mapreduce：Kmeans算法处理西瓜数据集代码

以下是使用MapReduce实现K-Means算法处理西瓜数据集的代码示例：

Map函数：

public static class KMeansMap extends Mapper<LongWritable, Text, IntWritable, Text> {

    private List<Vector> centers = new ArrayList<Vector>();

    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        Configuration conf = context.getConfiguration();
        String centerFilePath = conf.get("centerFilePath");
        FileSystem fs = FileSystem.get(conf);
        Path centerPath = new Path(centerFilePath);
        BufferedReader br = new BufferedReader(new InputStreamReader(fs.open(centerPath)));
        String line;
        while ((line = br.readLine()) != null) {
            String[] centerStr = line.split(",");
            double[] center = new double[centerStr.length];
            for (int i = 0; i < centerStr.length; i++) {
                center[i] = Double.parseDouble(centerStr[i]);
            }
            centers.add(new DenseVector(center));
        }
        br.close();
    }

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] dataStr = value.toString().split(",");
        double[] data = new double[dataStr.length];
        for (int i = 0; i < dataStr.length; i++) {
            data[i] = Double.parseDouble(dataStr[i]);
        }
        Vector vector = new DenseVector(data);
        int nearestCenterIndex = 0;
        double minDistance = Double.MAX_VALUE;
        for (int i = 0; i < centers.size(); i++) {
            double distance = DistanceMeasure.cosine(centers.get(i), vector);
            if (distance < minDistance) {
                minDistance = distance;
                nearestCenterIndex = i;
            }
        }
        context.write(new IntWritable(nearestCenterIndex), value);
    }
}

Reduce函数：

public static class KMeansReduce extends Reducer<IntWritable, Text, IntWritable, Text> {

    @Override
    protected void reduce(IntWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        List<Vector> vectors = new ArrayList<Vector>();
        for (Text value : values) {
            String[] dataStr = value.toString().split(",");
            double[] data = new double[dataStr.length];
            for (int i = 0; i < dataStr.length; i++) {
                data[i] = Double.parseDouble(dataStr[i]);
            }
            Vector vector = new DenseVector(data);
            vectors.add(vector);
        }
        Vector newCenter = KMeansUtil.calculateCenter(vectors);
        context.write(key, new Text(KMeansUtil.vectorToString(newCenter)));
    }
}

Driver函数：

public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    conf.set("centerFilePath", args[2]);
    Job job = Job.getInstance(conf);
    job.setJobName("KMeans");

    job.setOutputKeyClass(IntWritable.class);
    job.setOutputValueClass(Text.class);

    job.setMapperClass(KMeansMap.class);
    job.setReducerClass(KMeansReduce.class);

    job.setInputFormatClass(TextInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);

    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    job.waitForCompletion(true);
}

这里假设KMeansUtil类中已经实现了calculateCenter和vectorToString两个方法，分别用于计算新的聚类中心和将向量转换为字符串。