void AssimpScene::get_data(
float * arr_vertex, int arr_vertex_length,
float * arr_normal, int arr_normal_length,
int * arr_indices, int arr_indices_length,
int & store_num_arr, int & store_num_arr_indices)
{
store_num_arr = 0;
store_num_arr_indices = 0;
uncompress_recursive(this->scene->mRootNode,
arr_vertex, arr_vertex_length, store_num_arr,
arr_normal, arr_normal_length,
arr_indices, arr_indices_length, store_num_arr_indices);
update_center(arr_vertex, store_num_arr, sceneMin, sceneMax,sceneCenter);
rescale_verts(arr_vertex, store_num_arr);
/*
{
for (int i = 0; i < store_num_vertex / 3; i++) {
std::cout << "POS : " << arr_vertex[i * 3 + 0] << " " << arr_vertex[i * 3 + 1] <<" " << arr_vertex[i * 3 + 2] << std::endl;
std::cout << "NOR : " << arr_normal[i * 3 + 0] << " " << arr_normal[i * 3 + 1] << " " <<arr_normal[i * 3 + 2] << std::endl;
}
for (int i = 0; i < store_num_faces/3; i++) {
std::cout << "indices : " << arr_indices[i * 3 + 0] << " " << arr_indices[i * 3 + 1] << " "<< arr_indices[i * 3 + 2] << std::endl;
}
}
*/
}
void compute_cluster_centers(Dataset* dataset, Centroid** centroids){
// Datapoint* random_point = dataset->points[rand() % dataset->size];
for (size_t i = 0; i < dataset->size; i++) {
Centroid* closest_centroid = find_closest(centroids, dataset->points[i]);
update_center(dataset, closest_centroid);
}
scramble_dataset(dataset);
}
void transport(GLfloat x, GLfloat y, GLfloat z){
GLfloat M[4][4] = {0};
for(int i = 0; i < 4; ++i)
M[i][i] = 1;
M[0][3] = x;
M[1][3] = y;
M[2][3] = z;
GLfloat I[4][4] = {{1,0,0,0},{0,1,0,0},{0,0,1,0},{0,0,0,1}};
multiMatrix(geoMatrix[now], M, geoMatrix[now]);
multiple_all_matrix(M);
update_center(M);
}
double kmeans::update_centers(const cluster_sequence & clusters, dataset & centers) {
const dataset & data = *m_ptr_data;
const size_t dimension = data[0].size();
dataset calculated_clusters(clusters.size(), point(dimension, 0.0));
std::vector<double> changes(clusters.size(), 0.0);
parallel_for(std::size_t(0), clusters.size(), [this, &clusters, ¢ers, &calculated_clusters, &changes](const std::size_t p_index) {
calculated_clusters[p_index] = centers[p_index];
changes[p_index] = update_center(clusters[p_index], calculated_clusters[p_index]);
});
centers = std::move(calculated_clusters);
return *(std::max_element(changes.begin(), changes.end()));
}
void cluster()
{
int x; /* Goes through all the center points */
int i; /* Goes through all data points */
double distance; /* Gives the distance between the data points based on the metric */
double min_distance=INT_MAX; /* This will tell us which cluster to add it to */
for(i=0;i<n;i++)
{
min_distance=INT_MAX;
for(x=0;x<no_of_clusters;x++)
{
distance=Euclidian_metric(center_array[x],data[i]);
if(distance < min_distance)
{
min_distance=distance;
output[i]=x; /* Updates the cluster data point i belongs to */
}
}
update_center(output[i],i);
}
}