public static void main(String[] args) {
GoldenSectionLineSearch min = new GoldenSectionLineSearch(true, 0.00001, 0.001, 121.0);
Function<Double, Double> f1 =
new Function<Double, Double>() {
public Double apply(Double x) {
return Math.log(x * x - x + 1);
}
};
System.out.println(min.minimize(f1));
System.out.println();
min = new GoldenSectionLineSearch(false, 0.00001, 0.0, 1.0);
Function<Double, Double> f2 =
new Function<Double, Double>() {
public Double apply(Double x) {
// this function used to fail in Galen's version; min should be 0.2
// return - x * (2 * x - 1) * (x - 0.8);
// this function fails if you don't find an initial bracketing
return x < 0.1 ? 0.0 : (x > 0.2 ? 0.0 : (x - 0.1) * (x - 0.2));
// return - Math.sin(x * Math.PI);
// return -(3 + 6 * x - 4 * x * x);
}
};
System.out.println(min.minimize(f2));
} // end main
private void tuneSigma(final int[][] data, final int[] labels) {
Function<Double, Double> CVSigmaToPerplexity =
trialSigma -> {
double score = 0.0;
double sumScore = 0.0;
int foldSize, nbCV;
System.err.println("Trying sigma = " + trialSigma);
// test if enough training data
if (data.length >= folds) {
foldSize = data.length / folds;
nbCV = folds;
} else { // leave-one-out
foldSize = 1;
nbCV = data.length;
}
for (int j = 0; j < nbCV; j++) {
// System.out.println("CV j: "+ j);
int testMin = j * foldSize;
int testMax = testMin + foldSize;
LinearClassifier<L, F> c =
new LinearClassifier<>(
weights(data, labels, testMin, testMax, trialSigma, foldSize),
featureIndex,
labelIndex);
for (int i = testMin; i < testMax; i++) {
// System.out.println("test i: "+ i + " "+ new
// BasicDatum(featureIndex.objects(data[i])));
score -=
c.logProbabilityOf(new BasicDatum<>(featureIndex.objects(data[i])))
.getCount(labelIndex.get(labels[i]));
}
// System.err.printf("%d: %8g%n", j, score);
sumScore += score;
}
System.err.printf(": %8g%n", sumScore);
return sumScore;
};
GoldenSectionLineSearch gsls = new GoldenSectionLineSearch(true);
sigma = gsls.minimize(CVSigmaToPerplexity, 0.01, 0.0001, 2.0);
System.out.println("Sigma used: " + sigma);
}