/**
* Standardize the matrix entries by row- or column-wise z-scores (z=(x-u)/sigma)
*
* @param isByRow standardize by row if true; otherwise by column
*/
public void standardize(boolean isByRow) {
int iters = isByRow ? numRows : numColumns;
for (int iter = 0; iter < iters; iter++) {
SparseVector vec = isByRow ? row(iter) : column(iter);
if (vec.getCount() > 0) {
double[] data = vec.getData();
double mu = Stats.mean(data);
double sigma = Stats.sd(data, mu);
for (VectorEntry ve : vec) {
int idx = ve.index();
double val = ve.get();
double z = (val - mu) / sigma;
if (isByRow) this.set(iter, idx, z);
else this.set(idx, iter, z);
}
}
}
}
@Override
public Vector transMultAdd(double alpha, Vector x, Vector y) {
if (!(x instanceof DenseVector) || !(y instanceof DenseVector))
return super.transMultAdd(alpha, x, y);
checkTransMultAdd(x, y);
double[] xd = ((DenseVector) x).getData(), yd = ((DenseVector) y).getData();
// y = 1/alpha * y
y.scale(1. / alpha);
// y = A'x + y
for (int i = 0; i < numRows; ++i) {
SparseVector v = rowD[i];
int[] index = v.getIndex();
double[] data = v.getData();
int length = v.getUsed();
for (int j = 0; j < length; ++j) yd[index[j]] += data[j] * xd[i];
}
// y = alpha*y = alpha * A'x + y
return y.scale(alpha);
}