向MapReduce转换：通过部分成绩计算矩阵乘法

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代码共分为四部分：

<strong><span style="font-size:18px;">/***
 * @author YangXin
 * @info 封装共现关系列
 */
package unitSix;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.cf.taste.hadoop.item.VectorOrPrefWritable;
import org.apache.mahout.math.VectorWritable;

public class CooccurrenceColumnWrapperMapper extends Mapper<IntWritable, VectorWritable, IntWritable, VectorOrPrefWritable>{
	public void map(IntWritable key, VectorWritable value, Context context) throws IOException, InterruptedException{
		context.write(key, new VectorOrPrefWritable(value.get()));
	}
}
</span></strong>

<strong><span style="font-size:18px;">/***
 * @author YangXin
 * @info 分割用户数量
 */
package unitSix;

import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.cf.taste.hadoop.item.VectorOrPrefWritable;
import org.apache.mahout.math.VarLongWritable;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;

public class UserVectorSplitterMapper extends Mapper<VarLongWritable, VectorWritable, IntWritable, VectorOrPrefWritable>{
	public void map(VarLongWritable key, VectorWritable value, Context context) throws IOException, InterruptedException{
		long userID = key.get();
		Vector userVector = value.get();
		Iterator<Vector.Element> it = userVector.nonZeroes().iterator();
		IntWritable itemIndexWritable = new IntWritable();
		while(it.hasNext()){
			Vector.Element e = it.next();
			int itemIndex = e.index();
			float preferenceValue = (float)e.get();
			itemIndexWritable.set(itemIndex);
			context.write(itemIndexWritable, new VectorOrPrefWritable(userID, preferenceValue));
		}
	}
}</span></strong>

<strong><span style="font-size:18px;">/***
 * @author YangXin
 * @info 计算部分推荐向量
 */
package unitSix;
import java.io.IOException;
import java.util.List;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.cf.taste.hadoop.item.VectorAndPrefsWritable;
import org.apache.mahout.math.VarLongWritable;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;

public class PartialMultiplyMapper extends Mapper<IntWritable, VectorAndPrefsWritable, VarLongWritable, VectorWritable>{
	public void map(IntWritable key, VectorAndPrefsWritable vectorAndPrefsWritable, Context context) throws IOException, InterruptedException{
		Vector cooccurrenceColumn = vectorAndPrefsWritable.getVector();
		List<Long> userIDs = vectorAndPrefsWritable.getUserIDs();
		List<Float> prefValues = vectorAndPrefsWritable.getValues();
		for(int i = 0; i < userIDs.size(); i++){
			long userID = userIDs.get(i);
			float prefValue = prefValues.get(i);
			Vector partialProduct = cooccurrenceColumn.times(prefValue);
			context.write(new VarLongWritable(userID), new VectorWritable(partialProduct));;
		}
	}
}
</span></strong>

<strong><span style="font-size:18px;">/***
 * @author YangXin
 * @info 实现部分成绩的combiner
 */
package unitSix;

import java.io.IOException;

import org.apache.hadoop.mapreduce.Reducer;
import org.apache.mahout.math.VarLongWritable;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;

public class AggregateCombiner extends Reducer<VarLongWritable, VectorWritable, VarLongWritable, VectorWritable>{	
	public void reduce(VarLongWritable key, Iterable<VectorWritable> values, Context context) throws IOException, InterruptedException{
		Vector partial = null;
		for(VectorWritable vectorWritable : values){
			partial = partial == null ? vectorWritable.get() : partial.plus(vectorWritable.get());
		}
		context.write(key, new VectorWritable(partial));
	}
}
</span></strong>

向MapReduce转换：通过部分成绩计算矩阵乘法

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原文地址：http://blog.csdn.net/u012965373/article/details/50715798