public void convertFromVector( Vector parameters ) { int M = this.getNumAutoRegressiveCoefficients(); int N = this.getNumMovingAverageCoefficients(); if( (M+N) != parameters.getDimensionality() ) { throw new IllegalArgumentException( "Number of dimensions of the parameter Vector aren't equal to the number expected." ); } this.setAutoregressiveCoefficients( parameters.subVector( 0, M-1 ) ); this.setMovingAverageCoefficients( parameters.subVector( M, N+M-1 ) ); }
@Override public void convertFromVector( final Vector parameters) { int dim = this.getGaussian().getInputDimensionality(); int N = dim + dim*dim; parameters.assertDimensionalityEquals(N+2); this.getGaussian().convertFromVector(parameters.subVector(0, N-1) ); this.getInverseGamma().convertFromVector( parameters.subVector(N, N+1) ); }
@Override public void convertFromVector( final Vector parameters) { int dim = this.getGaussian().getInputDimensionality(); int N = dim + dim*dim; parameters.assertDimensionalityEquals(N+2); this.getGaussian().convertFromVector(parameters.subVector(0, N-1) ); this.getInverseGamma().convertFromVector( parameters.subVector(N, N+1) ); }
@Override public void convertFromVector( Vector parameters) { final int num = this.getInputDimensionality() * this.getOutputDimensionality(); parameters.assertDimensionalityEquals(num + this.getOutputDimensionality()); Vector mp = parameters.subVector(0,num-1); Vector bp = parameters.subVector(num, num+this.getOutputDimensionality()-1); super.convertFromVector( mp ); this.bias.convertFromVector(bp); }
public void convertFromVector( Vector parameters) { final int d = this.getInputDimensionality(); parameters.assertDimensionalityEquals( 1+d + 1+d*d ); this.setCovarianceDivisor( parameters.getElement(0) ); Vector mean = parameters.subVector(1, d); this.gaussian.setMean(mean); Vector iwp = parameters.subVector(d+1, parameters.getDimensionality()-1); this.inverseWishart.convertFromVector(iwp); }
@Override public void convertFromVector( Vector parameters) { final int num = this.getInputDimensionality() * this.getOutputDimensionality(); parameters.assertDimensionalityEquals(num + this.getOutputDimensionality()); Vector mp = parameters.subVector(0,num-1); Vector bp = parameters.subVector(num, num+this.getOutputDimensionality()-1); super.convertFromVector( mp ); this.bias.convertFromVector(bp); }
public void convertFromVector( Vector parameters) { final int d = this.getInputDimensionality(); parameters.assertDimensionalityEquals( 1+d + 1+d*d ); this.setCovarianceDivisor( parameters.getElement(0) ); Vector mean = parameters.subVector(1, d); this.gaussian.setMean(mean); Vector iwp = parameters.subVector(d+1, parameters.getDimensionality()-1); this.inverseWishart.convertFromVector(iwp); }
public void convertFromVector( Vector parameters) { final int d = this.getInputDimensionality(); parameters.assertDimensionalityEquals( 1+d + 1+d*d ); this.setCovarianceDivisor( parameters.getElement(0) ); Vector mean = parameters.subVector(1, d); this.gaussian.setMean(mean); Vector iwp = parameters.subVector(d+1, parameters.getDimensionality()-1); this.inverseWishart.convertFromVector(iwp); }
@Override public void convertFromVector( final Vector parameters) { int dim = this.getGaussian().getInputDimensionality(); int N = dim + dim*dim; parameters.assertDimensionalityEquals(N+2); this.getGaussian().convertFromVector(parameters.subVector(0, N-1) ); this.getInverseGamma().convertFromVector( parameters.subVector(N, N+1) ); }
public void convertFromVector( Vector parameters) { final int dim = this.getInputDimensionality(); parameters.assertDimensionalityEquals(1+dim+dim*dim); this.setDegreesOfFreedom( parameters.getElement(0) ); this.setMean( parameters.subVector(1, dim) ); Matrix p = this.getPrecision(); p.convertFromVector( parameters.subVector( dim+1, parameters.getDimensionality()-1) ); this.setPrecision(p); }
public void convertFromVector( Vector parameters) { final int dim = this.getInputDimensionality(); parameters.assertDimensionalityEquals(1+dim+dim*dim); this.setDegreesOfFreedom( parameters.getElement(0) ); this.setMean( parameters.subVector(1, dim) ); Matrix p = this.getPrecision(); p.convertFromVector( parameters.subVector( dim+1, parameters.getDimensionality()-1) ); this.setPrecision(p); }
public void convertFromVector( Vector parameters) { final int dim = this.getInputDimensionality(); parameters.assertDimensionalityEquals(1+dim+dim*dim); this.setDegreesOfFreedom( parameters.getElement(0) ); this.setMean( parameters.subVector(1, dim) ); Matrix p = this.getPrecision(); p.convertFromVector( parameters.subVector( dim+1, parameters.getDimensionality()-1) ); this.setPrecision(p); }
@Override public void convertFromVector( Vector parameters) { final int dim = this.getInputDimensionality() + 1; parameters.assertDimensionalityEquals( dim ); this.setWeightVector( parameters.subVector(0, dim-2) ); this.setBias( parameters.getElement(dim-1) ); }
@Override public void convertFromVector( final Vector parameters) { int p = this.getInputDimensionality(); parameters.assertDimensionalityEquals( 1 + p*p ); int dof = (int) Math.round(parameters.getElement(0)); Vector matrix = parameters.subVector(1, parameters.getDimensionality()-1 ); this.setDegreesOfFreedom(dof); this.getInverseScale().convertFromVector( matrix ); }
@Override public void convertFromVector( final Vector parameters) { int p = this.getInputDimensionality(); parameters.assertDimensionalityEquals( 1 + p*p ); int dof = (int) Math.round(parameters.getElement(0)); Vector matrix = parameters.subVector(1, parameters.getDimensionality()-1 ); this.setDegreesOfFreedom(dof); this.getInverseScale().convertFromVector( matrix ); }
@Override public void convertFromVector( Vector parameters) { final int dim = this.getInputDimensionality() + 1; parameters.assertDimensionalityEquals( dim ); this.setWeightVector( parameters.subVector(0, dim-2) ); this.setBias( parameters.getElement(dim-1) ); }
@Override public void convertFromVector( Vector parameters) { final int dim = this.getInputDimensionality() + 1; parameters.assertDimensionalityEquals( dim ); this.setWeightVector( parameters.subVector(0, dim-2) ); this.setBias( parameters.getElement(dim-1) ); }