source: trunk/packages/vizservers/nanovis/PCASplit.cpp @ 3362

/* -*- mode: c++; c-basic-offset: 4; indent-tabs-mode: nil -*- */
#include <stdio.h>

#define WANT_STREAM                  // include.h will get stream fns
#define WANT_MATH                    // include.h will get math fns
                                     // newmatap.h will get include.h
#include <newmatap.h>                // need matrix applications
#include <newmatio.h>                // need matrix output routines
#include <newmatrc.h>

#include "PCASplit.h"

#ifdef use_namespace
using namespace NEWMAT;              // access NEWMAT namespace
#endif

using namespace PCA;

PCASplit::PCASplit() : 
    _maxLevel(4),
    _minDistance(0.5f),
    _distanceScale(0.2f),
    _indexCount(0),
    _finalMaxLevel(0)
{
    _indices = new unsigned int[MAX_INDEX];
    _memClusterChunk1 = new ClusterListNode[MAX_INDEX];
    _memClusterChunk2 = new ClusterListNode[MAX_INDEX];
    _curMemClusterChunk = _memClusterChunk1;
    _memClusterChunkIndex = 0;
}

PCASplit::~PCASplit()
{
    delete [] _indices;
    delete [] _memClusterChunk1;
    delete [] _memClusterChunk2;
}

void 
PCASplit::computeCentroid(Point *data, int count, Vector3& mean)
{
    float sumx = 0, sumy = 0, sumz = 0;
    float size = 0;
    float sumsize = 0;
    for (int i = 0; i < count; ++i) {
        size = data[i].size;
        sumx += data[i].position.x * size;
        sumy += data[i].position.y * size;
        sumz += data[i].position.z * size;
        sumsize += size;
    }
    sumsize = 1.0f / sumsize;
    mean.set(sumx * sumsize, sumy * sumsize, sumz * sumsize);
}

void 
PCASplit::computeCovariant(Point *data, int count, const Vector3& mean, 
                           float *m)
{
    memset(m, 0, sizeof(float) * 9);

    for (int i = 0; i < count; ++i) {
        m[0] += (data[i].position.x - mean.x) * (data[i].position.x - mean.x);
        m[1] += (data[i].position.x - mean.x) * (data[i].position.y - mean.y);
        m[2] += (data[i].position.x - mean.x) * (data[i].position.z - mean.z);
        m[4] += (data[i].position.y - mean.y) * (data[i].position.y - mean.y);
        m[5] += (data[i].position.y - mean.y) * (data[i].position.z - mean.z);
        m[8] += (data[i].position.z - mean.z) * (data[i].position.z - mean.z);
    }
    
    float invCount = 1.0f / (count - 1);
    m[0] *= invCount;
    m[1] *= invCount;
    m[2] *= invCount;
    m[4] *= invCount;
    m[5] *= invCount;
    m[8] *= invCount;
    m[3] = m[1];
    m[6] = m[2];
    m[7] = m[5];
}

void 
PCASplit::computeDistortion(Point *data, int count, const Vector3& mean, 
                            float& distortion, float& finalSize)
{
    distortion = 0.0f;
    finalSize = 0.0f;
        
    float maxSize = 0.0f;
    float distance;
    for (int i = 0; i < count; ++i) {
        // sum 
        distance = mean.distanceSquare(data[i].position.x, data[i].position.y, data[i].position.z);
        distortion += distance;

        if (data[i].size > maxSize) {
            maxSize = data[i].size;
        }

        /*
          finalSize += data[i].size * sqrt(distance);
        */
        if (distance > finalSize) {
            finalSize = distance;
        }
    }
    // equation 2
    //finalSize = 0.5f * sqrt (finalSize) / (float) (count - 1) + maxSize;
    finalSize = sqrt (finalSize) + maxSize;
}

void
PCASplit::init()
{
    _curClusterNode = NULL;
    _curClusterCount = 0;
}

Cluster *
PCASplit::createClusterBlock(ClusterListNode *clusterList, int count, int level)
{
    static int cc = 0;
    cc += count;
    Cluster *clusterBlock = new Cluster[count];

