void
test_query_from_file(int verbose)
{
VlKDForest *forest;
int i;
float *data, *query;
vl_size dim;
vl_size num;
/*
* load
*/
data = load_data("data.bin", &dim, &num);
forest = load_VlKDForest("forest.bin");
forest->data = data;
if((query = (float *)malloc(dim * sizeof(float))) == NULL){
printf("not enough memoey\n");
exit(1);
}
for(i = 0;i < dim; i++) query[i] = 0.5;
if(verbose) printf("has created a query\n");
/*
* search neighbors
*/
vl_size numNeighbors = 10;
unsigned int numComparisons = 0 ;
unsigned int maxNumComparisons = 0 ;
VlKDForestNeighbor * neighbors ;
vl_kdforest_set_max_num_comparisons (forest, maxNumComparisons) ;
neighbors = vl_malloc (sizeof(VlKDForestNeighbor) * numNeighbors) ;
numComparisons = vl_kdforest_query (forest, neighbors, numNeighbors,
query);
for(i = 0;i < numNeighbors; i++){
printf("%d %f\n",
neighbors[i].index + 1, neighbors[i].distance);
/* check distance */
if(fabs(
dist_l2(dim, query, &data[neighbors[i].index * dim]) -
neighbors[i].distance) > 1e-6){
printf("%d distance is different. %f\n",
dist_l2(dim, query, &data[neighbors[i].index * dim]) );
}
/* check order */
if(i != 0 && neighbors[i-1].distance > neighbors[i].distance){
printf("order is wrong.\n");
}
}
vl_free(neighbors);
vl_kdforest_delete(forest);
free(data);
free(query);
}
bool VLFeat::Initialize_kmeans() {
bin_data dat(kmeansname);
kmeans = vl_kmeans_new(VL_TYPE_FLOAT, VlDistanceL2);
vl_kmeans_set_centers(kmeans, dat.vector_address(0),
dat.vdim(), dat.nobjects());
if (MethodVerbose())
cout << "VLFeat::Initialize_kmeans() read in kmeans <" << kmeansname
<< "> dim=" << descriptor_dim() << " nclust=" << nclusters()
<< endl;
if (kdtree_ntrees) {
kdtree = vl_kdforest_new(VL_TYPE_FLOAT, descriptor_dim(), kdtree_ntrees,
VlDistanceL2);
vl_kdforest_build(kdtree, nclusters(), means());
vl_kdforest_set_max_num_comparisons(kdtree, kdtree_maxcomps);
}
do_vlad = true;
return true;
}
void LLC::SetUp()
{
if (base_.get() == NULL || dim_ == 0 || num_base_ == 0)
{
cerr << "ERROR: must set the base before." << endl;
exit(-1);
}
if (kdforest_model_ != NULL)
vl_kdforest_delete(kdforest_model_);
kdforest_model_ = vl_kdforest_new(VL_TYPE_FLOAT, dim_, num_tree_,
dist_method_);
vl_kdforest_set_thresholding_method(kdforest_model_, thrd_method_);
vl_kdforest_set_max_num_comparisons(kdforest_model_, max_comp_);
vl_kdforest_build(kdforest_model_, num_base_, base_.get());
has_setup_ = true;
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_FOREST = 0, IN_DATA, IN_QUERY, IN_END} ;
enum {OUT_INDEX = 0, OUT_DISTANCE} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
VlKDForest * forest ;
mxArray const * forest_array = in[IN_FOREST] ;
mxArray const * data_array = in[IN_DATA] ;
mxArray const * query_array = in[IN_QUERY] ;
mxArray * index_array ;
mxArray * distance_array ;
void * query ;
vl_uint32 * index ;
void * distance ;
vl_size numNeighbors = 1 ;
vl_size numQueries ;
vl_uindex qi, ni;
unsigned int numComparisons = 0 ;
unsigned int maxNumComparisons = 0 ;
VlKDForestNeighbor * neighbors ;
mxClassID dataClass ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 3) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
}
if (nout > 2) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
forest = new_kdforest_from_array (forest_array, data_array) ;
dataClass = mxGetClassID (data_array) ;
if (mxGetClassID (query_array) != dataClass) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must have the same storage class as DATA.") ;
}
if (! vlmxIsReal (query_array)) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must be real.") ;
}
