clustalomega: clustalomega/clustal-omega-0.2.0/src/kmpp/KmTree.cpp comparison

comparison clustalomega/clustal-omega-0.2.0/src/kmpp/KmTree.cpp @ 0:ff1768533a07

Migrated tool version 0.2 from old tool shed archive to new tool shed repository

author	clustalomega
date	Tue, 07 Jun 2011 17:04:25 -0400
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--1:000000000000
+:ff1768533a07
+// See KmTree.cpp
+//
+// Author: David Arthur (darthur@gmail.com), 2009
+// Includes
+#include "KmTree.h"
+#include <iostream>
+#include <stdlib.h>
+#include <stdio.h>
+using namespace std;
+KmTree::KmTree(int n, int d, Scalar *points): n_(n), d_(d), points_(points) {
+// Initialize memory
+// DD: need to cast to long otherwise malloc will fail
+// if we need more than 2 gigabytes or so
+int node_size = sizeof(Node) + d_ * 3 * sizeof(Scalar);
+node_data_ = (char*)malloc((2*(long unsigned int)n-1) * node_size);
+point_indices_ = (int*)malloc(n * sizeof(int));
+for (int i = 0; i < n; i++)
+point_indices_[i] = i;
+KM_ASSERT(node_data_ != 0 && point_indices_ != 0);
+// Calculate the bounding box for the points
+Scalar *bound_v1 = PointAllocate(d_);
+Scalar *bound_v2 = PointAllocate(d_);
+KM_ASSERT(bound_v1 != 0 && bound_v2 != 0);
+PointCopy(bound_v1, points, d_);
+PointCopy(bound_v2, points, d_);
+for (int i = 1; i < n; i++)
+for (int j = 0; j < d; j++) {
+if (bound_v1[j] > points[i*d_ + j]) bound_v1[j] = points[i*d_ + j];
+if (bound_v2[j] < points[i*d_ + j]) bound_v1[j] = points[i*d_ + j];
+}
+// Build the tree
+char *temp_node_data = node_data_;
+top_node_ = BuildNodes(points, 0, n-1, &temp_node_data);
+// Cleanup
+PointFree(bound_v1);
+PointFree(bound_v2);
+}
+KmTree::~KmTree() {
+free(point_indices_);
+free(node_data_);
+}
+Scalar KmTree::DoKMeansStep(int k, Scalar *centers, int *assignment) const {
+// Create an invalid center for comparison purposes
+Scalar *bad_center = PointAllocate(d_);
+KM_ASSERT(bad_center != 0);
+memset(bad_center, 0xff, d_ * sizeof(Scalar));
+// Allocate data
+Scalar *sums = (Scalar*)calloc(k * d_, sizeof(Scalar));
+int *counts = (int*)calloc(k, sizeof(int));
+int num_candidates = 0;
+int *candidates = (int*)malloc(k * sizeof(int));
+KM_ASSERT(sums != 0 && counts != 0 && candidates != 0);
+for (int i = 0; i < k; i++)
+if (memcmp(centers + i*d_, bad_center, d_ * sizeof(Scalar)) != 0)
+candidates[num_candidates++] = i;
+// Find nodes
+Scalar result = DoKMeansStepAtNode(top_node_, num_candidates, candidates, centers, sums,
+counts, assignment);
+// Set the new centers
+for (int i = 0; i < k; i++) {
+if (counts[i] > 0) {
+PointScale(sums + i*d_, Scalar(1) / counts[i], d_);
+PointCopy(centers + i*d_, sums + i*d_, d_);
+} else {
+memcpy(centers + i*d_, bad_center, d_ * sizeof(Scalar));
+}
+}
+// Cleanup memory
+PointFree(bad_center);
+free(candidates);
+free(counts);
+free(sums);
+return result;
+}
+// Helper functions for constructor
+// ================================
+// Build a kd tree from the given set of points
+KmTree::Node *KmTree::BuildNodes(Scalar *points, int first_index, int last_index,
+char **next_node_data) {
+// Allocate the node
+Node *node = (Node*)(*next_node_data);
+(*next_node_data) += sizeof(Node);
