void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_I = 0, IN_S, IN_END} ;
enum {OUT_J = 0} ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
int padding = VL_PAD_BY_CONTINUITY ;
int kernel = GAUSSIAN ;
int flags ;
vl_size step = 1 ;
int verb = 0 ;
double sigma ;
mxClassID classid ;
mwSize M, N, K, M_, N_, ndims ;
mwSize dims_ [3] ;
mwSize const * dims ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 2) {
mexErrMsgTxt("At least two input arguments required.");
} else if (nout > 1) {
mexErrMsgTxt("Too many output arguments.");
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_padding :
{
enum {buflen = 32} ;
char buf [buflen] ;
if (!vlmxIsString(optarg, -1)) {
vlmxError(vlmxErrInvalidArgument,
"PADDING argument must be a string.") ;
}
mxGetString(optarg, buf, buflen) ;
buf [buflen - 1] = 0 ;
if (vlmxCompareStringsI("zero", buf) == 0) {
padding = VL_PAD_BY_ZERO ;
} else if (vlmxCompareStringsI("continuity", buf) == 0) {
padding = VL_PAD_BY_CONTINUITY ;
} else {
vlmxError(vlmxErrInvalidArgument,
"PADDING must be either ZERO or CONTINUITY, was '%s'.",
buf) ;
}
break ;
}
case opt_subsample :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument,
"SUBSAMPLE must be a scalar.") ;
}
step = *mxGetPr(optarg) ;
if (step < 1) {
vlmxError(vlmxErrInvalidArgument,
"SUBSAMPLE must be not less than one.") ;
}
break ;
case opt_kernel :
{
enum {buflen = 32} ;
char buf [buflen] ;
if (!vlmxIsString(optarg, -1)) {
vlmxError(vlmxErrInvalidArgument,
"KERNEL argument must be a string.") ;
}
mxGetString(optarg, buf, buflen) ;
buf [buflen - 1] = 0 ;
if (vlmxCompareStringsI("gaussian", buf) == 0) {
kernel = GAUSSIAN ;
} else if (vlmxCompareStringsI("triangular", buf) == 0) {
kernel = TRIANGULAR ;
} else {
vlmxError(vlmxErrInvalidArgument,
"Unknown kernel type '%s'.",
buf) ;
}
break ;
}
case opt_verbose :
++ verb ;
break ;
default:
abort() ;
}
}
if (! vlmxIsPlainScalar(IN(S))) {
vlmxError(vlmxErrInvalidArgument,
"S must be a real scalar.") ;
}
classid = mxGetClassID(IN(I)) ;
if (classid != mxDOUBLE_CLASS &&
classid != mxSINGLE_CLASS) {
vlmxError(vlmxErrInvalidArgument,
"I must be either DOUBLE or SINGLE.") ;
}
if (mxGetNumberOfDimensions(IN(I)) > 3) {
vlmxError(vlmxErrInvalidArgument,
"I must be either a two or three dimensional array.") ;
}
ndims = mxGetNumberOfDimensions(IN(I)) ;
dims = mxGetDimensions(IN(I)) ;
M = dims[0] ;
N = dims[1] ;
K = (ndims > 2) ? dims[2] : 1 ;
sigma = * mxGetPr(IN(S)) ;
if ((sigma < 0.01) && (step == 1)) {
OUT(J) = mxDuplicateArray(IN(I)) ;
return ;
}
M_ = (M - 1) / step + 1 ;
N_ = (N - 1) / step + 1 ;
dims_ [0] = M_ ;
dims_ [1] = N_ ;
if (ndims > 2) dims_ [2] = K ;
OUT(J) = mxCreateNumericArray(ndims, dims_, classid, mxREAL) ;
if (verb) {
char const *classid_str = 0, *kernel_str = 0, *padding_str = 0 ;
switch (padding) {
case VL_PAD_BY_ZERO : padding_str = "with zeroes" ; break ;
case VL_PAD_BY_CONTINUITY : padding_str = "by continuity" ; break ;
default: abort() ;
}
switch (classid) {
case mxDOUBLE_CLASS: classid_str = "DOUBLE" ; break ;
case mxSINGLE_CLASS: classid_str = "SINGLE" ; break ;
default: abort() ;
}
switch (kernel) {
