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diff --git a/binaries/src/clustalo/src/kmpp/KmTree.h b/binaries/src/clustalo/src/kmpp/KmTree.h
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+// BEWARE: BETA VERSION
+// --------------------
+//
+// A k-d tree that vastly speeds up an iteration of k-means (in any number of dimensions). The main
+// idea for this data structure is from Kanungo/Mount. This is used internally by Kmeans.cpp, and
+// will most likely not need to be used directly.
+//
+// The stucture works as follows:
+//   - All data points are placed into a tree where we choose child nodes by partitioning all data
+//     points along a plane parallel to the axis.
+//   - We maintain for each node, the bounding box of all data points stored at that node.
+//   - To do a k-means iteration, we need to assign points to clusters and calculate the sum and
+//     the number of points assigned to each cluster. For each node in the tree, we can rule out
+//     some cluster centers as being too far away from every single point in that bounding box.
+//     Once only one cluster is left, all points in the node can be assigned to that cluster in
+//     batch.
+//
+// Author: David Arthur (darthur@gmail.com), 2009
+
+#ifndef KM_TREE_H__
+#define KM_TREE_H__
+
+// Includes
+#include "KmUtils.h"
+
+// KmTree class definition
+class KmTree {
+ public:
+  // Constructs a tree out of the given n data points living in R^d.
+  KmTree(int n, int d, Scalar *points);
+  ~KmTree();
+
+  // Given k cluster centers, this runs a full k-means iterations, choosing the next set of
+  // centers and returning the cost function for this set of centers. If assignment is not null,
+  // it should be an array of size n that will be filled with the index of the cluster (0 - k-1)
+  // that each data point is assigned to. The new center values will overwrite the old ones.
+  Scalar DoKMeansStep(int k, Scalar *centers, int *assignment) const;
+
+  // Choose k initial centers for k-means using the kmeans++ seeding procedure. The resulting
+  // centers are returned via the centers variable, which should be pre-allocated to size k*d.
+  // The cost of the initial clustering is returned.
+  Scalar SeedKMeansPlusPlus(int k, Scalar *centers) const;
+
+ private:
+  struct Node {
+    int num_points;                 // Number of points stored in this node
+    int first_point_index;          // The smallest point index stored in this node
+    Scalar *median, *radius;        // Bounding box center and half side-lengths
+    Scalar *sum;                    // Sum of the points stored in this node
+    Scalar opt_cost;                // Min cost for putting all points in this node in 1 cluster
+    Node *lower_node, *upper_node;  // Child nodes
+    mutable int kmpp_cluster_index; // The cluster these points are assigned to or -1 if variable
+  };
+
+  // Helper functions for constructor
+  Node *BuildNodes(Scalar *points, int first_index, int last_index, char **next_node_data);
+  Scalar GetNodeCost(const Node *node, Scalar *center) const;
+
+  // Helper functions for DoKMeans step
+  Scalar DoKMeansStepAtNode(const Node *node, int k, int *candidates, Scalar *centers,
+                            Scalar *sums, int *counts, int *assignment) const;
+  bool ShouldBePruned(Scalar *box_median, Scalar *box_radius, Scalar *centers, int best_index,
+                      int test_index) const;
+
+  // Helper functions for SeedKMeansPlusPlus
+  void SeedKmppSetClusterIndex(const Node *node, int index) const;
+  Scalar SeedKmppUpdateAssignment(const Node *node, int new_cluster, Scalar *centers,
+                                  Scalar *dist_sq) const;
+
+  int n_, d_;
+  Scalar *points_;
+  Node *top_node_;
+  char *node_data_;
+  int *point_indices_;
+};
+
+#endif