本文整理汇总了Java中org.ejml.simple.SimpleSVD.getW方法的典型用法代码示例。如果您正苦于以下问题：Java SimpleSVD.getW方法的具体用法？Java SimpleSVD.getW怎么用？Java SimpleSVD.getW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.ejml.simple.SimpleSVD的用法示例。
在下文中一共展示了SimpleSVD.getW方法的2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: getSigmaPoints
import org.ejml.simple.SimpleSVD; //导入方法依赖的package包/类
/**
* Returns 2n+k sigma points starting with mean as the first point
*
* @param mean
* @param cov
* @param no
* @param k
* @return
*/
private static List<SimpleMatrix> getSigmaPoints(SimpleMatrix mean, SimpleMatrix cov, int no, int k) {
List<SimpleMatrix> resultVectors = new ArrayList<SimpleMatrix>();
int n = cov.numRows();
SimpleSVD<?> svd = cov.svd(true);
SimpleMatrix U = svd.getU();
SimpleMatrix S = svd.getW();
S = U.mult(MatrixOps.elemSqrt(S)).scale(Math.sqrt(n + k));
for (int i = 0; i < S.numCols(); i++) {
SimpleMatrix columnVector = S.extractVector(false, i);
SimpleMatrix negColumnVector = S.extractVector(false, i).scale(-1);
resultVectors.add(columnVector.plus(mean));
resultVectors.add(negColumnVector.plus(mean));
}
if (k != 0)
resultVectors.add(mean);
return resultVectors;
}
示例2: split
import org.ejml.simple.SimpleSVD; //导入方法依赖的package包/类
/**
* Splits a single component distribution into two components as described in the oKDE-paper.
* @return a TwoComponentDistribution
*/
public TwoComponentDistribution split(double parentWeight){
SimpleSVD<?> svd = mGlobalCovariance.svd(true);
SimpleMatrix S = svd.getW();
SimpleMatrix V = svd.getV();
SimpleMatrix d = S.extractDiag();
double max = MatrixOps.maxVectorElement(d);
int maxIndex = MatrixOps.maxVectorElementIndex(d);
int len = mGlobalCovariance.numRows();
SimpleMatrix M = new SimpleMatrix(len,1);
M.set(maxIndex, 0, 1.0d);
SimpleMatrix dMean = V.mult(M).scale(0.5*Math.sqrt(max));
SimpleMatrix meanSplit1 = mGlobalMean.plus(dMean);
SimpleMatrix meanSplit2 = mGlobalMean.minus(dMean);
SimpleMatrix dyadMean = mGlobalMean.mult(mGlobalMean.transpose());
SimpleMatrix dyadMeanSplit1 = meanSplit1.mult(meanSplit1.transpose());
SimpleMatrix dyadMeanSplit2 = meanSplit2.mult(meanSplit2.transpose());
SimpleMatrix covSplit = mGlobalCovariance.plus(dyadMean).minus(dyadMeanSplit1.plus(dyadMeanSplit2).scale(0.5));
SimpleMatrix[] means = {meanSplit1, meanSplit2};
SimpleMatrix[] covariances = {covSplit, covSplit};
double[] weights = {0.5, 0.5};
TwoComponentDistribution splitDist = null;
try {
splitDist = new TwoComponentDistribution(weights, means, covariances, mBandwidthMatrix);
splitDist.setGlobalWeight(parentWeight*mGlobalWeight);
splitDist.setGlobalCovariance(mGlobalCovariance);
splitDist.setGlobalMean(mGlobalMean);
} catch (TooManyComponentsException e) {
// cant be thrown
}
return splitDist;
}