case GAUSSIAN: kernel_str = "Gaussian" ; break ;
case TRIANGULAR: kernel_str = "triangular" ; break ;
default: abort() ;
}
mexPrintf("vl_imsmooth: [%dx%dx%d] -> [%dx%dx%d] (%s, subsampling step %d)\n",
N, M, K, N_, M_, K, classid_str, step) ;
mexPrintf("vl_imsmooth: padding: %s\n", padding_str) ;
mexPrintf("vl_imsmooth: kernel: %s\n", kernel_str) ;
mexPrintf("vl_imsmooth: sigma: %g\n", sigma) ;
mexPrintf("vl_imsmooth: SIMD enabled: %s\n",
vl_get_simd_enabled() ? "yes" : "no") ;
}
/* -----------------------------------------------------------------
* Do the job
* -------------------------------------------------------------- */
flags = padding ;
flags |= VL_TRANSPOSE ;
switch (classid) {
case mxSINGLE_CLASS:
_vl_imsmooth_smooth_f ((float*) mxGetPr(OUT(J)),
M_, N_,
(float const*) mxGetPr(IN(I)),
M, N, K,
kernel, sigma, step, flags) ;
break ;
case mxDOUBLE_CLASS:
_vl_imsmooth_smooth_d ((double*) mxGetPr(OUT(J)),
M_, N_,
(double const*) mxGetPr(IN(I)),
M, N, K,
kernel, sigma, step, flags) ;
break ;
default:
abort() ;
}
}
/** ------------------------------------------------------------------
** @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) ; */
}
/* driver */
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {
MANIP_STATE,
RUN_GENERATOR
} mode ;
VlRand * rand ;
VL_USE_MATLAB_ENV ;
rand = vl_get_rand() ;
/** -----------------------------------------------------------------
** Check the arguments
** -------------------------------------------------------------- */
if (nout > 1) {
vlmxError(vlmxErrTooManyInputArguments, NULL) ;
}
if (nin > 0 && ! mxIsNumeric(in[0])) {
mode = MANIP_STATE ;
} else {
mode = RUN_GENERATOR ;
}
switch (mode) {
case RUN_GENERATOR:
{
enum { maxNumDimensions = 30 } ;
vl_size numDimensions = 2, n ;
vl_uindex k ;
mwSize dimensions [maxNumDimensions] = {1, 1} ;
double * x ;
if (nin > 1) {
/* TWISTER(N1 N2 ...) style */
if (nin >= maxNumDimensions) {
vlmxError(vlmxErrTooManyInputArguments,
"Too many dimensions specified.") ;
}
for (k = 0 ; k < (unsigned)nin ; ++k) {
if (! vlmxIsPlainScalar(in[k])) {
vlmxError(vlmxErrInvalidArgument,
"The %d-th argument is not a plain scalar.", k + 1) ;
}
if (mxGetScalar(in[k]) < 0) {
vlmxError(vlmxErrInvalidArgument,
"The %d-th argument is negative.", k + 1) ;
}
dimensions[k] = mxGetScalar(in[k]) ;
}
numDimensions = k ;
} else if (nin == 1) {
/* TWISTER([N1 N2 ...]) style */
if (! vlmxIsPlainVector(in[0], -1)) {
vlmxError(vlmxErrInvalidArgument,
"The argument is not a plain vector.") ;
}
x = mxGetPr(in[0]) ;
n = mxGetNumberOfElements(in[0]) ;
numDimensions = VL_MAX(2, n) ;
if (numDimensions > maxNumDimensions) {
vlmxError(vlmxErrInvalidArgument,
"Too many dimensions specified.") ;
}
if (n == 1) {
if (*x < 0) {
vlmxError(vlmxErrInvalidArgument,
"The specified dimension is negative.") ;
}
dimensions[0] = dimensions[1] = *x ;
} else {
for (k = 0 ; k < n ; ++k) {
if (x[k] < 0) {
vlmxError(vlmxErrInvalidArgument,
"One of the specified dimensions is negative.") ;
}
dimensions[k] = x[k] ;
}
}
}
out[0] = mxCreateNumericArray (numDimensions, dimensions, mxDOUBLE_CLASS, mxREAL) ;
