@Override public void train(long trackingKey, String groupKey, int actual, Vector instance) { record++; buffer.add(new TrainingExample(trackingKey, groupKey, actual, instance)); //don't train until we have enough examples if (buffer.size() > bufferSize) { trainWithBufferedExamples(); } }
@Override public void train(long trackingKey, String groupKey, int actual, Vector instance) { record++; buffer.add(new TrainingExample(trackingKey, groupKey, actual, instance)); //don't train until we have enough examples if (buffer.size() > bufferSize) { trainWithBufferedExamples(); } }
@Override public void train(long trackingKey, String groupKey, int actual, Vector instance) { record++; buffer.add(new TrainingExample(trackingKey, groupKey, actual, instance)); //don't train until we have enough examples if (buffer.size() > bufferSize) { trainWithBufferedExamples(); } }
buffer = new ArrayList<>(); for (int i = 0; i < n; i++) { TrainingExample example = new TrainingExample(); example.readFields(in); buffer.add(example);
buffer = Lists.newArrayList(); for (int i = 0; i < n; i++) { TrainingExample example = new TrainingExample(); example.readFields(in); buffer.add(example);
buffer = Lists.newArrayList(); for (int i = 0; i < n; i++) { TrainingExample example = new TrainingExample(); example.readFields(in); buffer.add(example);
private static AdaptiveLogisticRegression.TrainingExample getExample(int i, Random gen, Vector beta) { Vector data = new DenseVector(200); for (Vector.Element element : data.all()) { element.set(gen.nextDouble() < 0.3 ? 1 : 0); } double p = 1 / (1 + Math.exp(1.5 - data.dot(beta))); int target = 0; if (gen.nextDouble() < p) { target = 1; } return new AdaptiveLogisticRegression.TrainingExample(i, null, target, data); }