if (! vlmxIsMatrix (query_array, forest->dimension, -1)) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must be a matrix with TREE.NUMDIMENSIONS rows.") ;
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_num_neighs :
if (! vlmxIsScalar(optarg) ||
(numNeighbors = mxGetScalar(optarg)) < 1) {
vlmxError(vlmxErrInvalidArgument,
"NUMNEIGHBORS must be a scalar not smaller than one.") ;
}
break;
case opt_max_num_comparisons :
if (! vlmxIsScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument,
"MAXNUMCOMPARISONS must be a scalar.") ;
}
maxNumComparisons = mxGetScalar(optarg) ;
break;
case opt_verbose :
++ verbose ;
break ;
}
}
vl_kdforest_set_max_num_comparisons (forest, maxNumComparisons) ;
neighbors = vl_malloc (sizeof(VlKDForestNeighbor) * numNeighbors) ;
query = mxGetData (query_array) ;
numQueries = mxGetN (query_array) ;
out[OUT_INDEX] = index_array = mxCreateNumericMatrix
(numNeighbors, numQueries, mxUINT32_CLASS, mxREAL) ;
out[OUT_DISTANCE] = distance_array = mxCreateNumericMatrix
(numNeighbors, numQueries, dataClass, mxREAL) ;
index = mxGetData (index_array) ;
distance = mxGetData (distance_array) ;
if (verbose) {
VL_PRINTF ("vl_kdforestquery: number of queries: %d\n", numQueries) ;
VL_PRINTF ("vl_kdforestquery: number of neighbors per query: %d\n", numNeighbors) ;
VL_PRINTF ("vl_kdforestquery: max num of comparisons per query: %d\n",
vl_kdforest_get_max_num_comparisons (forest)) ;
}
for (qi = 0 ; qi < numQueries ; ++ qi) {
numComparisons += vl_kdforest_query (forest, neighbors, numNeighbors,
query) ;
switch (dataClass) {
case mxSINGLE_CLASS:
{
float * distance_ = (float*) distance ;
for (ni = 0 ; ni < numNeighbors ; ++ni) {
*index++ = neighbors[ni].index + 1 ;
*distance_++ = neighbors[ni].distance ;
}
query = (float*)query + vl_kdforest_get_data_dimension (forest) ;
distance = distance_ ;
break ;
}
case mxDOUBLE_CLASS:
{
double * distance_ = (double*) distance ;
for (ni = 0 ; ni < numNeighbors ; ++ni) {
*index++ = neighbors[ni].index + 1 ;
*distance_++ = neighbors[ni].distance ;
}
query = (double*)query + vl_kdforest_get_data_dimension (forest) ;
distance = distance_ ;
break ;
}
default:
abort() ;
}
}
if (verbose) {
VL_PRINTF ("vl_kdforestquery: number of comparisons per query: %.3f\n",
((double) numComparisons) / numQueries) ;
VL_PRINTF ("vl_kdforestquery: number of comparisons per neighbor: %.3f\n",
((double) numComparisons) / (numQueries * numNeighbors)) ;
}
vl_kdforest_delete (forest) ;
vl_free (neighbors) ;
}
void
test_simple(int verbose)
{
VlKDForest *forest;
int i, j;
float *data, *query;
vl_size dim = 128;
vl_size num = 10000;
vl_size numTrees = 1;
/*
* create a test data
*/
if((data = create_data(dim ,num)) == NULL){
printf("not enough memoey\n");
exit(1);
}
if(verbose) printf("has created a test data\n");
if((query = (float *)malloc(dim * sizeof(float))) == NULL){
printf("not enough memoey\n");
exit(1);
}
for(i = 0;i < dim; i++) query[i] = 0.5;
if(verbose) printf("has created a query\n");
/*
* build a kd-tree forest
*/
forest = kdtreebuild(1, dim, numTrees, VL_KDTREE_MEDIAN, num, data);
if(verbose) printf("has created a forest\n");
if(verbose && 0){
for(j = 0;j < numTrees; j++){
printf("dataIndex[%d] = [", j);
for(i = 0;i < forest->numData; i++){
printf("%d ",
forest->trees[j]->dataIndex[i].index + 1);
}
printf("]\n");
}
}
/*
* save
*/
save_data("data.bin", data, dim, num);
save_VlKDForest("forest.bin", forest);
/*
* search neighbors
*/
vl_size numNeighbors = 10;
unsigned int numComparisons = 0 ;
unsigned int maxNumComparisons = 0 ;
VlKDForestNeighbor * neighbors ;
vl_kdforest_set_max_num_comparisons (forest, maxNumComparisons) ;