    _clusterHeader->numOfClusters[level - 1] = count;
    _clusterHeader->startPointerCluster[level - 1] = clusterBlock;

    TRACE("Cluster created %d [in level %d]:total %d\n", count, level, cc);

    int i = 0;
    ClusterListNode *clusterNode = clusterList;
    while (clusterNode) {
        clusterBlock[i].centroid = clusterList->data->centroid_t;
        clusterBlock[i].level = level;
        clusterBlock[i].scale = clusterList->data->scale_t;

        clusterNode = clusterNode->next;
        ++i;
    }
    if (count != i) {
        ERROR("Problem walking clusterList: count: %d, i: %d\n", count, i);
    }
    return clusterBlock;
}

ClusterAccel *
PCASplit::doIt(Point *data, int count)
{
    init();

    _clusterHeader = new ClusterAccel(_maxLevel);

    Cluster *root = new Cluster;
    Cluster_t *cluster_t = new Cluster_t();
    cluster_t->points_t = data;
    cluster_t->numOfPoints_t = count;
    root->level = 1;

    _clusterHeader->root = root;
    _clusterHeader->numOfClusters[0] = 1;
    _clusterHeader->startPointerCluster[0] = root;

    Vector3 mean;
    float distortion, scale;

    computeCentroid(cluster_t->points_t, cluster_t->numOfPoints_t, mean);
    computeDistortion(cluster_t->points_t, cluster_t->numOfPoints_t, mean, 
                      distortion, scale);

    cluster_t->centroid_t = mean;
    cluster_t->scale_t = scale;

    float mindistance = _minDistance;
    int level = 2;

    ClusterListNode *clustNodeList = &(_memClusterChunk2[0]);
    clustNodeList->data = cluster_t;
    clustNodeList->next = NULL;

    _curRoot = root;
    do {
        analyze(clustNodeList, _curRoot, level, mindistance);

        mindistance *= _distanceScale;
        ++level;

        // swap memory buffer & init
        _curMemClusterChunk = (_curMemClusterChunk == _memClusterChunk1) ?
            _memClusterChunk2 : _memClusterChunk1;
        _memClusterChunkIndex = 0;

        clustNodeList = _curClusterNode;
    } while (level <= _maxLevel);

    return _clusterHeader;
}

void 
PCASplit::addLeafCluster(Cluster_t *cluster)
{
    ClusterListNode *clusterNode = 
        &_curMemClusterChunk[_memClusterChunkIndex++];
    clusterNode->data = cluster;
    clusterNode->next = _curClusterNode;
    _curClusterNode = clusterNode;
    ++_curClusterCount;
}

void 
PCASplit::split(Point *data, int count, float limit)
{
    Vector3 mean;
    float m[9];

    computeCentroid(data, count, mean);
    float scale, distortion;
    computeDistortion(data, count, mean, distortion, scale);

    //if (distortion < limit)
    if (scale < limit) {
        Cluster_t *cluster_t = new Cluster_t();
        cluster_t->centroid_t = mean;
        cluster_t->points_t = data;
        cluster_t->numOfPoints_t = count;
        cluster_t->scale_t = scale;
        addLeafCluster(cluster_t);
        return;
    }

    computeCovariant(data, count, mean, m);

    // Begin newmat11 dependency

    SymmetricMatrix A(3);
    for (int i = 1; i <= 3; ++i) {
        for (int j = 1; j <= i; ++j) {
            A(i, j) = m[(i - 1) * 3 + j - 1];
        }
    }

    Matrix U;
    DiagonalMatrix D;
    eigenvalues(A, D ,U);
    Vector3 emax(U(1, 3), U(2, 3), U(3, 3));

    // End newmat11 dependency

    int left = 0, right = count - 1;

