public void add(FeatureVector other) throws InvalidFeatureVectorValueException { for (FeatureVector.Entry entry : other) { this.set(entry.index, this.get(entry.index) + entry.value); } }
public void multiply(double factor) throws InvalidFeatureVectorValueException { for (FeatureVector.Entry entry : this) { this.set(entry.index, this.get(entry.index) * factor); } }
private static TrainingInstance parseTI(String line) throws InvalidFeatureVectorValueException { String[] fields = line.split(" "); boolean label = fields[0].trim().equals("+1"); FeatureVector fv = new SparseFeatureVector(); for (int i = 1; i < fields.length; i++) { String[] parts = fields[i].split(":"); int featureIndex = Integer.valueOf(parts[0]); double featureValue = Double.valueOf(parts[1]); fv.set(featureIndex, featureValue); } return new TrainingInstance(label, fv); }
private static TrainingInstance parseTI(String line) throws InvalidFeatureVectorValueException { String[] fields = line.split(" "); boolean label = fields[0].trim().equals("+1"); FeatureVector fv = new SparseFeatureVector(); for (int i = 1; i < fields.length; i++) { String[] parts = fields[i].split(":"); int featureIndex = Integer.valueOf(parts[0]); double featureValue = Double.valueOf(parts[1]); fv.set(featureIndex, featureValue); } return new TrainingInstance(label, fv); }
private static SupportVector readSV(SvmLightReader in) throws IOException { String[] fields = in.readLine().split(" "); double alpha_y = Double.valueOf(fields[0]); FeatureVector fv = new SparseFeatureVector(); for (int i = 1; i < fields.length; i++) { String[] parts = fields[i].split(":"); int featureIndex = Integer.valueOf(parts[0]); double featureValue = Double.valueOf(parts[1]); try { fv.set(featureIndex, featureValue); } catch (InvalidFeatureVectorValueException e) { throw new IOException(e); } } return new SupportVector(alpha_y, fv); }
private static SupportVector readSV(SvmLightReader in) throws IOException { String[] fields = in.readLine().split(" "); double alpha_y = Double.valueOf(fields[0]); FeatureVector fv = new SparseFeatureVector(); for (int i = 1; i < fields.length; i++) { String[] parts = fields[i].split(":"); int featureIndex = Integer.valueOf(parts[0]); double featureValue = Double.valueOf(parts[1]); try { fv.set(featureIndex, featureValue); } catch (InvalidFeatureVectorValueException e) { throw new IOException(e); } } return new SupportVector(alpha_y, fv); }
double featureValue = Double.valueOf(parts[1]); try { fv.set(featureIndex, featureValue); } catch (InvalidFeatureVectorValueException e) { throw new IOException(e);
double featureValue = Double.valueOf(parts[1]); try { fv.set(featureIndex, featureValue); } catch (InvalidFeatureVectorValueException e) { throw new IOException(e);
double featureValue = Double.valueOf(parts[1]); try { vec.set(featureIndex, featureValue); } catch (InvalidFeatureVectorValueException e) { throw new IOException(e);