[mlpack-svn] r15435 - mlpack/conf/jenkins-conf/benchmark/methods/weka/src/kmeans

[fastlab-svn at coffeetalk-1.cc.gatech.edu]

Author: marcus
Date: Mon Jul  8 12:19:33 2013
New Revision: 15435

Log:
Add weka K-Means method src.

Added:
   mlpack/conf/jenkins-conf/benchmark/methods/weka/src/kmeans/
   mlpack/conf/jenkins-conf/benchmark/methods/weka/src/kmeans/KMeans.java

Added: mlpack/conf/jenkins-conf/benchmark/methods/weka/src/kmeans/KMeans.java
==============================================================================
--- (empty file)
+++ mlpack/conf/jenkins-conf/benchmark/methods/weka/src/kmeans/KMeans.java	Mon Jul  8 12:19:33 2013
@@ -0,0 +1,101 @@
+/**
+ * @file KMeans.java
+ * @author Marcus Edel
+ *
+ * K-Means Clustering with weka.
+ */
+
+import weka.clusterers.SimpleKMeans;
+import weka.core.Instances;
+import weka.core.Utils;
+import weka.core.converters.ConverterUtils.DataSource;
+
+/**
+ * This class use the weka libary to implement K-Means Clustering.
+ */
+public class KMeans {
+
+  private static final String USAGE = String
+      .format("This program performs K-Means clustering on the given dataset.\n\n"
+          + "Required options:\n"
+          + "(-c) [int]         Number of clusters to find.\n"
+          + "(-i) [string]      Input dataset to perform clustering on."
+          + "-m) [int]          Maximum number of iterations before K-Means\n"
+          + "                   terminates.  Default value 1000.\n"
+          + "(-s) [int]         Random seed. ");
+
+  public static void main(String args[]) {
+    Timers timer = new Timers();
+    try {
+      // Get the data set path.
+      String inputFile = Utils.getOption('i', args);
+      if (inputFile.length() == 0)
+        throw new IllegalArgumentException();
+      
+      // Load input dataset.
+      DataSource source = new DataSource(inputFile);
+      Instances data = source.getDataSet();
+      
+      // Create the KMeans object.
+      SimpleKMeans kmeans = new SimpleKMeans();
+      
+      // Gather parameters and validation of options.
+      String maxIteration = Utils.getOption('m', args);
+      int m = 1000;
+      if (maxIteration.length() != 0)
+      {
+        m = Integer.parseInt(maxIteration);
+        if (m < 0)
+        {
+          System.out.println("[Fatal] Invalid value for maximum iterations(" + 
+              maxIteration + ")! Must be greater than or equal to 0..");
+          System.exit(-1);          
+        }
+        else if(m == 0)
+        {
+          m = Integer.MAX_VALUE;
+        }
+      }
+      
+      String clusters = Utils.getOption('c', args);
+      if (clusters.length() == 0)
+      {
+        throw new IllegalArgumentException();
+      }
+      else
+      {
+        int c = Integer.parseInt(clusters);
+        if (c < 1)
+        {
+          System.out.println("[Fatal] Invalid number of clusters requested (" + 
+              clusters + ")! Must be greater than or equal to 1.");
+          System.exit(-1);
+        }
+        
+        kmeans.setNumClusters(c);
+      }     
+      
+      String seed = Utils.getOption('s', args);
+      if (seed.length() != 0)
+        kmeans.setSeed(Integer.parseInt(seed));
+            
+      kmeans.setMaxIterations(m);     
+      kmeans.setPreserveInstancesOrder(true);   
+      
+      // Perform K-Means clustering.
+      timer.StartTimer("total_time");
+      
+      kmeans.buildClusterer(data);
+      int[] assignments = kmeans.getAssignments();
+      
+      timer.StopTimer("total_time");
+      timer.PrintTimer("total_time");
+      
+    } catch (IllegalArgumentException e) {
+      System.err.println(USAGE);
+    } catch (Exception e) {
+      e.printStackTrace();
+    }
+  }
+
+}