n = mxGetNumberOfElements (out[0]) ;
x = mxGetPr (out[0]) ;
for (k = 0 ; k < n ; ++k) {
x[k] = vl_rand_res53(rand) ;
}
}
break ;
case MANIP_STATE:
{
enum { buff_size = 32 } ;
char buff [buff_size] ;
/* check for 'state' string */
if (! vlmxIsString(in[0], -1) ||
mxGetString(in[0], buff, buff_size) ||
vl_string_casei_cmp ("state", buff) != 0 ) {
vlmxError(vlmxErrInvalidArgument, NULL) ;
}
/* TWISTER('state') */
if (nin == 1) {
vl_uindex i ;
vl_uint32 * data ;
out[0] = mxCreateNumericMatrix (625, 1, mxUINT32_CLASS, mxREAL) ;
data = mxGetData(out[0]) ;
for (i = 0 ; i < 624 ; ++i) data[i] = rand->mt[i] ;
data[624] = (vl_uint32) rand->mti ;
} else {
if (vlmxIsPlainScalar(in[1])) {
/* TWISTER('state', X) */
vl_uint32 x = (vl_uint32) mxGetScalar(in[1]) ;
vl_rand_seed (rand, x) ;
} else if (mxIsNumeric(in[1]) &&
mxGetClassID(in[1]) == mxUINT32_CLASS &&
mxGetNumberOfElements(in[1]) == 624+1 &&
((vl_uint32 const*)mxGetData(in[1]))[624] <= 624 ) {
/* TWISTER('state', STATE) */
vl_uindex i ;
vl_uint32 * data = mxGetData(in[1]) ;
for (i = 0 ; i < 624 ; ++i) rand->mt[i] = data[i] ;
rand->mti = data [624] ;
} else if (mxIsNumeric(in[1]) &&
mxGetClassID(in[1]) == mxDOUBLE_CLASS &&
mxGetNumberOfElements(in[1]) <= 624) {
/* TWISTER('state', KEY) */
vl_uint32 key [624] ;
double const * x = mxGetPr(in[1]) ;
vl_size n = mxGetNumberOfElements(in[1]) ;
vl_uindex k ;
for (k = 0 ; k < n ; ++k) {
key [k] = x [k] ;
}
vl_rand_seed_by_array (rand, key, n) ;
}
}
}
break ;
default:
abort() ;
}
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_DATA = 0, IN_END} ;
enum {OUT_TREE = 0} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
VlKDForest * forest ;
void * data ;
vl_size numData ;
vl_size dimension ;
mxClassID dataClass ;
vl_type dataType ;
int thresholdingMethod = VL_KDTREE_MEDIAN ;
vl_size numTrees = 1 ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 1) {
vlmxError(vlmxErrInvalidArgument,
"At least one argument required") ;
} else if (nout > 2) {
vlmxError(vlmxErrInvalidArgument,
"Too many output arguments");
}
dataClass = mxGetClassID(IN(DATA)) ;
if (! vlmxIsMatrix (IN(DATA), -1, -1) ||
! vlmxIsReal (IN(DATA))) {
vlmxError(vlmxErrInvalidArgument,
"DATA must be a real matrix ") ;
}
switch (dataClass) {
case mxSINGLE_CLASS : dataType = VL_TYPE_FLOAT ; break ;
case mxDOUBLE_CLASS : dataType = VL_TYPE_DOUBLE ; break ;
default:
vlmxError(vlmxErrInvalidArgument,
"DATA must be either SINGLE or DOUBLE") ;
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
char buffer [1024] ;
switch (opt) {
case opt_threshold_method :
mxGetString (optarg, buffer, sizeof(buffer)/sizeof(buffer[0])) ;
if (! vlmxIsString(optarg, -1)) {
vlmxError(vlmxErrInvalidOption,
"THRESHOLDMETHOD must be a string") ;
}
if (vl_string_casei_cmp(buffer, "median") == 0) {
thresholdingMethod = VL_KDTREE_MEDIAN ;
} else if (vl_string_casei_cmp(buffer, "mean") == 0) {
thresholdingMethod = VL_KDTREE_MEAN ;
} else {
vlmxError(vlmxErrInvalidOption,
"Unknown thresholding method %s", buffer) ;
}
break ;
case opt_num_trees :
if (! vlmxIsScalar(optarg) ||
(numTrees = mxGetScalar(optarg)) < 1) {
vlmxError(vlmxErrInvalidOption,
