[mlpack-svn] r10427 - mlpack/trunk/src/mlpack/methods/kmeans

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

Author: rcurtin
Date: 2011-11-27 02:59:03 -0500 (Sun, 27 Nov 2011)
New Revision: 10427

Added:
   mlpack/trunk/src/mlpack/methods/kmeans/kmeans_main.cpp
Modified:
   mlpack/trunk/src/mlpack/methods/kmeans/CMakeLists.txt
Log:
Executable for K-Means.  Celebrate!


Modified: mlpack/trunk/src/mlpack/methods/kmeans/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/kmeans/CMakeLists.txt	2011-11-27 06:32:33 UTC (rev 10426)
+++ mlpack/trunk/src/mlpack/methods/kmeans/CMakeLists.txt	2011-11-27 07:59:03 UTC (rev 10427)
@@ -19,3 +19,11 @@
 # Append sources (with directory name) to list of all MLPACK sources (used at
 # the parent scope).
 set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
+
+# The main K-Means executable.
+add_executable(kmeans
+  kmeans_main.cpp
+)
+target_link_libraries(kmeans
+  mlpack
+)

Added: mlpack/trunk/src/mlpack/methods/kmeans/kmeans_main.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/kmeans/kmeans_main.cpp	                        (rev 0)
+++ mlpack/trunk/src/mlpack/methods/kmeans/kmeans_main.cpp	2011-11-27 07:59:03 UTC (rev 10427)
@@ -0,0 +1,134 @@
+/**
+ * @file kmeans_main.cpp
+ * @author Ryan Curtin
+ *
+ * Executable for running K-Means.
+ */
+#include <mlpack/core.hpp>
+
+#include "kmeans.hpp"
+#include "allow_empty_clusters.hpp"
+
+using namespace mlpack;
+using namespace mlpack::kmeans;
+using namespace std;
+
+// Define parameters for the executable.
+PROGRAM_INFO("K-Means Clustering", "This program performs K-Means clustering "
+    "on the given dataset, storing the learned cluster assignments either as "
+    "a column of labels in the file containing the input dataset or in a "
+    "separate file.  Empty clusters are not allowed by default; when a cluster "
+    "becomes empty, the point furthest from the centroid of the cluster with "
+    "maximum variance is taken to fill that cluster.", "");
+
+PARAM_STRING_REQ("input_file", "Input dataset to perform clustering on.", "");
+PARAM_INT_REQ("clusters", "Number of clusters to find.", "");
+PARAM_FLAG("in_place", "If specified, a column of the learned cluster "
+    "assignments will be added to the input dataset file.  In this case "
+    "--output_file is not necessary.", "");
+PARAM_STRING("output_file", "File to write output labels to.", "", "");
+PARAM_FLAG("allow_empty_clusters", "Allow empty clusters to be created.", "");
+PARAM_FLAG("labels_only", "Only output labels into output file.", "");
+PARAM_DOUBLE("overclustering", "Finds (overclustering * clusters) clusters, "
+    "then merges them together until only the desired number of clusters are "
+    "left.", "", 1.0);
+PARAM_INT("max_iterations", "Maximum number of iterations before K-Means "
+    "terminates.", "", 1000);
+
+int main(int argc, char** argv)
+{
+  CLI::ParseCommandLine(argc, argv);
+
+  // Initialize random seed -- because that makes a difference.
+  srand(time(NULL));
+
+  // Now do validation of options.
+  string input_file = CLI::GetParam<string>("input_file");
+  int clusters = CLI::GetParam<int>("clusters");
+  if (clusters < 1)
+  {
+    Log::Fatal << "Invalid number of clusters requested (" << clusters << ")! "
+        << "Must be greater than or equal to 1." << std::endl;
+  }
+
+  int maxIterations = CLI::GetParam<int>("max_iterations");
+  if (maxIterations < 0)
+  {
+    Log::Fatal << "Invalid value for maximum iterations (" << maxIterations <<
+        ")! Must be greater than or equal to 0." << std::endl;
+  }
+
+  double overclustering = CLI::GetParam<double>("overclustering");
+  if (overclustering < 1)
+  {
+    Log::Fatal << "Invalid value for overclustering (" << overclustering <<
+        ")! Must be greater than or equal to 1." << std::endl;
+  }
+
+  // Make sure we have an output file if we're not doing the work in-place.
+  if (!CLI::HasParam("in_place") && !CLI::HasParam("output_file"))
+  {
+    Log::Fatal << "--output_file not specified (and --in_place not set)."
+        << std::endl;
+  }
+
+  // Load our dataset.
+  arma::mat dataset;
+  data::Load(input_file.c_str(), dataset);
+
+  // Now create the KMeans object.  Because we could be using different types,
+  // it gets a little weird...
+  arma::Col<size_t> assignments;
+  if (CLI::HasParam("allow_empty_clusters"))
+  {
+    KMeans<metric::SquaredEuclideanDistance, RandomPartition,
+        AllowEmptyClusters> k(maxIterations, overclustering);
+
+    k.Cluster(dataset, clusters, assignments);
+  }
+  else
+  {
+    KMeans<> k(maxIterations, overclustering);
+
+    k.Cluster(dataset, clusters, assignments);
+  }
+
+  // Now figure out what to do with our results.
+  if (CLI::HasParam("in_place"))
+  {
+    // Add the column of assignments to the dataset; but we have to convert them
+    // to type double first.
+    arma::vec converted(assignments.n_elem);
+    for (size_t i = 0; i < assignments.n_elem; i++)
+      converted(i) = (double) assignments(i);
+
+    dataset.insert_rows(dataset.n_rows, converted);
+
+    // Save the dataset.
+    data::Save(input_file.c_str(), dataset);
+  }
+  else
+  {
+    if (CLI::HasParam("only_labels"))
+    {
+      // Save only the labels.
+      string output_file = CLI::GetParam<string>("output_file");
+      arma::Mat<size_t> output = trans(assignments);
+      data::Save(output_file.c_str(), output);
+    }
+    else
+    {
+      // Convert the assignments to doubles.
+      arma::vec converted(assignments.n_elem);
+      for (size_t i = 0; i < assignments.n_elem; i++)
+        converted(i) = (double) assignments(i);
+
+      dataset.insert_rows(dataset.n_rows, converted);
+
+      // Now save, in the different file.
+      string output_file = CLI::GetParam<string>("output_file");
+      data::Save(output_file.c_str(), dataset);
+    }
+  }
+}
+