本文整理汇总了Java中org.jblas.Singular.sparseSVD方法的典型用法代码示例。如果您正苦于以下问题：Java Singular.sparseSVD方法的具体用法？Java Singular.sparseSVD怎么用？Java Singular.sparseSVD使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.jblas.Singular的用法示例。
在下文中一共展示了Singular.sparseSVD方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: computeTruncatedSVD
import org.jblas.Singular; //导入方法依赖的package包/类
protected DoubleMatrix[] computeTruncatedSVD(DoubleMatrix mat, Double singValTol, int maxSize)
{
DoubleMatrix[] svdResult = Singular.sparseSVD(mat);
notifyPSRObservers(svdResult[1].toArray());
int singIter;
for(singIter = 0; singIter < svdResult[1].getRows(); singIter++)
{
if(svdResult[1].get(singIter,0) < singValTol)
break;
}
int numToKeep = Math.min(singIter, maxSize);
maxDim = numToKeep;
DoubleMatrix u = new DoubleMatrix(mat.getRows(),numToKeep);
u = svdResult[0].getRange(0,svdResult[0].getRows(), 0, numToKeep);
DoubleMatrix s = DoubleMatrix.diag(svdResult[1].getRange(0, numToKeep, 0, 1));
DoubleMatrix v = new DoubleMatrix(mat.getColumns(), numToKeep);
v = svdResult[2].getRange(0, svdResult[2].getRows(), 0, numToKeep);
DoubleMatrix[] truncatedSVDResult = {u.transpose(),s,v};
return truncatedSVDResult;
}
示例2: computeMapping
import org.jblas.Singular; //导入方法依赖的package包/类
private DoubleMatrix computeMapping(DoubleMatrix x) {
DoubleMatrix alphas = MatrixFunctions.pow(
MatrixFunctions.pow(
this.highDimControlPoints.subRowVector(x),
2
).rowSums(),
-1
);
double alpha = alphas.sum();
DoubleMatrix xTilde = this.highDimControlPoints.mulColumnVector(alphas).columnSums().div(alpha);
DoubleMatrix yTilde = this.lowDimControlPoints.mulColumnVector(alphas).columnSums().div(alpha);
DoubleMatrix xHats = this.highDimControlPoints.subRowVector(xTilde);
@SuppressWarnings("SuspiciousNameCombination")
DoubleMatrix yHats = this.lowDimControlPoints.subRowVector(yTilde);
DoubleMatrix sqrtAlphas = MatrixFunctions.sqrt(alphas);
DoubleMatrix A = xHats.mulColumnVector(sqrtAlphas);
DoubleMatrix B = yHats.mulColumnVector(sqrtAlphas);
DoubleMatrix[] svdComposition = Singular.sparseSVD(A.transpose().mmul(B));
DoubleMatrix U = svdComposition[0];
DoubleMatrix V = svdComposition[2];
DoubleMatrix M = U.mmul(V);
return x.sub(xTilde).mmul(M).add(yTilde);
}
示例3: CPSR
import org.jblas.Singular; //导入方法依赖的package包/类
public CPSR(TrainingDataSet trainData, double minSingularVal, int maxSVDDim, int projDim, int maxHistLen, ProjType projType, boolean histCompress, boolean randStart)
{
super(trainData, minSingularVal, maxSVDDim, maxHistLen);
this.projType = projType;
this.histCompress = histCompress;
this.projDim = projDim;
tPhi = constructRandomMatrix(projType, projDim, tests.size(), projDim);
if(!histCompress)
{
hPhi = constructRandomMatrix(ProjType.Eye,histories.size()+1,histories.size()+1, 1.0);
}
else
{
hPhi = constructRandomMatrix(projType,histories.size()+1,projDim, projDim);
}
DoubleMatrix one = DoubleMatrix.zeros(histories.size()+1, 1);
this.randStart = randStart;
one.put(0,0, 1.0);
if(randStart)
{
for(int i = 0; i < one.getRows(); i++)
one.put(i,0,1.0);
}
if(histCompress)
{
DoubleMatrix[] hPhiSVD = Singular.sparseSVD(hPhi);
DoubleMatrix sigma = DoubleMatrix.diag(hPhiSVD[1]);
e = hPhiSVD[2].mmul(Solve.pinv(sigma)).mmul(hPhiSVD[0].transpose());
e = e.mmul(one);
}
else
{
e = one;
}
}
示例4: updateSVD
import org.jblas.Singular; //导入方法依赖的package包/类
protected DoubleMatrix[] updateSVD(DoubleMatrix newTHData)
{
DoubleMatrix biggerV = DoubleMatrix.concatVertically(svdResults[2],
DoubleMatrix.zeros(newTHData.getColumns()-svdResults[2].getRows(), svdResults[2].getColumns()));
DoubleMatrix m = svdResults[0].mmul(newTHData);
DoubleMatrix p = newTHData.sub((svdResults[0].transpose()).mmul(m));
DoubleMatrix pBase = Singular.sparseSVD(p)[0];
pBase = DoubleMatrix.concatHorizontally(pBase, DoubleMatrix.zeros(pBase.getRows(), p.getColumns()-pBase.getColumns()));
DoubleMatrix rA = (pBase.transpose()).mmul(p);
DoubleMatrix n = (biggerV.transpose()).mmul(DoubleMatrix.eye(newTHData.getColumns()));
DoubleMatrix q = DoubleMatrix.eye(newTHData.getColumns()).sub(biggerV.mmul(n));
DoubleMatrix qBase = Singular.sparseSVD(q)[0];
DoubleMatrix rB = (qBase.transpose()).mmul(q);
DoubleMatrix z = DoubleMatrix.zeros(m.getRows(), m.getColumns());
DoubleMatrix z2 = DoubleMatrix.zeros(m.getColumns(),m.getColumns());
DoubleMatrix top = DoubleMatrix.concatHorizontally(svdResults[1],z);
DoubleMatrix bottom = DoubleMatrix.concatHorizontally(z.transpose(),z2);
DoubleMatrix comb = DoubleMatrix.concatVertically(top, bottom);
DoubleMatrix mRA = DoubleMatrix.concatVertically(m, rA);
DoubleMatrix nRB = DoubleMatrix.concatVertically(n, rB);
DoubleMatrix mRANRB = mRA.mmul(nRB.transpose());
DoubleMatrix k = comb.add(mRANRB);
DoubleMatrix[] newBases = computeTruncatedSVD(k, Double.MIN_VALUE, maxDim);
DoubleMatrix uP = (DoubleMatrix.concatHorizontally(svdResults[0].transpose(), p)).mmul(newBases[0].transpose());
DoubleMatrix vP = (DoubleMatrix.concatHorizontally(biggerV,q)).mmul(newBases[2]);
DoubleMatrix[] updatedSVDResults = new DoubleMatrix[3];
updatedSVDResults[0] = uP.transpose();
updatedSVDResults[1] = newBases[1];
updatedSVDResults[2] = vP;
double[] singVals = new double[updatedSVDResults[1].getRows()];
for(int i = 0; i < updatedSVDResults[1].getRows(); i++)
singVals[i] = updatedSVDResults[1].get(i,i);
return updatedSVDResults;
}