@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; AtransB = null; return result; }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; return result; }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; AtransB = null; return result; }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; return result; }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; return result; }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; return result; }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; AtransB = null; return result; }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; return result; }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; return result; }
@Override public InputOutputPair<InputType, OutputType> createPair( InputType first, OutputType second ) { return new DefaultInputOutputPair<InputType, OutputType>( first, second ); }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; return result; }
@Override public InputOutputPair<InputType, OutputType> createPair( InputType first, OutputType second ) { return new DefaultInputOutputPair<InputType, OutputType>( first, second ); }
@Override final protected InputOutputPair<Vector, Vector> completeSolver() { InputOutputPair<Vector, Vector> result = new DefaultInputOutputPair<Vector, Vector>(x0, x); A = null; residual = null; x = null; delta = 0; return result; }
/** * {@inheritDoc} * @return {@inheritDoc} */ protected boolean initializeAlgorithm() { this.previousDelta = null; this.result = new DefaultInputOutputPair<Vector, Double>( this.initialGuess.clone(), null ); return true; }
/** * {@inheritDoc} * @return {@inheritDoc} */ protected boolean initializeAlgorithm() { this.previousDelta = null; this.result = new DefaultInputOutputPair<Vector, Double>( this.initialGuess.clone(), null ); return true; }
@Override public boolean estimate(List<? extends IndependentPair<double[], T>> data) { 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() .copyArray(d.firstObject()), d.secondObject()); cfdata.add(iop); } model = learner.learn(cfdata); return true; }
@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; }
@Override protected boolean initializeAlgorithm() { this.result = new DefaultInputOutputPair<Vector, Double>( this.initialGuess, this.data.evaluate( this.initialGuess ) ); this.gradient = this.data.differentiate( this.initialGuess ); this.lineFunction = new DirectionalVectorToDifferentiableScalarFunction( this.data, this.initialGuess, this.gradient.scale(-1.0) ); return true; }
@Override protected boolean initializeAlgorithm() { this.result = new DefaultInputOutputPair<Vector, Double>( this.initialGuess, this.data.evaluate( this.initialGuess ) ); this.gradient = this.data.differentiate( this.initialGuess ); this.lineFunction = new DirectionalVectorToDifferentiableScalarFunction( this.data, this.initialGuess, this.gradient.scale(-1.0) ); return true; }
@Override public void train(Annotated<OBJECT, ANNOTATION> annotated) { final FeatureVector feature = extractor.extractFeature(annotated.getObject()); final Vector vec = VectorFactory.getDefault().copyArray(feature.asDoubleVector()); for (final ANNOTATION ann : annotated.getAnnotations()) { learner.update(categorizer, new DefaultInputOutputPair<Vector, ANNOTATION>(vec, ann)); } }