltaN;
double term1 = delta*deltaN*(n - 1.0);
mean += deltaN;
sumOfDiffQuads += term1*deltaN2*(n*n - 3.0*n + 3.0) +
6.0*deltaN2*sumOfDiffSquares - 4.0*deltaN*sumOfDiffCubics;
sumOfDiffCubics += term1*deltaN*(n - 2.0) -
3.0*deltaN*sumOfDiffSquares;
sumOfDiffSquares += term1;
}
stats.put(N, n);
stats.put(SUM, sum);
stats.put(SUM2, sum2);
stats.put(SUM3, sum3);
stats.put(SUM4, sum4);
stats.put(MEAN, mean);
stats.put(SUM_OF_DIFF_QUADS, sumOfDiffQuads);
stats.put(SUM_OF_DIFF_CUBICS, sumOfDiffCubics);
stats.put(SUM_OF_DIFF_SQUARES, sumOfDiffSquares);
stats.put(VARIANCE, sumOfDiffSquares/(n - 1.0));
stats.put(POPULATION_VARIANCE, sumOfDiffSquares/n);
stats.put(SKEWNESS,
(sumOfDiffCubics/n)/Math.pow(sumOfDiffSquares/n, 3.0/2.0) - 3.0);
stats.put(KURTOSIS,
(n*sumOfDiffQuads)/(sumOfDiffSquares*sumOfDiffSquares) - 3.0);
}
/**
* Utility method that calculates quantiles for the given data values and
* stores the results in {@code stats}.
* @param stats {@code Map} for storing results
* @see de.erichseifert.gral.util.MathUtils#quantile(java.util.List,double)
*/
private void createDistributionStats(Iterable> data, Map stats) {
// Create sorted list of data
List values = new SortedList();
for (Comparable cell : data) {
if (!(cell instanceof Number)) {
continue;
}
Number numericCell = (Number) cell;
double value = numericCell.doubleValue();
if (MathUtils.isCalculatable(value)) {
values.add(value);
}
}
if (values.size() <= 0) {
return;
}
stats.put(QUARTILE_1, MathUtils.quantile(values, 0.25));
stats.put(QUARTILE_2, MathUtils.quantile(values, 0.50));
stats.put(QUARTILE_3, MathUtils.quantile(values, 0.75));
stats.put(MEDIAN, stats.get(QUARTILE_2));
}
/**
* Returns the specified statistics value.
* @param key Requested information.
* @return The value for the specified key as value, or NaN
* if the specified statistical value does not exist
*/
public double get(String key) {
if (!statistics.containsKey(key)) {
if (MEDIAN.equals(key) || QUARTILE_1.equals(key) ||
QUARTILE_2.equals(key) || QUARTILE_3.equals(key)) {
createDistributionStats(data, statistics);
} else {
createBasicStats(data, statistics);
}
}
Double v = statistics.get(key);
return DataUtils.getValueOrDefault(v, Double.NaN);
}
}
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