"NUMTREES must be not smaller than one") ;
}
break ;
case opt_verbose :
++ verbose ;
break ;
}
}
data = mxGetData (IN(DATA)) ;
numData = mxGetN (IN(DATA)) ;
dimension = mxGetM (IN(DATA)) ;
forest = vl_kdforest_new (dataType, dimension, numTrees) ;
vl_kdforest_set_thresholding_method (forest, thresholdingMethod) ;
if (verbose) {
char const * str = 0 ;
mexPrintf("vl_kdforestbuild: data %s [%d x %d]\n",
vl_get_type_name (dataType), dimension, numData) ;
switch (vl_kdforest_get_thresholding_method(forest)) {
case VL_KDTREE_MEAN : str = "mean" ; break ;
case VL_KDTREE_MEDIAN : str = "median" ; break ;
default: abort() ;
}
mexPrintf("vl_kdforestbuild: threshold selection method: %s\n", str) ;
mexPrintf("vl_kdforestbuild: number of trees: %d\n",
vl_kdforest_get_num_trees(forest)) ;
}
/* -----------------------------------------------------------------
* Do job
* -------------------------------------------------------------- */
vl_kdforest_build (forest, numData, data) ;
if (verbose) {
vl_uindex ti ;
for (ti = 0 ; ti < vl_kdforest_get_num_trees(forest) ; ++ ti) {
mexPrintf("vl_kdforestbuild: tree %d: depth %d, num nodes %d\n",
ti,
vl_kdforest_get_depth_of_tree(forest, ti),
vl_kdforest_get_num_nodes_of_tree(forest, ti)) ;
}
}
out[OUT_TREE] = new_array_from_kdforest (forest) ;
vl_kdforest_delete (forest) ;
}
/* driver */
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_X=0,IN_C,IN_END} ;
enum {OUT_ASGN=0} ;
vl_uint* asgn ;
vl_ikm_acc* centers ;
vl_uint8* data ;
int M,N,j,K=0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
int method_type = VL_IKM_LLOYD ;
int verb = 0 ;
VlIKMFilt *ikmf ;
VL_USE_MATLAB_ENV ;
/** -----------------------------------------------------------------
** Check the arguments
** -------------------------------------------------------------- */
if (nin < 2) {
mexErrMsgTxt("At least two arguments required.") ;
} else if (nout > 2) {
mexErrMsgTxt("Too many output arguments.") ;
}
if(mxGetClassID(in[IN_X]) != mxUINT8_CLASS) {
mexErrMsgTxt("X must be of class UINT8") ;
}
if(mxGetClassID(in[IN_C]) != mxINT32_CLASS) {
mexErrMsgTxt("C must be of class INT32") ;
}
M = mxGetM(in[IN_X]) ; /* n of components */
N = mxGetN(in[IN_X]) ; /* n of elements */
K = mxGetN(in[IN_C]) ; /* n of centers */
if( (int) mxGetM(in[IN_C]) != M ) {
mexErrMsgTxt("DATA and CENTERS must have the same number of columns.") ;
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
char buf [1024] ;
switch (opt) {
case opt_verbose :
++ verb ;
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() ;
}
}
/** -----------------------------------------------------------------
** Check the arguments
** -------------------------------------------------------------- */
if (verb) {
char const * method_name = 0 ;
switch (method_type) {
case VL_IKM_LLOYD: method_name = "Lloyd" ; break ;
case VL_IKM_ELKAN: method_name = "Elkan" ; break ;
default :
abort() ;
}
mexPrintf("ikmeanspush: Method = %s\n", method_name) ;
mexPrintf("ikmeanspush: ndata = %d\n", N) ;
}
out[OUT_ASGN] = mxCreateNumericMatrix (1, N, mxUINT32_CLASS, mxREAL) ;
data = (vl_uint8*) mxGetData (in[IN_X]) ;
