[mlpack-svn] r11669 - mlpack/trunk/src/mlpack/methods/mvu

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

Author: rcurtin
Date: 2012-02-29 17:48:40 -0500 (Wed, 29 Feb 2012)
New Revision: 11669

Modified:
   mlpack/trunk/src/mlpack/methods/mvu/CMakeLists.txt
   mlpack/trunk/src/mlpack/methods/mvu/mvu.cpp
   mlpack/trunk/src/mlpack/methods/mvu/mvu.hpp
Log:
Some changes to mvu.  Not 100% sure if it works yet.


Modified: mlpack/trunk/src/mlpack/methods/mvu/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/mvu/CMakeLists.txt	2012-02-29 22:41:10 UTC (rev 11668)
+++ mlpack/trunk/src/mlpack/methods/mvu/CMakeLists.txt	2012-02-29 22:48:40 UTC (rev 11669)
@@ -5,8 +5,6 @@
 set(SOURCES
   mvu.hpp
   mvu.cpp
-  mvu_objective_function.hpp
-  mvu_objective_function.cpp
 )
 
 # Add directory name to sources.

Modified: mlpack/trunk/src/mlpack/methods/mvu/mvu.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/mvu/mvu.cpp	2012-02-29 22:41:10 UTC (rev 11668)
+++ mlpack/trunk/src/mlpack/methods/mvu/mvu.cpp	2012-02-29 22:48:40 UTC (rev 11669)
@@ -5,11 +5,12 @@
  * Implementation of the MVU class and its auxiliary objective function class.
  */
 #include "mvu.hpp"
-#include "mvu_objective_function.hpp"
 
 //#include <mlpack/core/optimizers/aug_lagrangian/aug_lagrangian.hpp>
 #include <mlpack/core/optimizers/lrsdp/lrsdp.hpp>
 
+#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
+
 using namespace mlpack;
 using namespace mlpack::mvu;
 using namespace mlpack::optimization;
@@ -23,16 +24,80 @@
                  const size_t numNeighbors,
                  arma::mat& outputData)
 {
-  // Set up LRSDP that we will solve.
-//  LRSDP mvuSolver;
+  // First we have to choose the output point.  We'll take a linear projection
+  // of the data for now (this is probably not a good final solution).
+  outputData = trans(data.rows(0, newDim));
 
+  // The number of constraints is the number of nearest neighbors plus one.
+  LRSDP mvuSolver(numNeighbors * data.n_cols + 1, outputData);
 
-  MVUObjectiveFunction obj(data, newDim, numNeighbors);
+  // Set up the objective.  Because we are maximizing the trace of (R R^T),
+  // we'll instead state it as min(-I_n * (R R^T)), meaning C() is -I_n.
+  mvuSolver.C().eye(newDim, newDim);
+  mvuSolver.C() *= -1;
 
-  // Set up Augmented Lagrangian method.
-  // Memory choice is arbitrary; this needs to be configurable.
-//AugLagrangian<MVUObjectiveFunction> aug(obj, 20);
+  // Now set up each of the constraints.
+  // The first constraint is trace(ones * R * R^T) = 0.
+  mvuSolver.B()[0] = 0;
+  mvuSolver.A()[0].ones(newDim, newDim);
 
-//  outputData = obj.GetInitialPoint();
-//  aug.Optimize(outputData, 0);
+  // All of our other constraints will be sparse except the first.  So set that
+  // vector of modes accordingly.
+  mvuSolver.AModes().ones();
+  mvuSolver.AModes()[0] = 0;
+
+  // Now all of the other constraints.  We first have to run AllkNN to get the
+  // list of nearest neighbors.
+  arma::Mat<size_t> neighbors;
+  arma::mat distances;
+
+  AllkNN allknn(data);
+  allknn.Search(numNeighbors, neighbors, distances);
+
+  // Add each of the other constraints.  They are sparse constraints:
+  //   Tr(A_ij K) = d_ij;
+  //   A_ij = zeros except for 1 at (i, i), (j, j); -1 at (i, j), (j, i).
+  for (size_t i = 0; i < neighbors.n_cols; ++i)
+  {
+    for (size_t j = 0; j < numNeighbors; ++j)
+    {
+      // This is the index of the constraint.
+      const size_t index = (i * numNeighbors) + j + 1;
+
+      arma::mat& aRef = mvuSolver.A()[index];
+
+      aRef.set_size(2, 4);
+
+      // A_ij(i, i) = 1.
+      aRef(0, 0) = i;
+      aRef(1, 0) = i;
+      aRef(2, 0) = 1;
+
+      // A_ij(i, j) = -1.
+      aRef(0, 1) = i;
+      aRef(1, 1) = neighbors(j, i);
+      aRef(2, 1) = -1;
+
+      // A_ij(j, i) = -1.
+      aRef(0, 2) = neighbors(j, i);
+      aRef(1, 2) = i;
+      aRef(2, 2) = -1;
+
+      // A_ij(j, j) = 1.
+      aRef(0, 3) = neighbors(j, i);
+      aRef(1, 3) = neighbors(j, i);
+      aRef(2, 3) = 1;
+
+      // The constraint b_ij is the distance between these two points.
+      mvuSolver.B()[index] = distances(j, i);
+    }
+  }
+
+  // Now on with the solving.
+  double objective = mvuSolver.Optimize(outputData);
+
+  Log::Info << "Final objective is " << objective << "." << std::endl;
+
+  // Revert to original data format.
+  outputData = trans(outputData);
 }

Modified: mlpack/trunk/src/mlpack/methods/mvu/mvu.hpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/mvu/mvu.hpp	2012-02-29 22:41:10 UTC (rev 11668)
+++ mlpack/trunk/src/mlpack/methods/mvu/mvu.hpp	2012-02-29 22:48:40 UTC (rev 11669)
@@ -8,7 +8,7 @@
  * Augmented Lagrangian optimizer (which in turn uses the L-BFGS optimizer).
  */
 #ifndef __MLPACK_METHODS_MVU_MVU_HPP
-#define __MLPACK_METHODS_VU_MVU_HPP
+#define __MLPACK_METHODS_MVU_MVU_HPP
 
 #include <mlpack/core.hpp>