use of gdsc.smlm.fitting.BinomialFitter in project GDSC-SMLM by aherbert.
the class PCPALMClusters method fitBinomial.
/**
* Fit a zero-truncated Binomial to the cumulative histogram
*
* @param histogramData
* @return
*/
private double[] fitBinomial(HistogramData histogramData) {
// Get the mean and sum of the input histogram
double mean;
double sum = 0;
count = 0;
for (int i = 0; i < histogramData.histogram[1].length; i++) {
count += histogramData.histogram[1][i];
sum += histogramData.histogram[1][i] * i;
}
mean = sum / count;
String name = "Zero-truncated Binomial distribution";
Utils.log("Mean cluster size = %s", Utils.rounded(mean));
Utils.log("Fitting cumulative " + name);
// Convert to a normalised double array for the binomial fitter
double[] histogram = new double[histogramData.histogram[1].length];
for (int i = 0; i < histogramData.histogram[1].length; i++) histogram[i] = histogramData.histogram[1][i] / count;
// Plot the cumulative histogram
String title = TITLE + " Cumulative Distribution";
Plot2 plot = null;
if (showCumulativeHistogram) {
// Create a cumulative histogram for fitting
double[] cumulativeHistogram = new double[histogram.length];
sum = 0;
for (int i = 0; i < histogram.length; i++) {
sum += histogram[i];
cumulativeHistogram[i] = sum;
}
double[] values = Utils.newArray(histogram.length, 0.0, 1.0);
plot = new Plot2(title, "N", "Cumulative Probability", values, cumulativeHistogram);
plot.setLimits(0, histogram.length - 1, 0, 1.05);
plot.addPoints(values, cumulativeHistogram, Plot2.CIRCLE);
Utils.display(title, plot);
}
// Do fitting for different N
double bestSS = Double.POSITIVE_INFINITY;
double[] parameters = null;
int worse = 0;
int N = histogram.length - 1;
int min = minN;
final boolean customRange = (minN > 1) || (maxN > 0);
if (min > N)
min = N;
if (maxN > 0 && N > maxN)
N = maxN;
Utils.log("Fitting N from %d to %d%s", min, N, (customRange) ? " (custom-range)" : "");
// Since varying the N should be done in integer steps do this
// for n=1,2,3,... until the SS peaks then falls off (is worse then the best
// score several times in succession)
BinomialFitter bf = new BinomialFitter(new IJLogger());
bf.setMaximumLikelihood(maximumLikelihood);
for (int n = min; n <= N; n++) {
PointValuePair solution = bf.fitBinomial(histogram, mean, n, true);
if (solution == null)
continue;
double p = solution.getPointRef()[0];
Utils.log("Fitted %s : N=%d, p=%s. SS=%g", name, n, Utils.rounded(p), solution.getValue());
if (bestSS > solution.getValue()) {
bestSS = solution.getValue();
parameters = new double[] { n, p };
worse = 0;
} else if (bestSS < Double.POSITIVE_INFINITY) {
if (++worse >= 3)
break;
}
if (showCumulativeHistogram)
addToPlot(n, p, title, plot, new Color((float) n / N, 0, 1f - (float) n / N));
}
// Add best it in magenta
if (showCumulativeHistogram && parameters != null)
addToPlot((int) parameters[0], parameters[1], title, plot, Color.magenta);
return parameters;
}
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