Beginning to refactor dual-tree k-means. So far the Pelleg-Moore rule is properly refactored (well, sort of, at least). Try to move IterationUpdate() into DualTreeKMeans::TreeUpdate(); for now this will make the algorithm simpler: instead of updating on the fly, update after an iteration. It may make more sense to do updates on demand later, but let's make the algorithm work first...

src/mlpack/methods/kmeans/dual_tree_kmeans.hpp

@@ -57,6 +57,10 @@ class DualTreeKMeans
 
   //! Track distance calculations.
   size_t distanceCalculations;
+
+  void TreeUpdate(TreeType* node,
+                  const size_t clusters,
+                  const arma::vec& clusterDistances);
 };
 
 template<typename MetricType, typename MatType>

src/mlpack/methods/kmeans/dual_tree_kmeans_impl.hpp

@@ -106,6 +106,9 @@ double DualTreeKMeans<MetricType, MatType, TreeType>::Iterate(
   }
   Log::Info << clusterDistances.t();
 
+  // Update the tree with the centroid movement information.
+  TreeUpdate(tree, centroids.n_cols, clusterDistances);
+
   delete centroidTree;
 
   ++iteration;
@@ -148,43 +151,72 @@ void DualTreeKMeans<MetricType, MatType, TreeType>::ClusterTreeUpdate(
   node->Stat().Owner() = maxChangeCluster;
   node->Stat().MinQueryNodeDistance() = maxChange;
 }
+*/
 
 template<typename MetricType, typename MatType, typename TreeType>
 void DualTreeKMeans<MetricType, MatType, TreeType>::TreeUpdate(
-    TreeType* node)
+    TreeType* node,
+    const size_t clusters,
+    const arma::vec& clusterDistances)
 {
   // This is basically IterationUpdate(), but pulled out to be separate from the
   // actual dual-tree algorithm.
 
-  // First, update the iteration.
-  const size_t itDiff = node->Stat().Iteration() - iteration;
+  if (node->Parent() != NULL && node->Parent()->Stat().Owner() < clusters)
+    node->Stat().Owner() = node->Parent()->Stat().Owner();
 
-  if (itDiff == 1)
+  // The easy case: this node had an owner.
+  if (node->Stat().Owner() < clusters)
   {
-    // The easy case.
-    if (node->Stat().Owner() < centroids.n_cols)
-    {
-      // During the last iteration, this node was pruned.  In addition, we have
-      // cached a lower bound on the second closest cluster.  So, use the
-      // triangle inequality: if the maximum distance between the point and the
-      // cluster centroid plus the distance that centroid moved is less than the
-      // lower bound minus the maximum moving centroid, then this cluster *must*
-      // still have the same owner.
-      const size_t owner = node->Stat().Owner();
-      const double closestUpperBound = node->Stat().MaxQueryNodeDistance() +
-          clusterDistances[owner];
-      const TreeType* nonOwner = (TreeType*) node->Stat().ClosestNonOwner();
-      const double tightestLowerBound = node->Stat().ClosestNonOwnerDistance() -
-          nonOwner->Stat().MinQueryNodeDistance() /* abused from earlier *;
-      if (closestUpperBound <= tightestLowerBound)
-      {
-        // Then the owner must not have changed.
+    // During the last iteration, this node was pruned.
+    const size_t owner = node->Stat().Owner();
+    if (node->Stat().MaxQueryNodeDistance() != DBL_MAX)
+      node->Stat().MaxQueryNodeDistance() += clusterDistances[owner];
+    if (node->Stat().MinQueryNodeDistance() != DBL_MAX)
+      node->Stat().MinQueryNodeDistance() += clusterDistances[owner];
 
-      }
+/*
+    // During the last iteration, this node was pruned.  In addition, we have
+    // cached a lower bound on the second closest cluster.  So, use the
+    // triangle inequality: if the maximum distance between the point and the
+    // cluster centroid plus the distance that centroid moved is less than the
+    // lower bound minus the maximum moving centroid, then this cluster *must*
+    // still have the same owner.
+    const size_t owner = node->Stat().Owner();
+    const double closestUpperBound = node->Stat().MaxQueryNodeDistance() +
+        clusterDistances[owner];
+    const TreeType* nonOwner = (TreeType*) node->Stat().ClosestNonOwner();
+    const double tightestLowerBound = node->Stat().ClosestNonOwnerDistance() -
+        nonOwner->Stat().MinQueryNodeDistance();
+    if (closestUpperBound <= tightestLowerBound)
+    {
+      // Then the owner must not have changed.
     }
-  }
-}
 */
+  }
+  else
+  {
+    // This node did not have a single owner, but did have a closest query
+    // node.  So we will simply loosen that bound.  The loosening here is too
+    // loose; TODO: tighten to the max cluster movement in the closest query
+    // node.
+    if (node->Stat().MaxQueryNodeDistance() != DBL_MAX)
+      node->Stat().MaxQueryNodeDistance() += clusterDistances[clusters];
+    if (node->Stat().MinQueryNodeDistance() != DBL_MAX)
+      node->Stat().MinQueryNodeDistance() += clusterDistances[clusters];
+  }
+
+  node->Stat().Iteration() = iteration;
+  node->Stat().ClustersPruned() = (node->Parent() == NULL) ? 0 : -1;
+  // We have to set the closest query node to NULL because the cluster tree will
+  // be rebuilt.
+  node->Stat().ClosestQueryNode() = NULL;
+//  node->Stat().MaxQueryNodeDistance() = DBL_MAX;
+//  node->Stat().MinQueryNodeDistance() = DBL_MAX;
+
+  for (size_t i = 0; i < node->NumChildren(); ++i)
+    TreeUpdate(&node->Child(i), clusters, clusterDistances);
+}
 
