/** ------------------------------------------------------------------
** @internal
** @brief MEX driver
**/
void mexFunction (int nout, mxArray * out[], int nin, const mxArray * in[])
{
vl_uint8 *data ;
enum {IN_DATA = 0, IN_K, IN_NLEAVES, IN_END} ;
enum {OUT_TREE = 0, OUT_ASGN} ;
int M, N, K = 2, depth = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
int nleaves = 1 ;
int method_type = VL_IKM_LLOYD ;
int max_niters = 200 ;
int verb = 0 ;
VlHIKMTree* tree ;
VL_USE_MATLAB_ENV ;
/* ------------------------------------------------------------------
* Check the arguments
* --------------------------------------------------------------- */
if (nin < 3)
{
mexErrMsgTxt ("At least three arguments required.");
}
else if (nout > 2)
{
mexErrMsgTxt ("Too many output arguments.");
}
if (mxGetClassID (in[IN_DATA]) != mxUINT8_CLASS)
{
mexErrMsgTxt ("DATA must be of class UINT8.");
}
if (! vlmxIsPlainScalar (in[IN_NLEAVES]) ||
(nleaves = (int) *mxGetPr (in[IN_NLEAVES])) < 1) {
mexErrMsgTxt ("NLEAVES must be a scalar not smaller than 2.") ;
}
M = mxGetM (in[IN_DATA]); /* n of components */
N = mxGetN (in[IN_DATA]); /* n of elements */
if (! vlmxIsPlainScalar (in[IN_K]) ||
(K = (int) *mxGetPr (in[IN_K])) > N ) {
mexErrMsgTxt ("Cannot have more clusters than data.") ;
}
data = (vl_uint8 *) mxGetPr (in[IN_DATA]) ;
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
char buf [1024] ;
switch (opt) {
case opt_verbose :
++ verb ;
break ;
case opt_max_niters :
if (!vlmxIsPlainScalar(optarg) || (max_niters = (int) *mxGetPr(optarg)) < 1) {
mexErrMsgTxt("MaxNiters must be not smaller than 1.") ;
}
break ;
case opt_method :
if (!vlmxIsString (optarg, -1)) {
mexErrMsgTxt("'Method' must be a string.") ;
}
if (mxGetString (optarg, buf, sizeof(buf))) {
mexErrMsgTxt("Option argument too long.") ;
}
if (strcmp("lloyd", buf) == 0) {
method_type = VL_IKM_LLOYD ;
} else if (strcmp("elkan", buf) == 0) {
method_type = VL_IKM_ELKAN ;
} else {
mexErrMsgTxt("Unknown cost type.") ;
}
break ;
default :
abort() ;
break ;
}
}
/* ---------------------------------------------------------------
* Do the job
* ------------------------------------------------------------ */
depth = VL_MAX(1, ceil (log (nleaves) / log(K))) ;
tree = vl_hikm_new (method_type) ;
if (verb) {
mexPrintf("hikmeans: # dims: %d\n", M) ;
mexPrintf("hikmeans: # data: %d\n", N) ;
mexPrintf("hikmeans: K: %d\n", K) ;
mexPrintf("hikmeans: depth: %d\n", depth) ;
}
vl_hikm_set_verbosity (tree, verb) ;
vl_hikm_init (tree, M, K, depth) ;
vl_hikm_train (tree, data, N) ;
out[OUT_TREE] = hikm_to_matlab (tree) ;
if (nout > 1) {
vl_uint *asgn ;
int j ;
out [OUT_ASGN] = mxCreateNumericMatrix
(vl_hikm_get_depth (tree), N, mxUINT32_CLASS, mxREAL) ;
asgn = mxGetData(out[OUT_ASGN]) ;
vl_hikm_push (tree, asgn, data, N) ;
for (j = 0 ; j < N*depth ; ++ j) asgn [j] ++ ;
}
if (verb) {
mexPrintf("hikmeans: done.\n") ;
}
/* vl_hikm_delete (tree) ; */
}
void hikm()
{
const vl_size data_dim = 2;
const vl_size num_data = 1000;
vl_uint8 data[data_dim * num_data] = { 0, };
for (vl_size i = 0; i < data_dim * num_data; ++i)
data[i] = (vl_uint8)std::floor((((float)std::rand() / RAND_MAX) * 255) + 0.5f);
//
std::cout << "start processing ..." << std::endl;
const VlIKMAlgorithms algorithm = VL_IKM_LLOYD;
const vl_size num_clusters = 3; // number of clusters per node.
const vl_size tree_depth = 5;
const vl_size num_max_iterations = 100;
VlHIKMTree *hikm = vl_hikm_new(algorithm);
vl_hikm_set_max_niters(hikm, num_max_iterations);
// initilization.
vl_hikm_init(hikm, data_dim, num_clusters, tree_depth);
// training.
vl_hikm_train(hikm, data, num_data);
{
//const int ndims = vl_hikm_get_ndims(hikm); // dim. of data.
//const int K = vl_hikm_get_K(hikm); // num. of clusters.
//const int depth = vl_hikm_get_depth(hikm); // tree depth.
//std::cout << "ndims: " << ndims << ", K: " << K << ", depth: " << depth << std::endl;
const VlHIKMNode *root = vl_hikm_get_root(hikm);
// TODO [implement] >> visualize the tree.
