org.apache.sysml.runtime.matrix.data.BinaryBlockToBinaryCellConverter Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of systemml Show documentation
Show all versions of systemml Show documentation
Declarative Machine Learning
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package org.apache.sysml.runtime.matrix.data;
import java.util.Iterator;
import org.apache.sysml.runtime.util.UtilFunctions;
public class BinaryBlockToBinaryCellConverter implements
Converter
{
private Iterator sparseIterator=null;
private double[] denseArray=null;
private int denseArraySize=0;
private int nextInDenseArray=-1;
private boolean sparse=true;
private int thisBlockWidth=0;
private MatrixIndexes startIndexes=new MatrixIndexes();
private boolean hasValue=false;
private int brow;
private int bcolumn;
private MatrixIndexes returnIndexes=new MatrixIndexes();
private MatrixCell cell=new MatrixCell();
private Pair pair=new Pair(returnIndexes, cell);
private void reset()
{
sparseIterator=null;
denseArray=null;
denseArraySize=0;
nextInDenseArray=-1;
sparse=true;
thisBlockWidth=0;
}
@Override
public void convert(MatrixIndexes k1, MatrixBlock v1) {
reset();
startIndexes.setIndexes(UtilFunctions.computeCellIndex(k1.getRowIndex(), brow,0),
UtilFunctions.computeCellIndex(k1.getColumnIndex(),bcolumn,0));
sparse=v1.isInSparseFormat();
thisBlockWidth=v1.getNumColumns();
if(sparse)
{
sparseIterator=v1.getSparseBlockIterator();
}
else
{
if(v1.getDenseBlock()==null)
return;
denseArray=v1.getDenseBlock();
nextInDenseArray=0;
denseArraySize=v1.getNumRows()*v1.getNumColumns();
}
hasValue=(v1.getNonZeros()>0);
}
@Override
public boolean hasNext() {
if(sparse)
{
if(sparseIterator==null)
hasValue=false;
else
hasValue=sparseIterator.hasNext();
}else
{
if(denseArray==null)
hasValue=false;
else
{
while(nextInDenseArray next() {
if(!hasValue)
return null;
long i, j;
double v;
if(sparse)
{
if(sparseIterator==null)
return null;
else
{
IJV tmpcell = sparseIterator.next();
i = tmpcell.getI() + startIndexes.getRowIndex();
j = tmpcell.getJ() + startIndexes.getColumnIndex();
v = tmpcell.getV();
}
}else
{
if(denseArray==null)
return null;
else
{
i=startIndexes.getRowIndex() + nextInDenseArray/thisBlockWidth;
j=startIndexes.getColumnIndex() + nextInDenseArray%thisBlockWidth;
v=denseArray[nextInDenseArray];
nextInDenseArray++;
}
}
returnIndexes.setIndexes(i,j);
cell.setValue(v);
return pair;
}
public void setBlockSize(int nr, int nc) {
brow=nr;
bcolumn=nc;
}
}