public LinearClassifier createLinearClassifier(double[] weights) {
double[][] weights2D;
if (objective != null) {
weights2D = objective.to2D(weights);
} else {
weights2D = ArrayUtils.to2D(weights, featureIndex.size(), labelIndex.size());
}
return new LinearClassifier<L, F>(weights2D, featureIndex, labelIndex);
}
/**
* Given the path to a file representing the text based serialization of a Linear Classifier,
* reconstitutes and returns that LinearClassifier.
*
* <p>TODO: Leverage Index
*/
public static LinearClassifier<String, String> loadFromFilename(String file) {
try {
BufferedReader in = IOUtils.readerFromString(file);
// Format: read indices first, weights, then thresholds
Index<String> labelIndex = HashIndex.loadFromReader(in);
Index<String> featureIndex = HashIndex.loadFromReader(in);
double[][] weights = new double[featureIndex.size()][labelIndex.size()];
int currLine = 1;
String line = in.readLine();
while (line != null && line.length() > 0) {
String[] tuples = line.split(LinearClassifier.TEXT_SERIALIZATION_DELIMITER);
if (tuples.length != 3) {
throw new Exception(
"Error: incorrect number of tokens in weight specifier, line="
+ currLine
+ " in file "
+ file);
}
currLine++;
int feature = Integer.valueOf(tuples[0]);
int label = Integer.valueOf(tuples[1]);
double value = Double.valueOf(tuples[2]);
weights[feature][label] = value;
line = in.readLine();
}
// First line in thresholds is the number of thresholds
int numThresholds = Integer.valueOf(in.readLine());
double[] thresholds = new double[numThresholds];
int curr = 0;
while ((line = in.readLine()) != null) {
double tval = Double.valueOf(line.trim());
thresholds[curr++] = tval;
}
in.close();
LinearClassifier<String, String> classifier =
new LinearClassifier<String, String>(weights, featureIndex, labelIndex);
return classifier;
} catch (Exception e) {
System.err.println("Error in LinearClassifierFactory, loading from file=" + file);
e.printStackTrace();
return null;
}
}
public Classifier<L, F> trainClassifier(Iterable<Datum<L, F>> dataIterable) {
Minimizer<DiffFunction> minimizer = getMinimizer();
Index<F> featureIndex = Generics.newIndex();
Index<L> labelIndex = Generics.newIndex();
for (Datum<L, F> d : dataIterable) {
labelIndex.add(d.label());
featureIndex.addAll(d.asFeatures()); // If there are duplicates, it doesn't add them again.
}
System.err.println(
String.format(
"Training linear classifier with %d features and %d labels",
featureIndex.size(), labelIndex.size()));
LogConditionalObjectiveFunction<L, F> objective =
new LogConditionalObjectiveFunction<L, F>(dataIterable, logPrior, featureIndex, labelIndex);
objective.setPrior(new LogPrior(LogPrior.LogPriorType.QUADRATIC));
double[] initial = objective.initial();
double[] weights = minimizer.minimize(objective, TOL, initial);
LinearClassifier<L, F> classifier =
new LinearClassifier<L, F>(objective.to2D(weights), featureIndex, labelIndex);
return classifier;
}