centers = (vl_ikm_acc*) mxGetData (in[IN_C]) ;
asgn = (vl_uint*) mxGetData (out[OUT_ASGN]) ;
ikmf = vl_ikm_new (method_type) ;
vl_ikm_set_verbosity (ikmf, verb) ;
vl_ikm_init (ikmf, centers, M, K) ;
vl_ikm_push (ikmf, asgn, data, N) ;
/* adjust for MATLAB indexing */
for(j = 0 ; j < N ; ++j) ++ asgn[j] ;
vl_ikm_delete (ikmf) ;
}
/* driver */
void mexFunction (int nout, mxArray * out[], int nin, const mxArray * in[])
{
enum {IN_X = 0, IN_K, IN_END} ;
enum {OUT_C = 0, OUT_I} ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
int M, N, K = 0 ;
int err = 0 ;
vl_uint *asgn = 0 ;
vl_ikm_acc *centers = 0 ;
vl_uint8 *data ;
int method_type = VL_IKM_LLOYD ;
int max_niters = 200 ;
int verb = 0 ;
VlIKMFilt *ikmf ;
VL_USE_MATLAB_ENV ;
/* ------------------------------------------------------------------
* Check the arguments
* --------------------------------------------------------------- */
if (nin < 2) {
mexErrMsgTxt ("At least two arguments required.");
}
else if (nout > 2) {
mexErrMsgTxt ("Too many output arguments.");
}
if (mxGetClassID (in[IN_X]) != mxUINT8_CLASS) {
mexErrMsgTxt ("X must be of class uint8");
}
M = mxGetM (in[IN_X]); /* n of components */
N = mxGetN (in[IN_X]); /* n of elements */
if (!vlmxIsPlainScalar (in[IN_K]) ||
(K = (int) *mxGetPr(in[IN_K])) < 1 ||
K > N ) {
mexErrMsgTxt ("K must be a positive integer not greater than the number of 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 method type.") ;
}
break ;
default :
abort() ;
}
}
/* ------------------------------------------------------------------
* Do the job
* --------------------------------------------------------------- */
if (verb) {
char const * method_name = 0 ;
switch (method_type) {
case VL_IKM_LLOYD: method_name = "Lloyd" ; break ;
case VL_IKM_ELKAN: method_name = "Elkan" ; break ;
default :
abort() ;
}
mexPrintf("ikmeans: MaxInters = %d\n", max_niters) ;
mexPrintf("ikmeans: Method = %s\n", method_name) ;
}
data = (vl_uint8*) mxGetPr(in[IN_X]) ;
ikmf = vl_ikm_new (method_type) ;
vl_ikm_set_verbosity (ikmf, verb) ;
vl_ikm_set_max_niters (ikmf, max_niters) ;
vl_ikm_init_rand_data (ikmf, data, M, N, K) ;
err = vl_ikm_train (ikmf, data, N) ;
if (err) mexWarnMsgTxt("ikmeans: possible overflow!") ;
/* ------------------------------------------------------------------
* Return results
* --------------------------------------------------------------- */
{
out[OUT_C] = mxCreateNumericMatrix (M, K, mxINT32_CLASS, mxREAL) ;
centers = mxGetData (out[OUT_C]) ;
memcpy (centers, vl_ikm_get_centers (ikmf), sizeof(vl_ikm_acc) * M * K) ;
}
if (nout > 1) {
int j ;
out[OUT_I] = mxCreateNumericMatrix (1, N, mxUINT32_CLASS, mxREAL) ;
asgn = mxGetData (out[OUT_I]) ;
vl_ikm_push (ikmf, asgn, data, N) ;
for (j = 0 ; j < N ; ++j)
++ asgn [j] ;
}
vl_ikm_delete (ikmf) ;
if (verb) {
mexPrintf("ikmeans: done\n") ;
}
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_DATA, IN_LABELS, IN_LAMBDA, IN_END} ;
enum {OUT_MODEL = 0, OUT_BIAS, OUT_INFO} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
mxArray *inputModel = NULL;
VlSvmPegasos* svm = NULL ;
vl_bool freeModel = VL_TRUE ;