//
{
vl_uint assignments[num_data * tree_depth] = { 0, };
vl_hikm_push(hikm, assignments, data, num_data);
for (int i = 0; i < num_data; ++i)
{
std::cout << '(';
for (int j = 0; j < data_dim; ++j)
std::cout << (0 == j ? "" : ",") << (int)data[i * data_dim + j]; // TODO [check] >> is it correct?
std::cout << ") => ";
for (int k = 0; k < tree_depth; ++k)
std::cout << (0 == k ? "" : "-") << assignments[i * tree_depth + k]; // TODO [check] >> is it correct?
std::cout << std::endl;
}
}
}
std::cout << "end processing ..." << std::endl;
//
if (hikm)
{
vl_hikm_delete(hikm);
hikm = NULL;
}
}
/** @brief MEX driver entry point
**/
void mexFunction (int nout, mxArray * out[], int nin, const mxArray * in[])
{
enum {IN_TREE = 0, IN_DATA, IN_END} ;
enum {OUT_ASGN = 0} ;
vl_uint8 const *data;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
int N = 0 ;
int method_type = VL_IKM_LLOYD ;
int verb = 0 ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 2)
mexErrMsgTxt ("At least two arguments required.");
else if (nout > 1)
mexErrMsgTxt ("Too many output arguments.");
if (mxGetClassID (in[IN_DATA]) != mxUINT8_CLASS) {
mexErrMsgTxt ("DATA must be of class UINT8");
}
N = mxGetN (in[IN_DATA]); /* n of elements */
data = (vl_uint8 *) mxGetPr (in[IN_DATA]);
while ((opt = uNextOption(in, nin, options, &next, &optarg)) >= 0) {
char buf [1024] ;
switch (opt) {
case opt_verbose :
++ verb ;
break ;
case opt_method :
if (!uIsString (optarg, -1)) {
mexErrMsgTxt("'Method' must be a string.") ;
}
if (mxGetString (optarg, buf, sizeof(buf))) {
mexErrMsgTxt("Option argument too long.") ;
}
if (strcmp("lloyd", buf) == 0) {
method_type = VL_IKM_LLOYD ;
} else if (strcmp("elkan", buf) == 0) {
method_type = VL_IKM_ELKAN ;
} else {
mexErrMsgTxt("Unknown cost type.") ;
}
break ;
default :
assert(0) ;
break ;
}
}
/* -----------------------------------------------------------------
* Do the job
* -------------------------------------------------------------- */
{
VlHIKMTree *tree ;
vl_uint *ids ;
int j;
int depth ;
tree = matlab_to_hikm (in[IN_TREE], method_type) ;
depth = vl_hikm_get_depth (tree) ;
if (verb) {
mexPrintf("hikmeanspush: ndims: %d K: %d depth: %d\n",
vl_hikm_get_ndims (tree),
vl_hikm_get_K (tree),
depth) ;
}
out[OUT_ASGN] = mxCreateNumericMatrix (depth, N, mxUINT32_CLASS, mxREAL) ;
ids = mxGetData (out[OUT_ASGN]) ;
vl_hikm_push (tree, ids, data, N) ;
vl_hikm_delete (tree) ;
for (j = 0 ; j < N*depth ; j++) ids [j] ++ ;
}
}
/** ----------------------------------------------------------------
**
**/
PyObject * vl_hikmeanspush_python(
VlHIKMTree_python & inTree,
PyArrayObject & inData,
int verb,
char * method)
{
vl_uint8 const *data;
int N = 0;
int method_type = VL_IKM_LLOYD;
N = inData.dimensions[1]; /* n of elements */
#ifdef DEBUG
printf("n of elements: %d\n", N);
printf("n of split: %d\n", inTree.K);
printf("depth: %d\n", inTree.depth);
printf("n of children: %d\n", inTree.sub.size());
#endif
data = (vl_uint8 *) inData.data;
if (strcmp("lloyd", method) == 0) {
method_type = VL_IKM_LLOYD;
} else if (strcmp("elkan", method) == 0) {
method_type = VL_IKM_ELKAN;
} else {
printf("Unknown cost type.\n");
}
/* -----------------------------------------------------------------
* Do the job
* -------------------------------------------------------------- */
VlHIKMTree * tree;
vl_uint *ids;
int j;
int depth;
tree = python_to_hikm(inTree, method_type);
depth = vl_hikm_get_depth(tree);
if (verb) {
printf("hikmeanspush: ndims: %d K: %d depth: %d\n", vl_hikm_get_ndims(
tree), vl_hikm_get_K(tree), depth);
}
npy_intp dims[2] = { depth, N };
PyArrayObject * out_asgn = (PyArrayObject*) PyArray_NewFromDescr(
&PyArray_Type, PyArray_DescrFromType(PyArray_INT32), 2, dims, NULL,
NULL, NPY_F_CONTIGUOUS, NULL);
ids = (vl_uint *) out_asgn->data;
vl_hikm_push(tree, ids, data, N);
vl_hikm_delete(tree);
for (j = 0; j < N * depth; j++)
ids[j]++;
return PyArray_Return(out_asgn);
}