+node->sum = (Scalar*)(*next_node_data);
+(*next_node_data) += sizeof(Scalar) * d_;
+node->median = (Scalar*)(*next_node_data);
+(*next_node_data) += sizeof(Scalar) * d_;
+node->radius = (Scalar*)(*next_node_data);
+(*next_node_data) += sizeof(Scalar) * d_;
+// Fill in basic info
+node->num_points = (last_index - first_index + 1);
+node->first_point_index = first_index;
+// Calculate the bounding box
+Scalar *first_point = points + point_indices_[first_index] * d_;
+Scalar *bound_p1 = PointAllocate(d_);
+Scalar *bound_p2 = PointAllocate(d_);
+KM_ASSERT(bound_p1 != 0 && bound_p2 != 0);
+PointCopy(bound_p1, first_point, d_);
+PointCopy(bound_p2, first_point, d_);
+for (int i = first_index+1; i <= last_index; i++)
+for (int j = 0; j < d_; j++) {
+Scalar c = points[point_indices_[i]*d_ + j];
+if (bound_p1[j] > c) bound_p1[j] = c;
+if (bound_p2[j] < c) bound_p2[j] = c;
+}
+// Calculate bounding box stats and delete the bounding box memory
+Scalar max_radius = -1;
+int split_d = -1;
+for (int j = 0; j < d_; j++) {
+node->median[j] = (bound_p1[j] + bound_p2[j]) / 2;
+node->radius[j] = (bound_p2[j] - bound_p1[j]) / 2;
+if (node->radius[j] > max_radius) {
+max_radius = node->radius[j];
+split_d = j;
+}
+}
+PointFree(bound_p2);
+PointFree(bound_p1);
+// If the max spread is 0, make this a leaf node
+if (max_radius == 0) {
+node->lower_node = node->upper_node = 0;
+PointCopy(node->sum, first_point, d_);
+if (last_index != first_index)
+PointScale(node->sum, Scalar(last_index - first_index + 1), d_);
+node->opt_cost = 0;
+return node;
+}
+// Partition the points around the midpoint in this dimension. The partitioning is done in-place
+// by iterating from left-to-right and right-to-left in the same way that partioning is done for
+// quicksort.
+Scalar split_pos = node->median[split_d];
+int i1 = first_index, i2 = last_index, size1 = 0;
+while (i1 <= i2) {
+bool is_i1_good = (points[point_indices_[i1]*d_ + split_d] < split_pos);
+bool is_i2_good = (points[point_indices_[i2]*d_ + split_d] >= split_pos);
+if (!is_i1_good && !is_i2_good) {
+int temp = point_indices_[i1];
+point_indices_[i1] = point_indices_[i2];
+point_indices_[i2] = temp;
+is_i1_good = is_i2_good = true;
+}
+if (is_i1_good) {
+i1++;
+size1++;
+}
+if (is_i2_good) {
+i2--;
+}
+}
+// Create the child nodes
+KM_ASSERT(size1 >= 1 && size1 <= last_index - first_index);
+node->lower_node = BuildNodes(points, first_index, first_index + size1 - 1, next_node_data);
+node->upper_node = BuildNodes(points, first_index + size1, last_index, next_node_data);
+// Calculate the new sum and opt cost
+PointCopy(node->sum, node->lower_node->sum, d_);
+PointAdd(node->sum, node->upper_node->sum, d_);
+Scalar *center = PointAllocate(d_);
+KM_ASSERT(center != 0);
+PointCopy(center, node->sum, d_);
+PointScale(center, Scalar(1) / node->num_points, d_);
+node->opt_cost = GetNodeCost(node->lower_node, center) + GetNodeCost(node->upper_node, center);
+PointFree(center);
+return node;
+}
+// Returns the total contribution of all points in the given kd-tree node, assuming they are all
+// assigned to a center at the given location. We need to return:
+//
+//   sum_{x \in node} ||x - center||^2.