vl_size dataDimension ;
vl_uint32* matlabPermutation ;
void * data ;
mxClassID dataClass ;
vl_type dataType ;
vl_size numSamples ;
vl_uint32 * permutation = NULL ;
vl_size permutationSize = 0 ;
DiagnosticsDispatcher* disp ;
VlSvmDatasetInnerProduct innerProduct = NULL ;
VlSvmDatasetAccumulator accumulator = NULL ;
/* maps */
VlSvmDatasetFeatureMap mapFunc = NULL ;
/* Homkermap */
VlHomogeneousKernelType kernelType = VlHomogeneousKernelChi2 ;
VlHomogeneousKernelMapWindowType windowType = VlHomogeneousKernelMapWindowRectangular ;
double gamma = 1.0 ;
int n = 0 ;
double period = -1 ;
VlSvmDataset* dataset ;
vl_bool homkermap = VL_FALSE ;
void * map = NULL ;
VL_USE_MATLAB_ENV ;
disp = (DiagnosticsDispatcher*) vl_malloc(sizeof(DiagnosticsDispatcher)) ;
disp->diagnosticsHandle = NULL ;
disp->callerRef = NULL ;
disp->verbose = 0 ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 3) {
vlmxError(vlmxErrInvalidArgument,
"At least three arguments are required.") ;
} else if (nout > 3) {
vlmxError(vlmxErrInvalidArgument,
"Too many output arguments.");
}
dataClass = mxGetClassID(IN(DATA)) ;
if (! vlmxIsMatrix (IN(DATA), -1, -1) ||
! vlmxIsReal (IN(DATA))) {
vlmxError(vlmxErrInvalidArgument,
"DATA must be a real matrix.") ;
}
data = mxGetData (IN(DATA)) ;
dataDimension = mxGetM(IN(DATA)) ;
numSamples = mxGetN(IN(DATA)) ;
/* Get order of the HOMKERMAP, if used. */
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
if (opt == opt_homkermap) {
homkermap = VL_TRUE ;
if (! vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "N is not a scalar.") ;
}
n = *mxGetPr(optarg) ;
if (n < 0) {
vlmxError(vlmxErrInvalidArgument, "N is negative.") ;
}
}
}
next = IN_END ;
if (! vlmxIsVector(IN(LABELS), numSamples)) {
vlmxError(vlmxErrInvalidArgument, "LABELS is not a vector of dimension compatible with DATA.") ;
}
switch (dataClass) {
case mxSINGLE_CLASS : dataType = VL_TYPE_FLOAT ; break ;
case mxDOUBLE_CLASS : dataType = VL_TYPE_DOUBLE ; break ;
default:
vlmxError(vlmxErrInvalidArgument,
"DATA must be either SINGLE or DOUBLE.") ;
}
if (mxGetClassID(IN(LABELS)) != mxINT8_CLASS) {
vlmxError(vlmxErrInvalidArgument, "LABELS must be INT8.") ;
}
if (! vlmxIsPlainScalar(IN(LAMBDA))) {
vlmxError(vlmxErrInvalidArgument, "LAMBDA is not a plain scalar.") ;
}
svm = vl_svmpegasos_new ((2*n + 1)*dataDimension,*mxGetPr(IN(LAMBDA))) ;
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_bias_multiplier :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "BIASMULTIPLIER is not a plain scalar.") ;
}
vl_svmpegasos_set_bias_multiplier(svm, *mxGetPr(optarg)) ;
break ;
case opt_max_iterations :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "MAXITERATIONS is not a plain scalar.") ;
}
if (*mxGetPr(optarg) < 0) {
vlmxError(vlmxErrInvalidArgument, "MAXITERATIONS is negative.") ;
}
vl_svmpegasos_set_maxiterations(svm, (vl_size) *mxGetPr(optarg)) ;
break ;
case opt_epsilon :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "EPSILON is not a plain scalar.") ;
}
if (*mxGetPr(optarg) < 0) {
vlmxError(vlmxErrInvalidArgument, "EPSILON is negative.") ;
}
vl_svmpegasos_set_epsilon(svm, (double) *mxGetPr(optarg)) ;
