/**
* Generic bootstrap resampling. Quite optimized - Don't be afraid to try it. Executes
* <tt>resamples</tt> resampling steps. In each resampling step does the following:
*
* <ul>
* <li>Uniformly samples (chooses) <tt>size()</tt> random elements <i>with replacement</i> from
* <tt>this</tt> and fills them into an auxiliary bin <tt>b1</tt>.
* <li>Uniformly samples (chooses) <tt>other.size()</tt> random elements <i>with replacement</i>
* from <tt>other</tt> and fills them into another auxiliary bin <tt>b2</tt>.
* <li>Executes the comparison function <tt>function</tt> on both auxiliary bins
* (<tt>function.apply(b1,b2)</tt>) and adds the result of the function to an auxiliary
* bootstrap bin <tt>b3</tt>.
* </ul>
*
* <p>Finally returns the auxiliary bootstrap bin <tt>b3</tt> from which the measure of interest
* can be read off.
*
* <p><b>Background:</b>
*
* <p>Also see a more <A HREF="http://garnet.acns.fsu.edu/~pkelly/bootstrap.html"> in-depth
* discussion</A> on bootstrapping and related randomization methods. The classical statistical
* test for comparing the means of two samples is the <i>t-test</i>. Unfortunately, this test
* assumes that the two samples each come from a normal distribution and that these distributions
* have the same standard deviation. Quite often, however, data has a distribution that is
* non-normal in many ways. In particular, distributions are often unsymmetric. For such data, the
* t-test may produce misleading results and should thus not be used. Sometimes asymmetric data
* can be transformed into normally distributed data by taking e.g. the logarithm and the t-test
* will then produce valid results, but this still requires postulation of a certain distribution
* underlying the data, which is often not warranted, because too little is known about the data
* composition.
*
* <p><i>Bootstrap resampling of means differences</i> (and other differences) is a robust
* replacement for the t-test and does not require assumptions about the actual distribution of
* the data. The idea of bootstrapping is quite simple: simulation. The only assumption required
* is that the two samples <tt>a</tt> and <tt>b</tt> are representative for the underlying
* distribution with respect to the statistic that is being tested - this assumption is of course
* implicit in all statistical tests. We can now generate lots of further samples that correspond
* to the two given ones, by sampling <i>with replacement</i>. This process is called
* <i>resampling</i>. A resample can (and usually will) have a different mean than the original
* one and by drawing hundreds or thousands of such resamples <tt>a<sub>r</sub></tt> from
* <tt>a</tt> and <tt>b<sub>r</sub></tt> from <tt>b</tt> we can compute the so-called bootstrap
* distribution of all the differences "mean of <tt>a<sub>r</sub></tt> minus mean of
* <tt>b<sub>r</sub></tt>". That is, a bootstrap bin filled with the differences. Now we can
* compute, what fraction of these differences is, say, greater than zero. Let's assume we have
* computed 1000 resamples of both <tt>a</tt> and <tt>b</tt> and found that only <tt>8</tt> of the
* differences were greater than zero. Then <tt>8/1000</tt> or <tt>0.008</tt> is the p-value
* (probability) for the hypothesis that the mean of the distribution underlying <tt>a</tt> is
* actually larger than the mean of the distribution underlying <tt>b</tt>. From this bootstrap
* test, we can clearly reject the hypothesis.
*
* <p>Instead of using means differences, we can also use other differences, for example, the
* median differences.
*
* <p>Instead of p-values we can also read arbitrary confidence intervals from the bootstrap bin.
* For example, <tt>90%</tt> of all bootstrap differences are left of the value <tt>-3.5</tt>,
* hence a left <tt>90%</tt> confidence interval for the difference would be
* <tt>(3.5,infinity)</tt>; in other words: the difference is <tt>3.5</tt> or larger with
* probability <tt>0.9</tt>.
*
* <p>Sometimes we would like to compare not only means and medians, but also the variability
* (spread) of two samples. The conventional method of doing this is the <i>F-test</i>, which
* compares the standard deviations. It is related to the t-test and, like the latter, assumes the
* two samples to come from a normal distribution. The F-test is very sensitive to data with
* deviations from normality. Instead we can again resort to more robust bootstrap resampling and
* compare a measure of spread, for example the inter-quartile range. This way we compute a
* <i>bootstrap resampling of inter-quartile range differences</i> in order to arrive at a test
* for inequality or variability.
