org.apache.sysml.runtime.matrix.data.BinaryBlockToBinaryCellConverter Maven / Gradle / Ivy

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Declarative Machine Learning
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/*
 * 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 org.apache.sysml.runtime.util.UtilFunctions;



public class BinaryBlockToBinaryCellConverter implements 
Converter
{
	
	private SparseRowsIterator 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.cellIndexCalculation(k1.getRowIndex(), brow,0), 
				UtilFunctions.cellIndexCalculation(k1.getColumnIndex(),bcolumn,0));
		sparse=v1.isInSparseFormat();
		thisBlockWidth=v1.getNumColumns();
		if(sparse)
		{
			sparseIterator=v1.getSparseRowsIterator();
		}
		else
		{
			if(v1.getDenseArray()==null)
				return;
			denseArray=v1.getDenseArray();
			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.i + startIndexes.getRowIndex();
				j = tmpcell.j + startIndexes.getColumnIndex();
				v = tmpcell.v;
			}
				
		}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;
	}
}