neighbors = vl_malloc (sizeof(VlKDForestNeighbor) * numNeighbors) ;
numComparisons = vl_kdforest_query (forest, neighbors, numNeighbors,
query);
for(i = 0;i < numNeighbors; i++){
printf("%d %f\n",
neighbors[i].index + 1, neighbors[i].distance);
/* check distance */
if(fabs(
dist_l2(dim, query, &data[neighbors[i].index * dim]) -
neighbors[i].distance) > 1e-6){
printf("%d distance is different. %f\n",
dist_l2(dim, query, &data[neighbors[i].index * dim]) );
}
/* check order */
if(i != 0 && neighbors[i-1].distance > neighbors[i].distance){
printf("order is wrong.\n");
}
}
vl_free(neighbors);
vl_kdforest_delete(forest);
free(data);
free(query);
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_FOREST = 0, IN_DATA, IN_QUERY, IN_END} ;
enum {OUT_INDEX = 0, OUT_DISTANCE} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
VlKDForest * forest ;
mxArray const * forest_array = in[IN_FOREST] ;
mxArray const * data_array = in[IN_DATA] ;
mxArray const * query_array = in[IN_QUERY] ;
void * query ;
vl_uint32 * index ;
void * distance ;
vl_size numNeighbors = 1 ;
vl_size numQueries ;
unsigned int numComparisons = 0 ;
unsigned int maxNumComparisons = 0 ;
mxClassID dataClass ;
vl_index i ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 3) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
}
if (nout > 2) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
forest = new_kdforest_from_array (forest_array, data_array) ;
dataClass = mxGetClassID (data_array) ;
if (mxGetClassID (query_array) != dataClass) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must have the same storage class as DATA.") ;
}
if (! vlmxIsReal (query_array)) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must be real.") ;
}
if (! vlmxIsMatrix (query_array, forest->dimension, -1)) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must be a matrix with TREE.NUMDIMENSIONS rows.") ;
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_num_neighs :
if (! vlmxIsScalar(optarg) ||
(numNeighbors = mxGetScalar(optarg)) < 1) {
vlmxError(vlmxErrInvalidArgument,
"NUMNEIGHBORS must be a scalar not smaller than one.") ;
}
break;
case opt_max_num_comparisons :
if (! vlmxIsScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument,
"MAXNUMCOMPARISONS must be a scalar.") ;
}
maxNumComparisons = mxGetScalar(optarg) ;
break;
case opt_verbose :
++ verbose ;
break ;
}
}
vl_kdforest_set_max_num_comparisons (forest, maxNumComparisons) ;
query = mxGetData (query_array) ;
numQueries = mxGetN (query_array) ;
out[OUT_INDEX] = mxCreateNumericMatrix (numNeighbors, numQueries, mxUINT32_CLASS, mxREAL) ;
out[OUT_DISTANCE] = mxCreateNumericMatrix (numNeighbors, numQueries, dataClass, mxREAL) ;
index = mxGetData (out[OUT_INDEX]) ;
distance = mxGetData (out[OUT_DISTANCE]) ;
if (verbose) {
VL_PRINTF ("vl_kdforestquery: number of queries: %d\n", numQueries) ;
VL_PRINTF ("vl_kdforestquery: number of neighbors per query: %d\n", numNeighbors) ;
VL_PRINTF ("vl_kdforestquery: max num of comparisons per query: %d\n",
vl_kdforest_get_max_num_comparisons (forest)) ;
}
numComparisons = vl_kdforest_query_with_array (forest, index, numNeighbors, numQueries, distance, query) ;
vl_kdforest_delete(forest) ;
/* adjust for MATLAB indexing */
for (i = 0 ; i < (signed) (numNeighbors * numQueries) ; ++i) { index[i] ++ ; }
if (verbose) {
VL_PRINTF ("vl_kdforestquery: number of comparisons per query: %.3f\n",
((double) numComparisons) / numQueries) ;
VL_PRINTF ("vl_kdforestquery: number of comparisons per neighbor: %.3f\n",
((double) numComparisons) / (numQueries * numNeighbors)) ;
}
}
