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) ;
}
/* driver */
void
mexFunction (int nout VL_UNUSED, mxArray * out[], int nin, const mxArray * in[])
{
enum {IN_DATA = 0, IN_MEANS, IN_COVARIANCES, IN_PRIORS, IN_END} ;
enum {OUT_ENC} ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
vl_size numClusters = 10;
vl_size dimension ;
vl_size numData ;
int flags = 0 ;
void * covariances = NULL;
void * means = NULL;
void * priors = NULL;
void * data = NULL ;
int verbosity = 0 ;
vl_type dataType ;
mxClassID classID ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 4) {
vlmxError (vlmxErrInvalidArgument,
"At least four arguments required.");
}
if (nout > 1) {
vlmxError (vlmxErrInvalidArgument,
"At most one output argument.");
}
classID = mxGetClassID (IN(DATA)) ;
switch (classID) {
case mxSINGLE_CLASS: dataType = VL_TYPE_FLOAT ; break ;
case mxDOUBLE_CLASS: dataType = VL_TYPE_DOUBLE ; break ;
default:
vlmxError (vlmxErrInvalidArgument,
"DATA is neither of class SINGLE or DOUBLE.") ;
}
if (mxGetClassID (IN(MEANS)) != classID) {
vlmxError(vlmxErrInvalidArgument, "MEANS is not of the same class as DATA.") ;
}
if (mxGetClassID (IN(COVARIANCES)) != classID) {
vlmxError(vlmxErrInvalidArgument, "COVARIANCES is not of the same class as DATA.") ;
}
if (mxGetClassID (IN(PRIORS)) != classID) {
vlmxError(vlmxErrInvalidArgument, "PRIORS is not of the same class as DATA.") ;
}
dimension = mxGetM (IN(DATA)) ;
numData = mxGetN (IN(DATA)) ;
numClusters = mxGetN (IN(MEANS)) ;
if (dimension == 0) {
vlmxError (vlmxErrInvalidArgument, "SIZE(DATA,1) is zero.") ;
}
if (!vlmxIsMatrix(IN(MEANS), dimension, numClusters)) {
vlmxError (vlmxErrInvalidArgument, "MEANS is not a matrix or does not have the right size.") ;
}
if (!vlmxIsMatrix(IN(COVARIANCES), dimension, numClusters)) {
vlmxError (vlmxErrInvalidArgument, "COVARIANCES is not a matrix or does not have the right size.") ;
}
if (!vlmxIsVector(IN(PRIORS), numClusters)) {
vlmxError (vlmxErrInvalidArgument, "PRIORS is not a vector or does not have the right size.") ;
}
if (!vlmxIsMatrix(IN(DATA), dimension, numData)) {
vlmxError (vlmxErrInvalidArgument, "DATA is not a matrix or does not have the right size.") ;
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_verbose : ++ verbosity ; break ;
case opt_normalized: flags |= VL_FISHER_FLAG_NORMALIZED ; break ;
case opt_square_root: flags |= VL_FISHER_FLAG_SQUARE_ROOT ; break ;
case opt_improved: flags |= VL_FISHER_FLAG_IMPROVED ; break ;
case opt_fast: flags |= VL_FISHER_FLAG_FAST ; break ;
default : abort() ;
}
}
/* -----------------------------------------------------------------
* Do the job
* -------------------------------------------------------------- */
data = mxGetPr(IN(DATA)) ;
means = mxGetPr(IN(MEANS)) ;
covariances = mxGetPr(IN(COVARIANCES)) ;
priors = mxGetPr(IN(PRIORS)) ;
if (verbosity) {
mexPrintf("vl_fisher: num data: %d\n", numData) ;
mexPrintf("vl_fisher: num clusters: %d\n", numClusters) ;
mexPrintf("vl_fisher: data dimension: %d\n", dimension) ;
mexPrintf("vl_fisher: code dimension: %d\n", numClusters * dimension) ;
mexPrintf("vl_fisher: normalized: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_NORMALIZED)) ;
mexPrintf("vl_fisher: square root: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_SQUARE_ROOT)) ;
mexPrintf("vl_fisher: normalized: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_NORMALIZED)) ;
mexPrintf("vl_fisher: fast: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_FAST)) ;
}
/* -------------------------------------------------------------- */
/* Encoding */
/* -------------------------------------------------------------- */
OUT(ENC) = mxCreateNumericMatrix (dimension * numClusters * 2, 1, classID, mxREAL) ;
vl_fisher_encode (mxGetData(OUT(ENC)), dataType,
means, dimension, numClusters,
covariances,
priors,
data, numData,
flags) ;
}
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) ;
}