CSGObject *CGaussianKernel::shallow_copy() const
{
// TODO: remove this after all the classes get shallow_copy properly implemented
// this assert is to avoid any subclass of CGaussianKernel accidentally called
// with the implement here
ASSERT(typeid(*this) == typeid(CGaussianKernel))
CGaussianKernel *ker = new CGaussianKernel(cache_size, width);
if (lhs)
{
ker->init(lhs, rhs);
}
return ker;
}
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();
}
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;
}
int main(int argc, char** argv)
{
init_shogun(&print_message);
index_t num_vec=300;
index_t num_feat=20;
index_t num_class=3;
// create some data
SGMatrix<float64_t> matrix(num_feat, num_vec);
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, i%num_class);
// 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(),
"batch 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("single output[%d]=%f\n", i, label);
ASSERT(output->get_label(i)==label);
}
SG_UNREF(output);
// free up memory
SG_UNREF(svm);
exit_shogun();
return 0;
}
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(int argc, char** argv)
{
init_shogun(&print_message);
/* create some random data */
SGMatrix<float64_t> matrix(n,n);
for(int32_t i=0; i<n*n; ++i)
matrix.matrix[i]=CMath::random((float64_t)-n,(float64_t)n);
CMath::display_matrix(matrix.matrix, n, n);
/* create n n-dimensional feature vectors */
CDenseFeatures<float64_t>* features= new CDenseFeatures<float64_t>(matrix);
/* create gaussian kernel with cache 10MB, width will be changed later */
CGaussianKernel* kernel = new CGaussianKernel(10, 0);
kernel->init(features, features);
/* create n labels (+1,-1,+1,-1,...) */
CLabels* labels=new CLabels(n);
for (int32_t i=0; i<n; ++i)
labels->set_label(i, i%2==0 ? +1 : -1);
/* create libsvm with C=10 and produced labels */
CLibSVM* svm=new CLibSVM(10, kernel, labels);
/* iterate over different width parameters */
for (int32_t i=0; i<10; ++i)
{
SG_SPRINT("\n\ncurrent kernel width: 2^%d=%f\n", i, CMath::pow(2.0,i));
float64_t width=CMath::pow(2.0,i);
/* create parameter to change current kernel width */
Parameter* param=new Parameter();
param->add(&width, "width", "");
/* tell kernel to use the newly produced parameter */
kernel->m_parameters->set_from_parameters(param);
/* print kernel matrix */
for (int32_t i=0; i<n; i++)
{
for (int32_t j=0; j<n; j++)
SG_SPRINT("%f ", kernel->kernel(i,j));
SG_SPRINT("\n");
}
/* train and classify */
svm->train();
for (int32_t i=0; i<n; ++i)
SG_SPRINT("output[%d]=%f\treal[%d]=%f\n", i,
svm->apply(i), i, labels->get_label(i));
delete param;
}
/* free up memory */
SG_UNREF(svm);
exit_shogun();
return 0;
}
