this.direction = (DenseVector) new DenseVector(endPoint).add(-1, new DenseVector(startPoint)); this.origin = new DenseVector(originPoint);
public Assignment sample (Randoms r) { // generate from standard normal double[] vals = new double [mean.size ()]; for (int k = 0; k < vals.length; k++) { vals[k] = r.nextGaussian (); } // and transform Vector Z = new DenseVector (vals, false); DenseVector result = new DenseVector (vals.length); variance.mult (Z, result); result = (DenseVector) result.add (mean); return new Assignment (vars.toVariableArray (), result.getData ()); }
public Assignment sample (Randoms r) { // generate from standard normal double[] vals = new double [mean.size ()]; for (int k = 0; k < vals.length; k++) { vals[k] = r.nextGaussian (); } // and transform Vector Z = new DenseVector (vals, false); DenseVector result = new DenseVector (vals.length); variance.mult (Z, result); result = (DenseVector) result.add (mean); return new Assignment (vars.toVariableArray (), result.getData ()); }
public Assignment sample (Randoms r) { // generate from standard normal double[] vals = new double [mean.size ()]; for (int k = 0; k < vals.length; k++) { vals[k] = r.nextGaussian (); } // and transform Vector Z = new DenseVector (vals, false); DenseVector result = new DenseVector (vals.length); variance.mult (Z, result); result = (DenseVector) result.add (mean); return new Assignment (vars.toVariableArray (), result.getData ()); }
@Override public Vector[] apply(double[] in) { Vector[] vmat = new Vector[in.length]; vmat[0] = new DenseVector(in); double norm = vmat[0].norm(Norm.Two); vmat[0].scale(1/norm); for (int j = 1; j < in.length; j++) { Vector randvec = randvec(vmat[0].size(),norm); vmat[j] = new DenseVector(vmat[0]).add(randvec); for (int i = 0; i < j; i++) { vmat[j].add(-1, project(vmat[j],vmat[i])); } vmat[j].scale(1/vmat[j].norm(Norm.Two)); } return vmat; }
if (observations[i].length > 0) { cov = cov.add(groupWeights[i], groupCovariance[i]); mean = (DenseVector) mean.add(groupWeights[i], groupMean[i]); means[i] = groupMean[i].getData();
if (observations[i].length > 0) { cov = cov.add(groupWeights[i], groupCovariance[i]); mean = (DenseVector) mean.add(groupWeights[i], groupMean[i]); means[i] = groupMean[i].getData();