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;
}
CLabels* CAUCKernel::setup_auc_maximization(CLabels* labels)
{
SG_INFO( "setting up AUC maximization\n") ;
ASSERT(labels);
ASSERT(labels->is_two_class_labeling());
// get the original labels
int32_t num=0;
ASSERT(labels);
int32_t* int_labels=labels->get_int_labels(num);
ASSERT(subkernel->get_num_vec_rhs()==num);
// count positive and negative
int32_t num_pos=0;
int32_t num_neg=0;
for (int32_t i=0; i<num; i++)
{
if (int_labels[i]==1)
num_pos++;
else
num_neg++;
}
// create AUC features and labels (alternate labels)
int32_t num_auc = num_pos*num_neg;
SG_INFO("num_pos: %i num_neg: %i num_auc: %i\n", num_pos, num_neg, num_auc);
uint16_t* features_auc = new uint16_t[num_auc*2];
int32_t* labels_auc = new int32_t[num_auc];
int32_t n=0 ;
for (int32_t i=0; i<num; i++)
{
if (int_labels[i]!=1)
continue;
for (int32_t j=0; j<num; j++)
{
if (int_labels[j]!=-1)
continue;
// create about as many positively as negatively labeled examples
if (n%2==0)
{
features_auc[n*2]=i;
features_auc[n*2+1]=j;
labels_auc[n]=1;
}
else
{
features_auc[n*2]=j;
features_auc[n*2+1]=i;
labels_auc[n]=-1;
}
n++;
ASSERT(n<=num_auc);
}
}
// create label object and attach it to svm
CLabels* lab_auc = new CLabels(num_auc);
lab_auc->set_int_labels(labels_auc, num_auc);
SG_REF(lab_auc);
// create feature object
CSimpleFeatures<uint16_t>* f = new CSimpleFeatures<uint16_t>(0);
f->set_feature_matrix(features_auc, 2, num_auc);
// create AUC kernel and attach the features
init(f,f);
delete[] int_labels;
delete[] labels_auc;
return lab_auc;
}
int main()
{
init_shogun_with_defaults();
// Generate random data, features and labels
gen_rand_data();
// Create features
CSimpleFeatures<float64_t>* features = new CSimpleFeatures<float64_t>();
SG_REF(features);
features->set_feature_matrix(feat, DIMS, NUM);
// Create a StreamingSimpleFeatures object which uses the above as input; labels (float64_t*) are optional
CStreamingSimpleFeatures<float64_t>* streaming_simple = new CStreamingSimpleFeatures<float64_t>(features, lab);
SG_REF(streaming_simple);
// Start parsing of the examples; in this case, it is trivial - returns each vector from the SimpleFeatures object
streaming_simple->start_parser();
int32_t counter=0;
SG_SPRINT("Processing examples...\n\n");
// Run a while loop over all the examples. Note that since
// features are "streaming", there is no predefined
// number_of_vectors known to the StreamingFeatures object.
// Thus, this loop must be used to iterate over all the
// features
while (streaming_simple->get_next_example())
{
counter++;
// Get the current vector; no other vector is accessible
SGVector<float64_t> vec = streaming_simple->get_vector();
float64_t label = streaming_simple->get_label();
SG_SPRINT("Vector %d: [\t", counter);
for (int32_t i=0; i<vec.vlen; i++)
{
SG_SPRINT("%f\t", vec.vector[i]);
}
SG_SPRINT("Label=%f\t", label);
// Calculate dot product of the current vector (from
// the StreamingFeatures object) with itself (the
// vector passed as argument)
float64_t dot_prod = streaming_simple->dense_dot(vec.vector, vec.vlen);
SG_SPRINT("]\nDot product of the vector with itself: %f", dot_prod);
SG_SPRINT("\n\n");
// Free the example, since we are done with processing it.
streaming_simple->release_example();
}
// Now that all examples are used, end the parser.
streaming_simple->end_parser();
SG_FREE(lab);
SG_UNREF(streaming_simple);
SG_UNREF(features);
exit_shogun();
return 0;
}
int main(int argc,char *argv[])
{
init_shogun(&print_message,&print_message,&print_message);//initialising shogun without giving arguments shogun wont be able to print
int32_t x_n=4,x_d=2;//X dimensions : x_n for no of datapoints and x_d for dimensionality of data
SGMatrix<float64_t> fmatrix(x_d,x_n);
SG_SPRINT("\nTEST 1:\n\n");
/*Initialising Feature Matrix */
for (int i=0; i<x_n*x_d; i++)
fmatrix.matrix[i] = i+1;
SG_SPRINT("FEATURE MATRIX :\n");
CMath::display_matrix(fmatrix.matrix,x_d,x_n);
CSimpleFeatures<float64_t>* features = new CSimpleFeatures<float64_t>(fmatrix);
SG_REF(features);
/*Creating random labels */
CLabels* labels=new CLabels(x_n);
// create labels, two classes
labels->set_label(0,1);
labels->set_label(1,-1);
labels->set_label(2,1);
labels->set_label(3,1);
SG_REF(labels);
/*Working with Newton SVM */
float64_t lambda=1.0;
int32_t iter=20;
CNewtonSVM *nsvm = new CNewtonSVM(lambda,features,labels,iter);
SG_REF(nsvm);
nsvm->train();
SG_UNREF(labels);
SG_UNREF(nsvm);
SG_SPRINT("TEST 2:\n\n");
x_n=5;
x_d=3;
SGMatrix<float64_t> fmatrix2(x_d,x_n);
for (int i=0; i<x_n*x_d; i++)
fmatrix2.matrix[i] = i+1;
SG_SPRINT("FEATURE MATRIX :\n");
CMath::display_matrix(fmatrix2.matrix,x_d,x_n);
features->set_feature_matrix(fmatrix2);
SG_REF(features);
/*Creating random labels */
CLabels* labels2=new CLabels(x_n);
// create labels, two classes
labels2->set_label(0,1);
labels2->set_label(1,-1);
labels2->set_label(2,1);
labels2->set_label(3,1);
labels2->set_label(4,-1);
SG_REF(labels2);
/*Working with Newton SVM */
lambda=1.0;
iter=20;
CNewtonSVM *nsvm2 = new CNewtonSVM(lambda,features,labels2,iter);
SG_REF(nsvm2);
nsvm2->train();
SG_UNREF(labels2);
SG_UNREF(nsvm2);
SG_UNREF(features);
exit_shogun();
return 0;
}
