Mahout-简介

Mahout是一个Apache项目，它是一个开源的机器学习库。Mahout的目标是为大规模的数据挖掘、分类、聚类、协同过滤、推荐算法等提供可扩展的、高效的算法实现。

特点

Mahout的主要特点包括：

1. 可扩展性：Mahout的算法支持分布式计算，提供了高效的并行计算能力。
2. 支持多种数据源：Mahout支持多种数据源，包括Hadoop、Hbase、数据库等。
3. 提供易用的API：Mahout提供了易用的API，可以方便地使用和拓展。
4. 支持多种算法：Mahout支持多种机器学习和数据挖掘算法，例如协同过滤、聚类、分类等。
5. 社区活跃：Mahout有一个活跃的社区，不断改进和优化算法实现。

算法列表

Mahout支持多种机器学习和数据挖掘算法，包括但不限于以下算法：

1. 协同过滤算法：Mahout支持基于用户的协同过滤、基于物品的协同过滤等。
2. 聚类算法：Mahout支持k-means聚类、Canopy聚类、Fuzzy K-Means聚类等。
3. 分类算法：Mahout支持朴素贝叶斯分类、决策树分类等。
4. 降维算法：Mahout支持Singular Value Decomposition(SVD)、Principal Component Analysis(PCA)等。

官方文档和教程

Mahout有完整的官方文档和教程，包括以下内容：

1. Mahout的安装和配置。
2. Mahout的API介绍和使用教程。
3. Mahout算法实现的例子和案例分析。

示例代码

下面是一个Mahout实现K-Means聚类算法的示例代码：

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;

import org.apache.mahout.clustering.classify.WeightedVectorWritable;
import org.apache.mahout.common.AbstractJob;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.common.distance.CosineDistanceMeasure;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.common.iterator.sequencefile.PathType;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirIterator;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileValueIterator;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileWriter;
import org.apache.mahout.common.iterator.sequencefile.SequenceFile.Writer;
import org.apache.mahout.math.VectorWritable;

import java.io.IOException;
import java.util.Iterator;

public final class RandomSeedGenerator extends AbstractJob {

  public static final String POINTS_DIR_OPTION = "points";
  public static final String OUTPUT_DIR_OPTION = "output";
  public static final String NUM_CLUSTERS_OPTION = "k";
  public static final String OVERWRITE_OPTION = "overwrite";
  public static final String DISTANCE_MEASURE_OPTION = "distance";
  public static final String SEED_OPTION = "seed";

  public static void main(String[] args) throws Exception {
    new RandomSeedGenerator().run(args);
  }

  @Override
  public int run(String[] args) throws Exception {
    addOption("i", POINTS_DIR_OPTION, "The path to the directory containing the input vectors", true);
    addOption("o", OUTPUT_DIR_OPTION, "The path for the output directory.", true);
    addOption("k", NUM_CLUSTERS_OPTION, "The number of clusters to generate.", true);
    addOption("ow", OVERWRITE_OPTION, "If set, overwrite the output directory.");
    addOption("dm", DISTANCE_MEASURE_OPTION, "The Distance Measure to use. Defaults to CosineDistanceMeasure");
    addOption("s", SEED_OPTION, "The RNG seed to use. Default is random");

    if (parseArguments(args) == null) {
      return -1;
    }

    Path inputPath = getInputPath();
    Path outputPath = getOutputPath();
    int k = getOption(NUM_CLUSTERS_OPTION, 20);
    boolean overwrite = hasOption(OVERWRITE_OPTION);
    DistanceMeasure measure = getOption(DISTANCE_MEASURE_OPTION, CosineDistanceMeasure.class);

    if (hasOption(SEED_OPTION)) {
      RandomUtils.useTestSeed();
    }

    Configuration conf = getConf();

    HadoopUtil.delete(conf, outputPath);

    // read the input points and pick k of them as the initial centers
    FileSystem fs = FileSystem.get(inputPath.toUri(), conf);
    SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, outputPath, Text.class, WeightedVectorWritable.class);
    try {
      int i = 0;
      for (Iterator<Writable> iter = new SequenceFileValueIterator<Writable>(inputPath, true, conf); iter.hasNext();) {
        VectorWritable value = (VectorWritable) iter.next();
        writer.append(new Text("centroid-" + i++), new WeightedVectorWritable(1, value.get()));
        if (i >= k) {
          break;
        }
      }
    } finally {
      writer.close();
    }

    return 0;
  }
}