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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.controlprogram.parfor.util;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.io.Serializable;

import org.apache.hadoop.io.Writable;

import org.apache.sysml.runtime.matrix.data.MatrixCell;
import org.apache.sysml.runtime.matrix.data.MatrixIndexes;

/**
 * Custom writable for a pair of matrix indexes and matrix cell
 * as required for binarycell in remote data partitioning.
 * 
 */
public class PairWritableCell implements Writable, Serializable
{

	private static final long serialVersionUID = 6835655576808999370L;
	
	public MatrixIndexes indexes;
	public MatrixCell cell;
	
	@Override
	public void readFields(DataInput in) throws IOException 
	{
		indexes = new MatrixIndexes();
		indexes.readFields(in);
		
		cell = new MatrixCell();
		cell.readFields(in);
	}

	@Override
	public void write(DataOutput out) throws IOException 
	{
		indexes.write(out);
		cell.write(out);
	}
}