/** * Basic setter for the categorized vector. * @param newCategorizedVector */ public void setCategorizedVector(Vector newCategorizedVector) { categorizedVector = newCategorizedVector.clone(); }
/** * Sets the initial guess ("x0") * * @param initialGuess the initial guess ("x0") */ @Override final public void setInitialGuess(Vector initialGuess) { x0 = initialGuess.clone(); }
@Override public Vector convertToVector() { return this.parameters.clone(); }
/** * Returns the initial guess at "x" * * @return the initial guess at "x" */ @Override final public Vector getInitialGuess() { return x0.clone(); }
/** * Basic setter for the test vector. * @param newTestVector */ public void setTestVector(Vector newTestVector) { testingVector = newTestVector.clone(); }
/** * Sets the initial guess ("x0") * * @param initialGuess the initial guess ("x0") */ @Override final public void setInitialGuess(Vector initialGuess) { x0 = initialGuess.clone(); }
@Override final public Vector plus( final Vector v) { // I need to flip this so that if it the input is a dense vector, I // return a dense vector. If it's a sparse vector, then a sparse vector // is still returned. Vector result = v.clone(); result.plusEquals(this); return result; }
@Override final public Vector plus( final Vector v) { // I need to flip this so that if it the input is a dense vector, I // return a dense vector. If it's a sparse vector, then a sparse vector // is still returned. Vector result = v.clone(); result.plusEquals(this); return result; }
/** * {@inheritDoc} * @return {@inheritDoc} */ protected boolean initializeAlgorithm() { this.previousDelta = null; this.result = new DefaultInputOutputPair<Vector, Double>( this.initialGuess.clone(), null ); return true; }
@Override final public Vector plus( final Vector v) { // I need to flip this so that if it the input is a dense vector, I // return a dense vector. If it's a sparse vector, then a sparse vector // is still returned. Vector result = v.clone(); result.plusEquals(this); return result; }
@Override final public Vector minus( final Vector v) { // I need to flip this so that if it the input is a dense vector, I // return a dense vector. If it's a sparse vector, then a sparse vector // is still returned. Vector result = v.clone(); result.negativeEquals(); result.plusEquals(this); return result; }
/** * Copy Constructor * @param other VectorBasedCognitiveModelInput to clone */ public VectorBasedCognitiveModelInput( VectorBasedCognitiveModelInput other ) { this( other.getIdentifiers(), other.getValues().clone() ); }
@Override final public Vector minus( final Vector v) { // I need to flip this so that if it the input is a dense vector, I // return a dense vector. If it's a sparse vector, then a sparse vector // is still returned. Vector result = v.clone(); result.negativeEquals(); result.plusEquals(this); return result; }
@Override public AutoRegressiveMovingAverageFilter clone() { AutoRegressiveMovingAverageFilter clone = (AutoRegressiveMovingAverageFilter) super.clone(); clone.setAutoregressiveCoefficients( this.getAutoRegressiveCoefficients().clone() ); clone.setMovingAverageCoefficients( this.getMovingAverageCoefficients().clone() ); return clone; }
@Override public MovingAverageFilter clone() { MovingAverageFilter clone = (MovingAverageFilter) super.clone(); clone.setMovingAverageCoefficients( this.getMovingAverageCoefficients().clone() ); return clone; }
@Override public MovingAverageFilter clone() { MovingAverageFilter clone = (MovingAverageFilter) super.clone(); clone.setMovingAverageCoefficients( this.getMovingAverageCoefficients().clone() ); return clone; }
@Override public MovingAverageFilter clone() { MovingAverageFilter clone = (MovingAverageFilter) super.clone(); clone.setMovingAverageCoefficients( this.getMovingAverageCoefficients().clone() ); return clone; }
public MultivariateGaussian createInitialLearnedObject() { return new MultivariateGaussian( this.getMotionModel().getState().clone(), this.getModelCovariance() ); }
public MultivariateGaussian createPredictiveDistribution( MultivariateGaussian posterior) { Vector mean = posterior.getMean().clone(); Matrix C = posterior.getCovariance().plus( this.parameter.getConditionalDistribution().getCovariance() ); return new MultivariateGaussian( mean, C ); }
public MultivariateGaussian createPredictiveDistribution( MultivariateGaussian posterior) { Vector mean = posterior.getMean().clone(); Matrix C = posterior.getCovariance().plus( this.parameter.getConditionalDistribution().getCovariance() ); return new MultivariateGaussian( mean, C ); }