/** Calculate the log probabilities of each class, for the given datum (feature bundle). */
public <F, L> double[] getLogProbabilities(
EncodedDatum datum,
double[] weights,
Encoding<F, L> encoding,
IndexLinearizer indexLinearizer) {
// Compute unnormalized log probabilities
int numSubLabels = encoding.getNumSubLabels();
double[] logProbabilities = DoubleArrays.constantArray(0.0, numSubLabels);
for (int i = 0; i < datum.getNumActiveFeatures(); i++) {
int featureIndex = datum.getFeatureIndex(i);
double featureCount = datum.getFeatureCount(i);
for (int j = 0; j < numSubLabels; j++) {
int index = indexLinearizer.getLinearIndex(featureIndex, j);
double weight = weights[index];
logProbabilities[j] += weight * featureCount;
}
}
// Normalize
double logNormalizer = SloppyMath.logAdd(logProbabilities);
for (int i = 0; i < numSubLabels; i++) {
logProbabilities[i] -= logNormalizer;
}
return logProbabilities;
}
/**
* The most important part of the classifier learning process! This method determines, for the
* given weight vector x, what the (negative) log conditional likelihood of the data is, as well
* as the derivatives of that likelihood wrt each weight parameter.
*/
public Pair<Double, double[]> calculate() {
double objective = 0.0;
System.out.println("In Calculate...");
double[] derivatives = DoubleArrays.constantArray(0.0, dimension());
int numSubLabels = encoding.getNumSubLabels();
int numData = data.length;
for (int l = 0; l < numData; ++l) {
EncodedDatum datum = data[l];
double[] logProbabilities = getLogProbabilities(datum, x, encoding, indexLinearizer);
int C = datum.getLabelIndex();
double[] labelWeights = datum.getWeights();
int numSubstatesC = labelWeights.length;
int substate0 = encoding.getLabelSubindexBegin(C);
for (int c = 0; c < numSubstatesC; c++) { // For each substate of label C
objective -= labelWeights[c] * logProbabilities[substate0 + c];
}
// Convert to probabilities:
double[] probabilities = new double[numSubLabels];
double sum = 0.0;
for (int c = 0; c < numSubLabels; ++c) { // For each substate
probabilities[c] = Math.exp(logProbabilities[c]);
sum += probabilities[c];
}
if (Math.abs(sum - 1.0) > 1e-3) {
System.err.println("Probabilities do not sum to 1!");
}
// Compute derivatives:
for (int i = 0; i < datum.getNumActiveFeatures(); ++i) {
int featureIndex = datum.getFeatureIndex(i);
double featureCount = datum.getFeatureCount(i);
for (int c = 0; c < numSubLabels; ++c) { // For each substate
int index = indexLinearizer.getLinearIndex(featureIndex, c);
derivatives[index] += featureCount * probabilities[c];
}
for (int c = 0; c < numSubstatesC; c++) { // For each substate of label C
int index = indexLinearizer.getLinearIndex(featureIndex, substate0 + c);
derivatives[index] -= labelWeights[c] * featureCount;
}
}
}
// Incorporate penalty terms (regularization) into the objective and derivatives
double sigma2 = sigma * sigma;
double penalty = 0.0;
for (int index = 0; index < x.length; ++index) {
penalty += x[index] * x[index];
}
objective += penalty / (2 * sigma2);
for (int index = 0; index < x.length; ++index) {
// 'x' and 'derivatives' have same layout
derivatives[index] += x[index] / sigma2;
}
return new Pair<Double, double[]>(objective, derivatives);
}
