[mlpack-svn] r10390 - in mlpack/trunk/src/mlpack/methods: . gmm

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

Author: rcurtin
Date: 2011-11-24 02:25:55 -0500 (Thu, 24 Nov 2011)
New Revision: 10390

Removed:
   mlpack/trunk/src/mlpack/methods/nnsvm/
   mlpack/trunk/src/mlpack/methods/svm/
Modified:
   mlpack/trunk/src/mlpack/methods/CMakeLists.txt
   mlpack/trunk/src/mlpack/methods/gmm/gmm.cpp
   mlpack/trunk/src/mlpack/methods/gmm/gmm.hpp
Log:
These methods are unmaintainable and will have to be re-implemented.


Modified: mlpack/trunk/src/mlpack/methods/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/CMakeLists.txt	2011-11-24 07:14:00 UTC (rev 10389)
+++ mlpack/trunk/src/mlpack/methods/CMakeLists.txt	2011-11-24 07:25:55 UTC (rev 10390)
@@ -11,10 +11,8 @@
   #mvu  # (currently known to not work)
   naive_bayes
   nca
-  pca
   neighbor_search
-  nnsvm
-  svm
+  pca
 )
 
 foreach(dir ${DIRS})

Modified: mlpack/trunk/src/mlpack/methods/gmm/gmm.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/gmm/gmm.cpp	2011-11-24 07:14:00 UTC (rev 10389)
+++ mlpack/trunk/src/mlpack/methods/gmm/gmm.cpp	2011-11-24 07:25:55 UTC (rev 10390)
@@ -13,6 +13,26 @@
 using namespace mlpack;
 using namespace gmm;
 
+double GMM::Probability(const arma::vec& observation) const
+{
+  // Sum the probability for each Gaussian in our mixture (and we have to
+  // multiply by the prior for each Gaussian too).
+  double sum = 0;
+  for (size_t i = 0; i < gaussians; i++)
+    sum += weights[i] * phi(observation, means[i], covariances[i]);
+
+  return sum;
+}
+
+/**
+ * Return a randomly generated observation according to the probability
+ * distribution defined by this object.
+ */
+arma::vec GMM::Random() const
+{
+  return "0 0";
+}
+
 void GMM::ExpectationMaximization(const arma::mat& data)
 {
   // Create temporary models and set to the right size.

Modified: mlpack/trunk/src/mlpack/methods/gmm/gmm.hpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/gmm/gmm.hpp	2011-11-24 07:14:00 UTC (rev 10389)
+++ mlpack/trunk/src/mlpack/methods/gmm/gmm.hpp	2011-11-24 07:25:55 UTC (rev 10390)
@@ -84,49 +84,28 @@
   //! Return a reference to the a priori weights of each Gaussian.
   arma::vec& Weights() { return weights; }
 
+
   /**
-   * This function outputs the parameters of the model
-   * to an arraylist of doubles
+   * Return the probability that the given observation came from this
+   * distribution.
    *
-   * @code
-   * ArrayList<double> results;
-   * mog.OutputResults(&results);
-   * @endcode
+   * @param observation Observation to evaluate the probability of.
    */
-  void OutputResults(std::vector<double>& results)
-  {
-    // Initialize the size of the output array
-    results.resize(gaussians * (1 + dimension * (1 + dimension)));
+  double Probability(const arma::vec& observation) const;
 
-    // Copy values to the array from the private variables of the class
-    for (size_t i = 0; i < gaussians; i++)
-    {
-      results[i] = weights[i];
-      for (size_t j = 0; j < dimension; j++)
-      {
-        results[gaussians + (i * dimension) + j] = (means[i])[j];
-        for (size_t k = 0; k < dimension; k++)
-        {
-          results[gaussians * (1 + dimension) +
-              (i * dimension * dimension) + (j * dimension) + k] =
-              (covariances[i])(j, k);
-        }
-      }
-    }
-  }
-
   /**
-   * This function calculates the parameters of the model
-   * using the Maximum Likelihood function via the
-   * Expectation Maximization (EM) Algorithm.
+   * Return a randomly generated observation according to the probability
+   * distribution defined by this object.
    *
-   * @code
-   * MoG mog;
-   * Matrix data = "the data on which you want to fit the model";
-   * ArrayList<double> results;
-   * mog.ExpectationMaximization(data, &results);
-   * @endcode
+   * @return Random observation from this GMM.
    */
+  arma::vec Random() const;
+
+  /**
+   * This function estimates the parameters of the Gaussian Mixture Model using
+   * the Maximum Likelihood estimator, via the EM (Expectation Maximization)
+   * algorithm.
+   */
   void ExpectationMaximization(const arma::mat& data_points);
 
   /**