*
* <p><b>Example:</b>
*
* <table>
* <td class="PRE">
* <pre>
* // v1,v2 - the two samples to compare against each other
* double[] v1 = { 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 21, 22,23,24,25,26,27,28,29,30,31};
* double[] v2 = {10,11,12,13,14,15,16,17,18,19, 20, 30,31,32,33,34,35,36,37,38,39};
* hep.aida.bin.DynamicBin1D X = new hep.aida.bin.DynamicBin1D();
* hep.aida.bin.DynamicBin1D Y = new hep.aida.bin.DynamicBin1D();
* X.addAllOf(new cern.colt.list.DoubleArrayList(v1));
* Y.addAllOf(new cern.colt.list.DoubleArrayList(v2));
* cern.jet.random.engine.RandomEngine random = new cern.jet.random.engine.MersenneTwister();
*
* // bootstrap resampling of differences of means:
* BinBinFunction1D diff = new BinBinFunction1D() {
* public double apply(DynamicBin1D x, DynamicBin1D y) {return x.mean() - y.mean();}
* };
*
* // bootstrap resampling of differences of medians:
* BinBinFunction1D diff = new BinBinFunction1D() {
* public double apply(DynamicBin1D x, DynamicBin1D y) {return x.median() - y.median();}
* };
*
* // bootstrap resampling of differences of inter-quartile ranges:
* BinBinFunction1D diff = new BinBinFunction1D() {
* public double apply(DynamicBin1D x, DynamicBin1D y) {return (x.quantile(0.75)-x.quantile(0.25)) - (y.quantile(0.75)-y.quantile(0.25)); }
* };
*
* DynamicBin1D boot = X.sampleBootstrap(Y,1000,random,diff);
*
* cern.jet.math.Functions F = cern.jet.math.Functions.functions;
* System.out.println("p-value="+ (boot.aggregate(F.plus, F.greater(0)) / boot.size()));
* System.out.println("left 90% confidence interval = ("+boot.quantile(0.9) + ",infinity)");
*
* -->
* // bootstrap resampling of differences of means:
* p-value=0.0080
* left 90% confidence interval = (-3.571428571428573,infinity)
*
* // bootstrap resampling of differences of medians:
* p-value=0.36
* left 90% confidence interval = (5.0,infinity)
*
* // bootstrap resampling of differences of inter-quartile ranges:
* p-value=0.5699
* left 90% confidence interval = (5.0,infinity)
* </pre>
* </td>
* </table>
*
* @param other the other bin to compare the receiver against.
* @param resamples the number of times resampling shall be done.
* @param randomGenerator a random number generator. Set this parameter to <tt>null</tt> to use a
* default random number generator seeded with the current time.
* @param function a difference function comparing two samples; takes as first argument a sample
* of <tt>this</tt> and as second argument a sample of <tt>other</tt>.
* @return a bootstrap bin holding the results of <tt>function</tt> of each resampling step.
* @see cern.colt.GenericPermuting#permutation(long,int)
*/
public synchronized DynamicBin1D sampleBootstrap(
DynamicBin1D other,
int resamples,
cern.jet.random.engine.RandomEngine randomGenerator,
BinBinFunction1D function) {
if (randomGenerator == null) randomGenerator = cern.jet.random.Uniform.makeDefaultGenerator();
// since "resamples" can be quite large, we care about performance and memory
int maxCapacity = 1000;
int s1 = size();
int s2 = other.size();
// prepare auxiliary bins and buffers
DynamicBin1D sample1 = new DynamicBin1D();
cern.colt.buffer.DoubleBuffer buffer1 = sample1.buffered(Math.min(maxCapacity, s1));
DynamicBin1D sample2 = new DynamicBin1D();
cern.colt.buffer.DoubleBuffer buffer2 = sample2.buffered(Math.min(maxCapacity, s2));
DynamicBin1D bootstrap = new DynamicBin1D();
cern.colt.buffer.DoubleBuffer bootBuffer = bootstrap.buffered(Math.min(maxCapacity, resamples));
// resampling steps
for (int i = resamples; --i >= 0; ) {
sample1.clear();
sample2.clear();
this.sample(s1, true, randomGenerator, buffer1);
other.sample(s2, true, randomGenerator, buffer2);
bootBuffer.add(function.apply(sample1, sample2));
}
bootBuffer.flush();
return bootstrap;
}
/**
* Removes the <tt>s</tt> smallest and <tt>l</tt> largest elements from the receiver. The
* receivers size will be reduced by <tt>s + l</tt> elements.
*
* @param s the number of smallest elements to trim away (<tt>s >= 0</tt>).
* @param l the number of largest elements to trim away (<tt>l >= 0</tt>).
*/
public synchronized void trim(int s, int l) {
DoubleArrayList elems = sortedElements();
clear();
addAllOfFromTo(elems, s, elems.size() - 1 - l);
}