void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_DATA, IN_LABELS, IN_LAMBDA, IN_END} ;
enum {OUT_MODEL = 0} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
double biasMultiplier = 0 ;
double lambda ;
void * data ;
void * preconditioner = NULL ;
vl_size preconditionerDimension = 0 ;
mxClassID dataClass ;
vl_type dataType ;
vl_size numSamples ;
vl_size dimension ;
vl_size numIterations = 0 ;
vl_uindex startingIteration = 1 ;
vl_uint32 * permutation = NULL ;
vl_size permutationSize = 0 ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 3) {
vlmxError(vlmxErrInvalidArgument,
"At least three arguments are 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.") ;
}
data = mxGetData (IN(DATA)) ;
dimension = mxGetM(IN(DATA)) ;
numSamples = mxGetN(IN(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 (! vlmxIsVector(IN(LABELS), numSamples)) {
vlmxError(vlmxErrInvalidArgument, "LABELS is not a vector of dimension compatible with DATA.") ;
}
if (! vlmxIsPlainScalar(IN(LAMBDA))) {
vlmxError(vlmxErrInvalidArgument, "LAMBDA is not a plain scalar.") ;
}
lambda = *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.") ;
}
biasMultiplier = *mxGetPr(optarg) ;
break ;
case opt_num_iterations :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "NUMITERATIONS is not a plain scalar.") ;
}
if (*mxGetPr(optarg) < 0) {
vlmxError(vlmxErrInvalidArgument, "NUMITERATIONS is negative.") ;
}
numIterations = (vl_size) *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.") ;
}
startingIteration = (vl_size) *mxGetPr(optarg) ;
break ;
case opt_starting_model :
if (!vlmxIsVector(optarg, -1) ||
mxIsComplex(optarg) ||
!mxIsNumeric(optarg)) {
vlmxError(vlmxErrInvalidArgument, "STARTINGMODEL is not a real vector.") ;
}
OUT(MODEL) = mxDuplicateArray(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) ;
{
/* adjust indexing */
vl_uint32 const * matlabPermutation = mxGetData(optarg) ;
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) ;
}
}
}
break ;
case opt_preconditioner :
if (!vlmxIsVector(optarg, -1) ||
mxIsComplex(optarg) ||
!mxIsNumeric(optarg)) {
vlmxError(vlmxErrInvalidArgument, "PRECONDITIONER is not a real vector.") ;
}
if (mxGetClassID(optarg) != dataClass) {
vlmxError(vlmxErrInvalidArgument, "PRECODNITIONER storage class does not match the data.") ;
}
preconditioner = mxGetData(optarg) ;
preconditionerDimension = mxGetNumberOfElements(optarg) ;
break ;
case opt_verbose :
++ verbose ;
break ;
}
}
if (preconditioner && preconditionerDimension != (dimension + (biasMultiplier > 0))) {
vlmxError(vlmxErrInvalidArgument, "PRECONDITIONER has incompatible dimension.") ;
}
if (numIterations == 0) {
numIterations = (vl_size) 10 / (lambda + 1) ;
}
if (! OUT(MODEL)) {
OUT(MODEL) = mxCreateNumericMatrix(dimension + (biasMultiplier > 0),
1,
dataClass, mxREAL) ;
} else {
if (mxGetClassID(OUT(MODEL)) != dataClass) {
vlmxError(vlmxErrInvalidArgument, "STARTINGMODEL is not of the same class of DATA.") ;
}
if (mxGetNumberOfElements(OUT(MODEL)) != dimension + (biasMultiplier > 0)) {
vlmxError(vlmxErrInvalidArgument, "STARTINGMODEL has incompatible dimension.") ;
}
}
if (verbose) {
mexPrintf("vl_pegasos: Lambda = %g\n", lambda) ;
mexPrintf("vl_pegasos: BiasMultiplier = %g\n", biasMultiplier) ;
mexPrintf("vl_pegasos: NumIterations = %d\n", numIterations) ;