break ;
case opt_starting_iteration :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "STARTINGITERATION is not a plain scalar.") ;
}
if (*mxGetPr(optarg) < 1) {
vlmxError(vlmxErrInvalidArgument, "STARTINGITERATION is smaller than 1.") ;
}
vl_svmpegasos_set_iterations(svm, (vl_size) *mxGetPr(optarg) - 1) ;
break ;
case opt_starting_model :
if (!vlmxIsVector(optarg, -1) ||
mxIsComplex(optarg) ||
mxGetClassID(optarg) != mxDOUBLE_CLASS) {
vlmxError(vlmxErrInvalidArgument, "STARTINGMODEL is not a real vector.") ;
}
inputModel = mxDuplicateArray(optarg) ;
vl_svmpegasos_set_model(svm,(double*) mxGetData(inputModel)) ;
freeModel = VL_FALSE ;
break ;
case opt_starting_bias :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "STARTINGBIAS is not a plain scalar.") ;
}
vl_svmpegasos_set_bias(svm, (double) *mxGetPr(optarg)) ;
break ;
case opt_permutation :
if (!vlmxIsVector(optarg, -1) ||
mxIsComplex(optarg) ||
mxGetClassID(optarg) != mxUINT32_CLASS) {
vlmxError(vlmxErrInvalidArgument, "PERMUTATION is not a UINT32 vector.") ;
}
permutationSize = mxGetNumberOfElements(optarg) ;
permutation = mxMalloc(sizeof(vl_uint32) * permutationSize) ;
matlabPermutation = mxGetData(optarg) ;
{
/* adjust indexing */
vl_uindex k ;
for (k = 0 ; k < permutationSize ; ++k) {
permutation[k] = matlabPermutation[k] - 1 ;
if (permutation[k] >= numSamples) {
vlmxError(vlmxErrInconsistentData,
"Permutation indexes out of bounds: PERMUTATION(%d) = %d > %d = number of data samples.",
k + 1, permutation[k] + 1, numSamples) ;
}
}
}
vl_svmpegasos_set_permutation(svm,permutation,permutationSize) ;
break ;
case opt_bias_learningrate :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "BIASLEARNINGRATE is not a plain scalar.") ;
}
if (mxGetClassID(optarg) != mxDOUBLE_CLASS) {
vlmxError(vlmxErrInvalidArgument, "BIASLEARNINGRATE must be double.") ;
}
vl_svmpegasos_set_bias_learningrate(svm, (double)*mxGetPr(optarg)) ;
break ;
case opt_diagnostic :
if( !mxIsClass( optarg , "function_handle")) {
mexErrMsgTxt("DIAGNOSTICSFUNCTION must be a function handle.");
}
disp->diagnosticsHandle = (mxArray*)(optarg) ;
break ;
case opt_diagnostic_caller_ref :
disp->callerRef = (mxArray*)(optarg) ;
break ;
case opt_energy_freq :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "ENERGYFREQ is not a plain scalar.") ;
}
vl_svmpegasos_set_energy_frequency (svm, (vl_size)*mxGetPr(optarg)) ;
break ;
case opt_verbose :
++ verbose ;
disp->verbose = 1 ;
break ;
case opt_KINTERS:
case opt_KL1:
kernelType = VlHomogeneousKernelIntersection ;
break ;
case opt_KCHI2:
kernelType = VlHomogeneousKernelChi2 ;
break ;
case opt_KJS:
kernelType = VlHomogeneousKernelJS ;
break ;
case opt_period:
if (! vlmxIsPlainScalar(optarg)){
vlmxError(vlmxErrInvalidArgument, "PERIOD is not a scalar.") ;
}
period = *mxGetPr(optarg) ;
if (period <= 0) {
vlmxError(vlmxErrInvalidArgument, "PERIOD is not positive.") ;
}
break ;
case opt_gamma:
if (! vlmxIsPlainScalar(optarg)){
vlmxError(vlmxErrInvalidArgument, "GAMMA is not a scalar.") ;
}
gamma = *mxGetPr(optarg) ;
if (gamma <= 0) {
