bool fImgSvm::kmeans(SGMatrix<float64_t> &data , CDenseFeatures<float64_t>* ¢ers ,int32_t num_features)
{
init_shogun(&print_message);
int32_t num_clusters= mwordnum ;
int32_t dim_features=SIFTN;
float64_t cluster_std_dev=2.0;
/* build random cluster centers */
SGMatrix<float64_t> cluster_centers(dim_features, num_clusters);
SGVector<float64_t>::random_vector(cluster_centers.matrix, dim_features*num_clusters,
0, 20.0);
//SGMatrix<float64_t>::display_matrix(cluster_centers.matrix, cluster_centers.num_rows,
// cluster_centers.num_cols, "cluster centers");
/* create features, SG_REF to avoid deletion */
CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t> ();
features->set_feature_matrix(data);
SG_REF(features);
/* create labels for cluster centers */
CMulticlassLabels* labels=new CMulticlassLabels(num_features);
for (index_t i=0; i<num_features; ++i)
labels->set_label(i, 0);
/* create distance */
CEuclideanDistance* distance=new CEuclideanDistance(features, features);
/* create distance machine */
CKMeans* clustering=new CKMeans(num_clusters, distance);
clustering->train(features);
/* build clusters */
// CMulticlassLabels* result=CMulticlassLabels::obtain_from_generic(clustering->apply());
// for (index_t i=0; i<result->get_num_labels(); ++i)
// SG_SPRINT("cluster index of vector %i: %f\n", i, result->get_label(i));
/* print cluster centers */
centers = (CDenseFeatures<float64_t>*)distance->get_lhs();
SGMatrix<float64_t> centers_matrix=centers->get_feature_matrix();
//SG_UNREF(result);
SG_UNREF(centers);
SG_UNREF(clustering);
SG_UNREF(labels);
SG_UNREF(features);
exit_shogun();
}
void R_unload_sg(DllInfo *info)
#endif
{
#ifdef HAVE_PYTHON
CPythonInterface::run_python_exit();
#endif
#ifdef HAVE_OCTAVE
COctaveInterface::run_octave_exit();
#endif
exit_shogun();
}
void fImgSvm::test_libsvm()
{
init_shogun(&print_message);
index_t num_vec=imgvec.size();
index_t num_feat=SIFTN;
index_t num_class=2;
// create some data
SGMatrix<float64_t> matrix(num_feat, num_vec);
for(int i = 0 ; i < num_vec ; i ++ ) {
for(int j = 0 ; j < num_feat ; j ++ ) {
matrix(j,i) = imgvec[i][j];
}
}
//SGVector<float64_t>::range_fill_vector(matrix.matrix, num_feat*num_vec);
// create vectors
// shogun will now own the matrix created
CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(matrix);
// create three labels
CMulticlassLabels* labels=new CMulticlassLabels(num_vec);
for (index_t i=0; i<num_vec; ++i)
labels->set_label(i,imgtrainlabelvec[i]);
// create gaussian kernel with cache 10MB, width 0.5
CGaussianKernel* kernel = new CGaussianKernel(10, 0.5);
kernel->init(features, features);
// create libsvm with C=10 and train
CMulticlassLibSVM* svm = new CMulticlassLibSVM(10, kernel, labels);
svm->train();
// classify on training examples
CMulticlassLabels* output=CMulticlassLabels::obtain_from_generic(svm->apply());
SGVector<float64_t>::display_vector(output->get_labels().vector, output->get_num_labels(),
"初始的 output");
/* assert that batch apply and apply(index_t) give same result */
for (index_t i=0; i<output->get_num_labels(); ++i) {
float64_t label=svm->apply_one(i);
SG_SPRINT("result output[%d]=%f\n", i, label);
ASSERT(output->get_label(i)==label);
}
SG_UNREF(output);
// free up memory
SG_UNREF(svm);
exit_shogun();
}
void fImgSvm::test_libsvm2()
{
init_shogun(&print_message);
const int32_t feature_cache=0;
const int32_t kernel_cache=0;
const float64_t rbf_width=10;
const float64_t svm_C=10;
const float64_t svm_eps=0.001;
int32_t num=mtrainimgsum;
int32_t dims=SIFTN;
float64_t dist=0.5;
SGVector<float64_t> lab(num); //标签
SGMatrix<float64_t> feat(dims, num);
//gen_rand_data(lab, feat, dist);
for(int i = 0 ; i < num ; i ++ ) {
for(int j = 0 ; j < dims ; j ++ ) {
feat(j,i) = imgvec[i][j];
}
}
for(int i = 0 ; i < num ; i ++ ) {
//lab[i] = imglabelvec[i]*1.0;
if(imgtrainlabelvec[i] == 1)
lab[i] = -1.0;
else
lab[i] = 1.0;
}
// create train labels
CLabels* labels=new CBinaryLabels(lab);
// create train features
CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(feature_cache);
SG_REF(features);
features->set_feature_matrix(feat);
// create gaussian kernel
CGaussianKernel* kernel=new CGaussianKernel(kernel_cache, rbf_width);
SG_REF(kernel);
kernel->init(features, features);
// create svm via libsvm and train
CLibSVM* svm=new CLibSVM(svm_C, kernel, labels);
SG_REF(svm);
svm->set_epsilon(svm_eps);
svm->train();
SG_SPRINT("num_sv:%d b:%f\n", svm->get_num_support_vectors(),
svm->get_bias());
// classify + display output
CBinaryLabels* out_labels=CBinaryLabels::obtain_from_generic(svm->apply());
for (int32_t i=0; i<num; i++) {
SG_SPRINT("out[%d]=%f (%f)\n", i, out_labels->get_label(i),
out_labels->get_confidence(i));
}
CBinaryLabels* result = CBinaryLabels::obtain_from_generic (svm->apply(features) );
for (int32_t i=0; i<3; i++)
SG_SPRINT("output[%d]=%f\n", i, result->get_label(i));
// update
// predict the
printf("----------------test -----------------\n");
getTestImg(imgtestvec);
int32_t testnum = mtestingsum;
SGMatrix<float64_t> testfeat(dims, testnum);
for(int i = 0 ; i < testnum ; i ++ ) {
for(int j = 0 ; j < dims ; j ++ ) {
testfeat(j,i) = imgtestvec[i][j];
}
}
CDenseFeatures<float64_t>* testfeatures=new CDenseFeatures<float64_t>(feature_cache);
SG_REF(testfeatures);
testfeatures->set_feature_matrix(testfeat);
CBinaryLabels* testresult = CBinaryLabels::obtain_from_generic (svm->apply(testfeatures) );
int32_t rightnum1 = 0;
int32_t rightsum1 = 0;
int32_t rightnum2 = 0;
for (int32_t i=0; i<testnum; i++) {
SG_SPRINT("output[%d]=%f\n", i, testresult->get_label(i));
if(imgtestlabelvec[i] == 1 ) {
if( (testresult->get_label(i)) < 0.0) {
rightnum1 ++;
}
rightsum1 ++ ;
} else
if(imgtestlabelvec[i] == 2 && testresult->get_label(i) > 0.0) {
rightnum2 ++ ;
}
}
printf(" %lf\n ",(rightnum1+rightnum2)*1.0 / testnum);
printf("class 1 : %lf\n",rightnum1 *1.0 / rightsum1);
printf("class 2 : %lf\n",rightnum2 *1.0 / (testnum - rightsum1));
SG_UNREF(out_labels);
SG_UNREF(kernel);
SG_UNREF(features);
SG_UNREF(svm);
exit_shogun();
}