    Point p;
    for (;left < right;) {
        while (left < count && emax.dot(data[left].position - mean) >= 0.0f) {
            ++left;
        }
        while (right >= 0 && emax.dot(data[right].position - mean) < 0.0f) {
            --right;
        }
        if (left > right) {
            break;
        }

        p = data[left];
        data[left] = data[right];
        data[right] = p;
        ++left, --right;
    }

    if (left == 0 || right == count - 1) {
        TRACE("error\n");
        exit(1);
    } else {
        split(data, left, limit);
        split(data + left, count - left, limit);
    }
}

void 
PCASplit::analyze(ClusterListNode *clusterNode, Cluster *parent, int level, 
                  float limit)
{
    if (level > _maxLevel) {
        return;
    } 
    if (level > _finalMaxLevel) {
        _finalMaxLevel = level;
    }

    init();

    ClusterListNode *clNode = clusterNode;

    // initialize the indexCount of indices
    _indexCount = 0;
    while (clNode) {
        if (clNode->data) {
            split(clNode->data->points_t, clNode->data->numOfPoints_t, limit);
            _indices[_indexCount++] = _curClusterCount;
        }
        clNode = clNode->next;
    }

    //Vector3 mean;
    //computeCentroid(cluster->points, cluster->numOfPoints, mean);

    // the process values of split are in _curClusterNode and _curClusterCount
    ClusterListNode *curClusterNode = _curClusterNode;
    unsigned int curClusterNodeCount = _curClusterCount;

    if (curClusterNodeCount) {
        // create and init centroid
        Cluster *retClusterBlock = 
            createClusterBlock(curClusterNode, curClusterNodeCount, level);

        _curRoot = retClusterBlock;

        if (level == _maxLevel) {
            ClusterListNode *node = curClusterNode;
            if (_indexCount > 0) {
                // for parent
                Point *points = new Point[curClusterNodeCount];

                parent[0].setChildren(retClusterBlock, _indices[0]);
                parent[0].setPoints(points, _indices[0]);

                for (unsigned int i = 0, in = 0; i < curClusterNodeCount; ++i) {
                    if (i >= _indices[in]) {
                        in++;
                        parent[in].setChildren(retClusterBlock + _indices[in - 1], _indices[in] - _indices[in - 1]);
                        parent[in].setPoints(points + _indices[in - 1], _indices[in] - _indices[in - 1]);
                    }

                    retClusterBlock[i].scale = node->data->scale_t;
                    retClusterBlock[i].centroid = node->data->centroid_t;
                    retClusterBlock[i].points = node->data->points_t;
                    retClusterBlock[i].numOfPoints = node->data->numOfPoints_t;
                    retClusterBlock[i].color.set(float(rand()) / RAND_MAX, 
                                                 float(rand()) / RAND_MAX, 
                                                 float(rand()) / RAND_MAX,
                                                 0.2);

                    points[i].position = node->data->centroid_t;
                    points[i].color.set(1.0f, 1.0f, 1.0f, 0.2f);
                    points[i].size = node->data->scale_t;

                    node = node->next;
                }
            }
        } else {
            Point *points = new Point[curClusterNodeCount];
            ClusterListNode *node = curClusterNode;

            if (_indexCount > 0) {
                parent[0].setPoints(points, _indices[0]);
                parent[0].setChildren(retClusterBlock, _indices[0]);
                for (int k = 1; k < _indexCount; ++k) {
                    parent[k].setPoints(points + _indices[k - 1], 
                                        _indices[k] - _indices[k - 1]);
                    parent[k].setChildren(retClusterBlock + _indices[k - 1], 
                                          _indices[k] - _indices[k - 1]);
                }

                // set points of sub-clusters
                for (unsigned int i = 0; i < curClusterNodeCount; ++i) {
                    points[i].position = node->data->centroid_t;
                    points[i].color.set(1.0f, 1.0f, 1.0f, 0.2f);
                    points[i].size = node->data->scale_t;
                    node = node->next;
                }
            }
        }
    }
}