/** @return the cardinary of current matrix */
public int size() {
int size = 0;
for (MatrixEntry me : this) if (me.get() != 0) size++;
return size;
}
/**
* Normalize the matrix entries to (0, 1) by (x-min)/(max-min)
*
* @param min minimum value
* @param max maximum value
*/
public void normalize(double min, double max) {
assert max > min;
for (MatrixEntry me : this) {
double entry = me.get();
if (entry != 0) me.set((entry - min) / (max - min));
}
}
/** @return the data table of this matrix as (row, column, value) cells */
public Table<Integer, Integer, Double> getDataTable() {
Table<Integer, Integer, Double> res = HashBasedTable.create();
for (MatrixEntry me : this) {
if (me.get() != 0) res.put(me.row(), me.column(), me.get());
}
return res;
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append(String.format("%d\t%d\t%d\n", new Object[] {numRows, numColumns, size()}));
for (MatrixEntry me : this)
if (me.get() != 0)
sb.append(
String.format("%d\t%d\t%f\n", new Object[] {me.row() + 1, me.column() + 1, me.get()}));
return sb.toString();
}
/** @return the transpose of current matrix */
public SparseMatrix transpose() {
if (isCCSUsed) {
SparseMatrix tr = new SparseMatrix(numColumns, numRows);
tr.copyCRS(this.rowData, this.rowPtr, this.colInd);
tr.copyCCS(this.colData, this.colPtr, this.rowInd);
return tr;
} else {
Table<Integer, Integer, Double> dataTable = HashBasedTable.create();
for (MatrixEntry me : this) dataTable.put(me.column(), me.row(), me.get());
return new SparseMatrix(numColumns, numRows, dataTable);
}
}
@Override
public RQ factor(DenseMatrix A) {
if (Q.numRows() != A.numRows())
throw new IllegalArgumentException("Q.numRows() != A.numRows()");
else if (Q.numColumns() != A.numColumns())
throw new IllegalArgumentException("Q.numColumns() != A.numColumns()");
else if (R == null) throw new IllegalArgumentException("R == null");
/*
* Calculate factorisation, and extract the triangular factor
*/
intW info = new intW(0);
LAPACK.getInstance().dgerqf(m, n, A.getData(), Matrices.ld(m), tau, work, work.length, info);
if (info.val < 0) throw new IllegalArgumentException();
R.zero();
for (MatrixEntry e : A)
if (e.column() >= (n - m) + e.row()) R.set(e.row(), e.column() - (n - m), e.get());
/*
* Generate the orthogonal matrix
*/
info.val = 0;
LAPACK
.getInstance()
.dorgrq(m, n, k, A.getData(), Matrices.ld(m), tau, workGen, workGen.length, info);
if (info.val < 0) throw new IllegalArgumentException();
Q.set(A);
return this;
}
@Override
void copy(Matrix A) {
for (MatrixEntry e : A) if (e.row() >= e.column()) set(e.row(), e.column(), e.get());
}
@Test
public void testIterator() throws Exception {
File f = getSparseBinarySVDLIBCFile();
Iterator<MatrixEntry> it = new SvdlibcSparseBinaryFileIterator(f);
MatrixEntry me = it.next();
// Col 0
assertEquals(0, me.column());
assertEquals(0, me.row());
me = it.next();
assertEquals(0, me.column());
assertEquals(2, me.row());
me = it.next();
// Col 1
assertEquals(1, me.column());
assertEquals(1, me.row());
me = it.next();
// Col 2
assertEquals(2, me.column());
assertEquals(0, me.row());
me = it.next();
assertEquals(2, me.column());
assertEquals(1, me.row());
me = it.next();
assertEquals(2, me.column());
assertEquals(2, me.row());
assertFalse(it.hasNext());
}
