Is there a way on ojAlgo to normalize a Matrix?

I am interested to know if there is a way to find a normalized form for a MatrixStore using the ojAlgo matrix library.

Perhaps a routine or a task that once performed on a MatrixStore will cause each of the rows to have a mean of 0 and a standard deviation of 1.

If one is familiar with sklearn, what I'm looking for is some function on ojAlgo that functions similarly like the preprocessing module on sklearn.

Not directly. You have to write some loops and calculations yourself. Here's one possible way to do it:

PrimitiveDenseStore matrix = ...;

SampleSet sampleSet = SampleSet.make();
for (int j = 0; j < matrix.countColumns(); j++) {
    sampleSet.swap(matrix.sliceColumn(j));
    for (int i = 0; i < matrix.countRows(); i++) {
        matrix.set(i, j, sampleSet.getStandardScore(i));
    }
}

With ojAlgo I strongly recommend organising data in columns.

I didn't actually test that code. Possibly there could be a problem to update the matrix in-place like this.

...

With v47.1.1 (just released) it is now possible to do it this way:

matrix.modifyAny(DataPreprocessors.STANDARD_SCORE);