+//
+// If c denotes the center of mass of the points in this node and n denotes the number of points in
+// it, then this quantity is given by
+//
+//   n * ||c - center||^2 + sum_{x \in node} ||x - c||^2
+//
+// The sum is precomputed for each node as opt_cost. This formula follows from expanding both sides
+// as dot products. See Kanungo/Mount for more info.
+Scalar KmTree::GetNodeCost(const Node *node, Scalar *center) const {
+Scalar dist_sq = 0;
+for (int i = 0; i < d_; i++) {
+Scalar x = (node->sum[i] / node->num_points) - center[i];
+dist_sq += x*x;
+}
+return node->opt_cost + node->num_points * dist_sq;
+}
+// Helper functions for DoKMeans step
+// ==================================
+// A recursive version of DoKMeansStep. This determines which clusters all points that are rooted
+// node will be assigned to, and updates sums, counts and assignment (if not null) accordingly.
+// candidates maintains the set of cluster indices which could possibly be the closest clusters
+// for points in this subtree.
+Scalar KmTree::DoKMeansStepAtNode(const Node *node, int k, int *candidates, Scalar *centers,
+Scalar *sums, int *counts, int *assignment) const {
+// Determine which center the node center is closest to
+Scalar min_dist_sq = PointDistSq(node->median, centers + candidates[0]*d_, d_);
+int closest_i = candidates[0];
+for (int i = 1; i < k; i++) {
+Scalar dist_sq = PointDistSq(node->median, centers + candidates[i]*d_, d_);
+if (dist_sq < min_dist_sq) {
+min_dist_sq = dist_sq;
+closest_i = candidates[i];
+}
+}
+// If this is a non-leaf node, recurse if necessary
+if (node->lower_node != 0) {
+// Build the new list of candidates
+int new_k = 0;
+int *new_candidates = (int*)malloc(k * sizeof(int));
+KM_ASSERT(new_candidates != 0);
+for (int i = 0; i < k; i++)
+if (!ShouldBePruned(node->median, node->radius, centers, closest_i, candidates[i]))
+new_candidates[new_k++] = candidates[i];
+// Recurse if there's at least two
+if (new_k > 1) {
+Scalar result = DoKMeansStepAtNode(node->lower_node, new_k, new_candidates, centers,
+sums, counts, assignment) +
+DoKMeansStepAtNode(node->upper_node, new_k, new_candidates, centers,
+sums, counts, assignment);
+free(new_candidates);
+return result;
+} else {
+free(new_candidates);
+}
+}
+// Assigns all points within this node to a single center
+PointAdd(sums + closest_i*d_, node->sum, d_);
+counts[closest_i] += node->num_points;
+if (assignment != 0) {
+for (int i = node->first_point_index; i < node->first_point_index + node->num_points; i++)
+assignment[point_indices_[i]] = closest_i;
+}
+return GetNodeCost(node, centers + closest_i*d_);
+}
+// Determines whether every point in the box is closer to centers[best_index] than to
+// centers[test_index].
+//
+// If x is a point, c_0 = centers[best_index], c = centers[test_index], then:
+//       (x-c).(x-c) < (x-c_0).(x-c_0)
+//   <=> (c-c_0).(c-c_0) < 2(x-c_0).(c-c_0)
+//
+// The right-hand side is maximized for a vertex of the box where for each dimension, we choose
+// the low or high value based on the sign of x-c_0 in that dimension.