vlmxError(vlmxErrInvalidArgument, "GAMMA is not positive.") ;
}
break ;
case opt_window:
if (! vlmxIsString(optarg,-1)){
vlmxError(vlmxErrInvalidArgument, "WINDOW is not a string.") ;
} else {
char buffer [1024] ;
mxGetString(optarg, buffer, sizeof(buffer) / sizeof(char)) ;
if (vl_string_casei_cmp("uniform", buffer) == 0) {
windowType = VlHomogeneousKernelMapWindowUniform ;
} else if (vl_string_casei_cmp("rectangular", buffer) == 0) {
windowType = VlHomogeneousKernelMapWindowRectangular ;
} else {
vlmxError(vlmxErrInvalidArgument, "WINDOW=%s is not recognized.", buffer) ;
}
}
break ;
}
}
if (verbose) {
mexPrintf("vl_pegasos: Lambda = %g\n", svm->lambda) ;
mexPrintf("vl_pegasos: BiasMultiplier = %g\n", svm->biasMultiplier) ;
mexPrintf("vl_pegasos: MaxIterations = %d\n", svm->maxIterations) ;
mexPrintf("vl_pegasos: permutation size = %d\n", permutationSize) ;
}
switch (dataType) {
case VL_TYPE_FLOAT :
innerProduct = (VlSvmDatasetInnerProduct)&vl_svmdataset_innerproduct_f ;
accumulator = (VlSvmDatasetAccumulator)&vl_svmdataset_accumulator_f ;
break ;
case VL_TYPE_DOUBLE:
innerProduct = (VlSvmDatasetInnerProduct)&vl_svmdataset_innerproduct_d ;
accumulator = (VlSvmDatasetAccumulator)&vl_svmdataset_accumulator_d ;
break ;
}
dataset = vl_svmdataset_new(data,dataDimension) ;
if (homkermap) {
map = vl_homogeneouskernelmap_new (kernelType, gamma, n, period, windowType) ;
mapFunc = (VlSvmDatasetFeatureMap)&vl_homogeneouskernelmap_evaluate_d ;
vl_svmdataset_set_map(dataset,map,mapFunc,2*n + 1) ;
}
/* -----------------------------------------------------------------
* Do job
* -------------------------------------------------------------- */
if (disp->diagnosticsHandle) {
vl_svmpegasos_set_diagnostic (svm, (VlSvmDiagnostics)&diagnosticDispatcher, disp) ;
}
vl_svmpegasos_train (svm,dataset, numSamples,innerProduct, accumulator,
(vl_int8 const *)mxGetData(IN(LABELS))) ;
/* -----------------------------------------------------------------
* Output
* -------------------------------------------------------------- */
if (nout >= 1) {
double * tempBuffer ;
mwSize dims[2] ;
dims[0] = svm->dimension ;
dims[1] = 1 ;
out[OUT_MODEL] = mxCreateNumericArray(2, dims,
mxDOUBLE_CLASS, mxREAL) ;
tempBuffer = (double*) mxGetData(out[OUT_MODEL]) ;
memcpy(tempBuffer,svm->model,svm->dimension * sizeof(double)) ;
}
if (nout >= 2) {
double * tempBuffer ;
mwSize dims[2] ;
dims[0] = 1 ;
dims[1] = 1 ;
out[OUT_BIAS] = mxCreateNumericArray(2, dims,
mxDOUBLE_CLASS, mxREAL) ;
tempBuffer = (double*) mxGetData(out[OUT_BIAS]) ;
*tempBuffer = svm->bias ;
}
if (nout == 3) {
out[OUT_INFO] = createInfoStruct(svm) ;
}
if (homkermap) {
vl_homogeneouskernelmap_delete(map);
}
vl_svmdataset_delete(dataset) ;
vl_svmpegasos_delete(svm,freeModel) ;
vl_free(disp) ;
}
/** @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 = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
char buf [1024] ;
switch (opt) {
case opt_verbose :
++ verb ;
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() ;
}
}
/* -----------------------------------------------------------------
* 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] ++ ;
}
}
