int main(int argc, char** argv)
{
init_shogun(&print_message);
// create some data
float64_t* matrix = new float64_t[6];
for (int32_t i=0; i<6; i++)
matrix[i]=i;
// create three 2-dimensional vectors
// shogun will now own the matrix created
CSimpleFeatures<float64_t>* features= new CSimpleFeatures<float64_t>();
features->set_feature_matrix(matrix, 2, 3);
// create three labels
CLabels* labels=new CLabels(3);
labels->set_label(0, -1);
labels->set_label(1, +1);
labels->set_label(2, -1);
// 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
CLibSVM* svm = new CLibSVM(10, kernel, labels);
svm->train();
// classify on training examples
for (int32_t i=0; i<3; i++)
SG_SPRINT("output[%d]=%f\n", i, svm->classify_example(i));
// free up memory
SG_UNREF(svm);
exit_shogun();
return 0;
}
int main()
{
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;
init_shogun();
gen_rand_data();
// create train labels
CLabels* labels=new CLabels(SGVector<float64_t>(lab, NUM));
SG_REF(labels);
// create train features
CSimpleFeatures<float64_t>* features = new CSimpleFeatures<float64_t>(feature_cache);
SG_REF(features);
features->set_feature_matrix(feat, DIMS, NUM);
// 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();
printf("num_sv:%d b:%f\n", svm->get_num_support_vectors(), svm->get_bias());
// classify + display output
CLabels* out_labels=svm->apply();
for (int32_t i=0; i<NUM; i++)
printf("out[%d]=%f\n", i, out_labels->get_label(i));
SG_UNREF(labels);
SG_UNREF(out_labels);
SG_UNREF(kernel);
SG_UNREF(features);
SG_UNREF(svm);
exit_shogun();
return 0;
}