+bool KmTree::ShouldBePruned(Scalar *box_median, Scalar *box_radius, Scalar *centers,
+int best_index, int test_index) const {
+if (best_index == test_index)
+return false;
+Scalar *best = centers + best_index*d_;
+Scalar *test = centers + test_index*d_;
+Scalar lhs = 0, rhs = 0;
+for (int i = 0; i < d_; i++) {
+Scalar component = test[i] - best[i];
+lhs += component * component;
+if (component > 0)
+rhs += (box_median[i] + box_radius[i] - best[i]) * component;
+else
+rhs += (box_median[i] - box_radius[i] - best[i]) * component;
+}
+return (lhs >= 2*rhs);
+}
+Scalar KmTree::SeedKMeansPlusPlus(int k, Scalar *centers) const {
+Scalar *dist_sq = (Scalar*)malloc(n_ * sizeof(Scalar));
+KM_ASSERT(dist_sq != 0);
+// Choose an initial center uniformly at random
+SeedKmppSetClusterIndex(top_node_, 0);
+int i = GetRandom(n_);
+memcpy(centers, points_ + point_indices_[i]*d_, d_*sizeof(Scalar));
+Scalar total_cost = 0;
+for (int j = 0; j < n_; j++) {
+dist_sq[j] = PointDistSq(points_ + point_indices_[j]*d_, centers, d_);
+total_cost += dist_sq[j];
+}
+// Repeatedly choose more centers
+for (int new_cluster = 1; new_cluster < k; new_cluster++) {
+while (1) {
+Scalar cutoff = (rand() / Scalar(RAND_MAX)) * total_cost;
+Scalar cur_cost = 0;
+for (i = 0; i < n_; i++) {
+cur_cost += dist_sq[i];
+if (cur_cost >= cutoff)
+break;
+}
+if (i < n_)
+break;
+}
+memcpy(centers + new_cluster*d_, points_ + point_indices_[i]*d_, d_*sizeof(Scalar));
+total_cost = SeedKmppUpdateAssignment(top_node_, new_cluster, centers, dist_sq);
+}
+// Clean up and return
+free(dist_sq);
+return total_cost;
+}
+// Helper functions for SeedKMeansPlusPlus
+// =======================================
+// Sets kmpp_cluster_index to 0 for all nodes
+void KmTree::SeedKmppSetClusterIndex(const Node *node, int value) const {
+node->kmpp_cluster_index = value;
+if (node->lower_node != 0) {
+SeedKmppSetClusterIndex(node->lower_node, value);
+SeedKmppSetClusterIndex(node->upper_node, value);
+}
+}
+Scalar KmTree::SeedKmppUpdateAssignment(const Node *node, int new_cluster, Scalar *centers,
+Scalar *dist_sq) const {
+// See if we can assign all points in this node to one cluster
+if (node->kmpp_cluster_index >= 0) {
+if (ShouldBePruned(node->median, node->radius, centers, node->kmpp_cluster_index, new_cluster))
+return GetNodeCost(node, centers + node->kmpp_cluster_index*d_);
+if (ShouldBePruned(node->median, node->radius, centers, new_cluster,
+node->kmpp_cluster_index)) {
+SeedKmppSetClusterIndex(node, new_cluster);
+for (int i = node->first_point_index; i < node->first_point_index + node->num_points; i++)
+dist_sq[i] = PointDistSq(points_ + point_indices_[i]*d_, centers + new_cluster*d_, d_);
+return GetNodeCost(node, centers + new_cluster*d_);
+}
+// It may be that the a leaf-node point is equidistant from the new center or old
+if (node->lower_node == 0)
+return GetNodeCost(node, centers + node->kmpp_cluster_index*d_);
+}
+// Recurse
+Scalar cost = SeedKmppUpdateAssignment(node->lower_node, new_cluster, centers, dist_sq) +
+SeedKmppUpdateAssignment(node->upper_node, new_cluster, centers, dist_sq);
+int i1 = node->lower_node->kmpp_cluster_index, i2 = node->upper_node->kmpp_cluster_index;
+if (i1 == i2 && i1 != -1)
+node->kmpp_cluster_index = i1;
+else
+node->kmpp_cluster_index = -1;
+return cost;
+}

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comparison clustalomega/clustal-omega-0.2.0/src/kmpp/KmTree.cpp @ 0:ff1768533a07