/**
* Computes an alpha trimmed mean upon the current region of the current function. Note that this
* method uses memory to make a copy of the input values. Larger input regions might require a lot
* of memory.
*
* @param alpha A number between 0 and 0.5 specifying the proportion of samples to ignore on each
* end.
* @return The measured value
*/
public double alphaTrimmedMean(double alpha) {
if ((alpha < 0) || (alpha >= 0.5))
throw new IllegalArgumentException("alpha value must be >= 0 and < 0.5");
T tmp = func.createOutput();
values.clear();
iter.reset();
while (iter.hasNext()) {
long[] pos = iter.next();
func.compute(pos, tmp);
values.add(tmp.getRealDouble());
}
values.sortValues();
double tailSize = alpha * values.size();
// can we avoid interpolation?
if (tailSize == Math.floor(tailSize)) {
// yes, trim count is exactly an integer
return calcTrimmedMean(values, (int) tailSize);
}
// no, trim count is a float value
// calc two trimmed means and interpolate to find the value between them
double mean1 = calcTrimmedMean(values, (int) Math.floor(tailSize));
double mean2 = calcTrimmedMean(values, (int) Math.ceil(tailSize));
double fraction = tailSize - Math.floor(tailSize);
double interpolation = ((1 - fraction) * mean1) + (fraction * mean2);
return interpolation;
}
/**
* Computes a trimmed mean upon the current region of the current function. Note that this method
* uses memory to make a copy of the input values. Larger input regions might require a lot of
* memory.
*
* @param halfTrimSize The number of samples to ignore from each end of the data
* @return The measured value
*/
public double trimmedMean(int halfTrimSize) {
T tmp = func.createOutput();
values.clear();
iter.reset();
while (iter.hasNext()) {
long[] pos = iter.next();
func.compute(pos, tmp);
values.add(tmp.getRealDouble());
}
values.sortValues();
return calcTrimmedMean(values, halfTrimSize);
}
private double calcTrimmedMean(PrimitiveDoubleArray vals, int halfTrim) {
final int trimSize = halfTrim * 2;
final int numElements = vals.size();
if (numElements <= trimSize)
throw new IllegalArgumentException(
"number of samples must be greater than number of trimmed values");
final int top = numElements - halfTrim;
double sum = 0;
for (int i = halfTrim; i < top; i++) {
sum += vals.get(i);
}
return sum / (numElements - trimSize);
}
/**
* Resets the StatCalculator to work with a new function and/or region. The calculator does the
* minimum amount of reinitialization.
*
* @param newFunc The new {@link Function} to use for obtaining sample values
* @param newRegion The new {@link PointSet} region over which to gather samples
*/
public void reset(Function<long[], T> newFunc, PointSet newRegion) {
func = newFunc;
if (newRegion == region) {
iter.reset();
} else {
region = newRegion;
iter = region.iterator();
}
values.clear();
}
/**
* Computes the median upon the current region of the current function. Note that this method uses
* memory to make a copy of the input values. Larger input regions might require a lot of memory.
*
* @return The measured value
*/
public double median() {
T tmp = func.createOutput();
values.clear();
iter.reset();
while (iter.hasNext()) {
long[] pos = iter.next();
func.compute(pos, tmp);
values.add(tmp.getRealDouble());
}
final int numElements = values.size();
if (numElements <= 0)
throw new IllegalArgumentException("number of samples must be greater than 0");
values.sortValues();
// odd number of elements
if ((numElements % 2) == 1) return values.get(numElements / 2);
// else an even number of elements
double value1 = values.get((numElements / 2) - 1);
double value2 = values.get((numElements / 2));
return (value1 + value2) / 2;
}