mexPrintf("vl_pegasos: permutation size = %d\n", permutationSize) ;
mexPrintf("vl_pegasos: using preconditioner = %s\n", VL_YESNO(preconditioner)) ;
}
switch (dataType) {
case VL_TYPE_FLOAT :
vl_pegasos_train_binary_svm_f((float *)mxGetData(OUT(MODEL)),
(float const *)mxGetPr(IN(DATA)), dimension, numSamples,
(vl_int8 const *)mxGetData(IN(LABELS)),
lambda,
biasMultiplier,
startingIteration,
numIterations,
NULL,
permutation,
permutationSize,
(float const*)preconditioner) ;
break ;
case VL_TYPE_DOUBLE:
vl_pegasos_train_binary_svm_d((double *)mxGetData(OUT(MODEL)),
(double const *)mxGetData(IN(DATA)), dimension, numSamples,
(vl_int8 const *)mxGetData(IN(LABELS)),
lambda,
biasMultiplier,
startingIteration,
numIterations,
NULL,
permutation,
permutationSize,
(double const *)preconditioner) ;
break ;
}
if (permutation) vl_free(permutation) ;
}
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
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) ;
}
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) ;
}
/* driver */
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
typedef int unsigned data_t ;
vl_bool autoComparison = VL_TRUE ;
VlVectorComparisonType comparisonType = VlDistanceL2 ;
enum {IN_X = 0, IN_Y} ;
enum {OUT_D = 0} ;
mwSize numDataX = 0 ;
mwSize numDataY = 0 ;
mwSize dimension ;
mxClassID classId ;
/* for option parsing */
int opt ;
int next ;
mxArray const *optarg ;
VL_USE_MATLAB_ENV ;
if (nout > 1) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
if (nin < 1) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
}
if (! (vlmxIsMatrix (in[IN_X],-1,-1) && vlmxIsReal(in[IN_X]))) {
vlmxError(vlmxErrInvalidArgument, "X must be a real matrix.") ;
}
next = 1 ;
classId = mxGetClassID(in[IN_X]) ;
dimension = mxGetM(in[IN_X]) ;
numDataX = mxGetN(in[IN_X]) ;
if (nin > 1 && vlmxIsMatrix (in[IN_Y],-1,-1) && vlmxIsReal(in[IN_Y])) {
next = 2 ;
autoComparison = VL_FALSE ;
numDataY = mxGetN(in[IN_Y]) ;
if (mxGetClassID(in[IN_Y]) != classId) {
vlmxError(vlmxErrInvalidArgument, "X and Y must have the same class.") ;
}
if (dimension != mxGetM(in[IN_Y])) {
vlmxError(vlmxErrInvalidArgument, "X and Y must have the same number of rows.") ;
}
}
if (classId != mxSINGLE_CLASS && classId != mxDOUBLE_CLASS) {
vlmxError(vlmxErrInvalidArgument,
"X must be either of class SINGLE or DOUBLE.");
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_L2 : comparisonType = VlDistanceL2 ; break ;
case opt_L1 : comparisonType = VlDistanceL1 ; break ;
case opt_CHI2 : comparisonType = VlDistanceChi2 ; break ;
case opt_HELL : comparisonType = VlDistanceHellinger ; break ;
case opt_JS : comparisonType = VlDistanceJS ; break ;
case opt_KL2 : comparisonType = VlKernelL2 ; break ;
case opt_KL1 : comparisonType = VlKernelL1 ; break ;
case opt_KCHI2 : comparisonType = VlKernelChi2 ; break ;
case opt_KHELL : comparisonType = VlKernelHellinger ; break ;
case opt_KJS : comparisonType = VlKernelJS ; break ;
default:
abort() ;
}
}
/* allocate output */
{
mwSize dims [2] ;
dims[0] = numDataX ;
dims[1] = autoComparison ? numDataX : numDataY ;
out[OUT_D] = mxCreateNumericArray (2, dims, classId, mxREAL) ;
}
/* If either numDataX or numDataY are null, their data pointers are
null as well. This may confuse
vl_eval_vector_comparison_on_all_pairs_*, so we intercept this as
a special case. The same is true if dimension is null.
