/** * Creates a one-dimensional zero vector: (0.0). * * @return * A one-dimensional zero vector. */ public Vector1D createVector1D() { return this.createVector1D(0.0); }
/** * Creates a one-dimensional zero vector: (0.0). * * @return * A one-dimensional zero vector. */ public Vector1D createVector1D() { return this.createVector1D(0.0); }
/** * Creates a one-dimensional zero vector: (0.0). * * @return * A one-dimensional zero vector. */ public Vector1D createVector1D() { return this.createVector1D(0.0); }
@Override public T predict(Double data) { return model.evaluate(VectorFactory.getDefault().createVector1D(data)); }
@Override public boolean estimate(List<? extends IndependentPair<Double, T>> data) { final VectorNaiveBayesCategorizer.BatchGaussianLearner<T> learner = new VectorNaiveBayesCategorizer.BatchGaussianLearner<T>(); final List<InputOutputPair<Vector, T>> cfdata = new ArrayList<InputOutputPair<Vector, T>>(); for (final IndependentPair<Double, T> d : data) { final InputOutputPair<Vector, T> iop = new DefaultInputOutputPair<Vector, T>(VectorFactory.getDefault() .createVector1D(d.firstObject()), d.secondObject()); cfdata.add(iop); } model = learner.learn(cfdata); return true; }