[mlpack-svn] r11777 - mlpack/trunk/src/mlpack/methods/sparse_coding

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

Author: niche
Date: 2012-03-07 14:12:45 -0500 (Wed, 07 Mar 2012)
New Revision: 11777

Modified:
   mlpack/trunk/src/mlpack/methods/sparse_coding/sparse_coding.cpp
Log:
Switched to Cholesky-based LARS. this version of LARS now uses Gram matrix in some places. We use SetGramMem rather than SetGram to avoid copying Gram matrix unnecessarily. Also, removed some commented out that was quite old

Modified: mlpack/trunk/src/mlpack/methods/sparse_coding/sparse_coding.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/sparse_coding/sparse_coding.cpp	2012-03-07 18:10:47 UTC (rev 11776)
+++ mlpack/trunk/src/mlpack/methods/sparse_coding/sparse_coding.cpp	2012-03-07 19:12:45 UTC (rev 11777)
@@ -18,33 +18,7 @@
 namespace mlpack {
 namespace sparse_coding {
 
-/*
-void SparseCoding::Init(double* memX, u32 nDims, u32 nPoints,
-			u32 nAtoms, double lambda1) {
-  matX = mat(memX, nDims, nPoints, false, true);
 
-  this->nDims = nDims;
-  this->nPoints = nPoints;
-
-  this->nAtoms = nAtoms;
-  //matD = mat(nDims, nAtoms);
-  matZ = mat(nAtoms, nPoints);
-  
-  this->lambda1 = lambda1;
-  lambda2 = 0;
-}
-*/
-
-/*
-void SparseCoding::SetDictionary(double* memD) {
-  matD = mat(memD, nDims, nAtoms, false, true);
-}
-*/
-
-
-
-
-
 SparseCoding::SparseCoding(const mat& matX, u32 nAtoms, double lambda1, double lambda2) :
   nDims(matX.n_rows),  
   nAtoms(nAtoms),
@@ -149,13 +123,15 @@
 
 
 void SparseCoding::OptimizeCode() {
-  mat matGram;
-  if(lambda2 > 0) {
-    matGram = trans(matD) * matD + lambda2 * eye(nAtoms, nAtoms);
-  }
-  else {
-    matGram = trans(matD) * matD;
-  }
+  // when using Cholesky version of LARS, this is correct even if lambda2 > 0
+  mat matGram = trans(matD) * matD;
+  // mat matGram;
+  // if(lambda2 > 0) {
+  //   matGram = trans(matD) * matD + lambda2 * eye(nAtoms, nAtoms);
+  // }
+  // else {
+  //   matGram = trans(matD) * matD;
+  // }
   
   for(u32 i = 0; i < nPoints; i++) {
     // report progress
@@ -163,18 +139,7 @@
       Log::Debug << "\t" << i << endl;
     }
     
-    //Lars lars;
-    // do we still need 0.5 * lambda? no, because we're using the standard objective now, which includes 0.5 scaling for quadratic terms
-    //lars.Init(D.memptr(), matX.colptr(i), nDims, nAtoms, true, lambda1); // apparently not as fast as using the below duo
-                                                                                       // this may change, depending on the dimensionality and sparsity
-
-    // the duo
-    //lars.Init(matD.memptr(), matX.colptr(i), nDims, nAtoms, false, lambda1);
-    //lars.SetGram(matGram.memptr(), nAtoms);
-    //lars.DoLARS();
- 
-
-    bool useCholesky = false;
+    bool useCholesky = true;
     LARS* lars;
     if(lambda2 > 0) {
       lars = new LARS(useCholesky, lambda1, lambda2);
@@ -182,7 +147,7 @@
     else {
       lars = new LARS(useCholesky, lambda1);
     }
-    lars -> SetGram(matGram);
+    lars -> SetGramMem(matGram.memptr(), matGram.n_rows);
     lars -> DoLARS(matD, matX.unsafe_col(i));
     
     vec beta;