*/
if (numDataX == 0 || (! autoComparison && numDataY == 0)) {
return ;
}
if (dimension == 0) {
return ;
}
/* make calculation */
switch (classId) {
case mxSINGLE_CLASS:
{
VlFloatVectorComparisonFunction f = vl_get_vector_comparison_function_f (comparisonType) ;
if (autoComparison) {
vl_eval_vector_comparison_on_all_pairs_f ((float*)mxGetData(out[OUT_D]),
dimension,
(float*)mxGetData(in[IN_X]), numDataX,
0, 0,
f) ;
} else {
vl_eval_vector_comparison_on_all_pairs_f ((float*)mxGetData(out[OUT_D]),
dimension,
(float*)mxGetData(in[IN_X]), numDataX,
(float*)mxGetData(in[IN_Y]), numDataY,
f) ;
}
}
break ;
case mxDOUBLE_CLASS:
{
VlDoubleVectorComparisonFunction f = vl_get_vector_comparison_function_d (comparisonType) ;
if (autoComparison) {
vl_eval_vector_comparison_on_all_pairs_d ((double*)mxGetData(out[OUT_D]),
dimension,
(double*)mxGetData(in[IN_X]), numDataX,
0, 0,
f) ;
} else {
vl_eval_vector_comparison_on_all_pairs_d ((double*)mxGetData(out[OUT_D]),
dimension,
(double*)mxGetData(in[IN_X]), numDataX,
(double*)mxGetData(in[IN_Y]), numDataY,
f) ;
}
}
break ;
default:
abort() ;
}
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
float * image ;
vl_size width, height ;
vl_size cellSize = 16 ;
enum {IN_I = 0, IN_CELLSIZE} ;
enum {OUT_FEATURES = 0} ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin > 2) {
vlmxError(vlmxErrTooManyInputArguments, NULL) ;
}
if (nin < 2) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
}
if (nout > 1) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
if (! mxIsNumeric(IN(I)) ||
! vlmxIsReal(IN(I)) ||
! vlmxIsMatrix(IN(I), -1, -1)) {
vlmxError(vlmxErrInvalidArgument,
"I is not a numeric matrix.") ;
}
if (mxGetClassID(IN(I)) != mxSINGLE_CLASS) {
vlmxError(vlmxErrInvalidArgument,
"I is not of class SINGLE.") ;
}
if (! vlmxIsPlainScalar(IN(CELLSIZE))) {
vlmxError(vlmxErrInvalidArgument,
"CELLSIZE is not a plain scalar.") ;
}
if (mxGetScalar(IN(CELLSIZE)) < 1.0) {
vlmxError(vlmxErrInvalidArgument,
"CELLSIZE is less than 1.") ;
}
cellSize = (vl_size) mxGetScalar(IN(CELLSIZE)) ;
image = mxGetData(IN(I)) ;
width = mxGetN(IN(I)) ;
height = mxGetM(IN(I)) ;
/* do job */
{
/* recall that MATLAB images are transposed */
mwSize dimensions [3] ;
/* get LBP object */
VlLbp * lbp = vl_lbp_new (VlLbpUniform, VL_TRUE) ;
if (lbp == NULL) {
vlmxError(vlmxErrAlloc, NULL) ;
}
/* get output buffer */
dimensions[0] = height / cellSize ;
dimensions[1] = width / cellSize ;
dimensions[2] = vl_lbp_get_dimension(lbp) ;
OUT(FEATURES) = mxCreateNumericArray(3, dimensions, mxSINGLE_CLASS, mxREAL) ;
vl_lbp_process(lbp, mxGetData(OUT(FEATURES)), image, height, width, cellSize) ;
vl_lbp_delete(lbp) ;
}
}
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)) ;
}
}