 
 } // namespace kmeans

src/mlpack/methods/kmeans/dual_tree_kmeans_rules_impl.hpp

@@ -53,8 +53,8 @@ inline force_inline double DualTreeKMeansRules<MetricType, TreeType>::BaseCase(
 
   // Collect the number of clusters that have been pruned during the traversal.
   // The ternary operator may not be necessary.
-  const size_t traversalPruned = (traversalInfo.LastReferenceNode() != NULL &&
-      traversalInfo.LastReferenceNode()->Stat().Iteration() == iteration) ?
+  const size_t traversalPruned = (traversalInfo.LastReferenceNode() != NULL) ?
+//      traversalInfo.LastReferenceNode()->Stat().Iteration() == iteration) ?
       traversalInfo.LastReferenceNode()->Stat().ClustersPruned() : 0;
 
   // It's possible that the reference node has been pruned before we got to the
@@ -101,7 +101,7 @@ double DualTreeKMeansRules<MetricType, TreeType>::Score(
     TreeType& referenceNode)
 {
   // Update from previous iteration, if necessary.
-  IterationUpdate(referenceNode);
+//  IterationUpdate(referenceNode);
 
   // No pruning here, for now.
   return 0.0;
@@ -112,26 +112,32 @@ double DualTreeKMeansRules<MetricType, TreeType>::Score(
     TreeType& queryNode,
     TreeType& referenceNode)
 {
-  if (IterationUpdate(referenceNode) == DBL_MAX)
-  {
+//  if (IterationUpdate(referenceNode) == DBL_MAX)
+//  {
     // The iteration update showed that the owner could not possibly change.
-    return DBL_MAX;
-  }
+//    return DBL_MAX;
+//  }
+
+  if (referenceNode.Stat().ClustersPruned() == size_t(-1))
+    referenceNode.Stat().ClustersPruned() =
+        referenceNode.Parent()->Stat().ClustersPruned();
 
   traversalInfo.LastReferenceNode() = &referenceNode;
 
   // Can we update the minimum query node distance for this reference node?
   const double minDistance = referenceNode.MinDistance(&queryNode);
-  ++distanceCalculations;
-  if (minDistance < referenceNode.Stat().MinQueryNodeDistance())
+  const double maxDistance = referenceNode.MaxDistance(&queryNode);
+  distanceCalculations += 2;
+  if (maxDistance < referenceNode.Stat().MaxQueryNodeDistance())
   {
     referenceNode.Stat().ClosestQueryNode() = (void*) &queryNode;
     referenceNode.Stat().MinQueryNodeDistance() = minDistance;
-    referenceNode.Stat().MaxQueryNodeDistance() =
+    referenceNode.Stat().MaxQueryNodeDistance() = maxDistance;
         referenceNode.MaxDistance(&queryNode);
-    ++distanceCalculations;
+//    ++distanceCalculations;
     return 0.0; // Pruning is not possible.
   }
+
   else if (IsDescendantOf(
       *((TreeType*) referenceNode.Stat().ClosestQueryNode()), queryNode))
   {
@@ -144,9 +150,10 @@ double DualTreeKMeansRules<MetricType, TreeType>::Score(
     return 0.0; // Pruning is not possible.
   }
 
-  double score = ElkanTypeScore(queryNode, referenceNode);
-  if (score != DBL_MAX)
-    score = PellegMooreScore(queryNode, referenceNode, minDistance);
+//  double score = ElkanTypeScore(queryNode, referenceNode);
+//  if (score != DBL_MAX)
+
+  double score = PellegMooreScore(queryNode, referenceNode, minDistance);
 
   if (score == DBL_MAX)
   {
@@ -179,6 +186,7 @@ double DualTreeKMeansRules<MetricType, TreeType>::Score(
   }
 
   return score;
+//  return 0.0;
 }
 
 template<typename MetricType, typename TreeType>
@@ -208,6 +216,7 @@ template<typename MetricType, typename TreeType>
 inline double DualTreeKMeansRules<MetricType, TreeType>::IterationUpdate(
     TreeType& referenceNode)
 {
+  Log::Fatal << "Update! Why!\n";
   if (referenceNode.Stat().Iteration() == iteration)
     return 0;