void test_conlitron_learner_3d()
{
C2A_Model* P = NULL;
C2A_Model* Q = NULL;
readOffFile(P, "../data/cup.off");
readOffFile(Q, "../data/spoon.off");
P->ComputeRadius();
Q->ComputeRadius();
ContactSpaceR3 contactspace(P, Q, 0.05 * (P->radius + Q->radius));
std::vector<ContactSpaceSampleData> contactspace_samples = contactspace.uniform_sample(10000);
std::ofstream out("space_test_3d.txt");
asciiWriter(out, contactspace_samples);
DataVector w(3);
w[0] = 1; w[1] = 1; w[2] = 1;
MulticonlitronLearner learner(w, 0.01);
///////////////////////////////////////
learner.use_approximate_dist = false;
///////////////////////////////////////
tools::Profiler::Begin("learn without approximate knn");
learner.learn(contactspace_samples, 3);
tools::Profiler::End("learn without approximate knn");
std::cout << learner.model.numOfHyperPlanes() << std::endl;
std::cout << contactspace_samples.size() << ": " << empiricalErrorRatio(contactspace_samples, learner) << " " << errorRatioOnGrid(contactspace, learner, 50) << std::endl;
delete P;
delete Q;
}
void test_svm_learner_3d_rotation()
{
{
C2A_Model* P = NULL;
C2A_Model* Q = NULL;
readOffFile(P, "../data/cup.off");
readOffFile(Q, "../data/spoon.off");
P->ComputeRadius();
Q->ComputeRadius();
tools::Profiler::Begin("learner");
ContactSpaceSE3Euler contactspace(P, Q, 0.05 * (P->radius + Q->radius));
std::vector<ContactSpaceSampleData> contactspace_samples = contactspace.uniform_sample(10000);
tools::Profiler::End("learner");
std::ofstream out("space_test_3d_rotation.txt");
asciiWriter(out, contactspace_samples);
SVMLearner learner;
learner.setC(50);
learner.setProbability(true);
learner.setScaler(contactspace.getScaler());
learner.setUseScaler(true);
learner.setGamma(50);
std::ofstream scaler_file("scaler_3d_rotation.txt");
scaler_file << contactspace.getScaler() << std::endl;
tools::Profiler::Begin("learner");
learner.learn(contactspace_samples, contactspace.active_data_dim());
tools::Profiler::End("learner");
learner.save("model_3d_rotation.txt");
std::cout << "model saved" << std::endl;
std::vector<ContactSpaceSampleData> test_samples = contactspace.uniform_sample(10000);
std::cout << contactspace_samples.size() << ": " << empiricalErrorRatio(contactspace_samples, learner) << " " << empiricalErrorRatio(test_samples, learner) << std::endl;
//std::cout << contactspace_samples.size() << ": " << empiricalErrorRatio(contactspace_samples, learner) << " " << errorRatioOnGrid(contactspace, learner, 5) << " ";
//for(std::size_t i = 0; i < contactspace_samples.size(); ++i)
// std::cout << "(" << results[i].label << "," << contactspace_samples[i].col << ")";
//std::cout << std::endl;
delete P;
delete Q;
}
}
void Mesh::read(std::istream& is, const char* fileExtension) {
bool loaded=false;
if(!strcmp(fileExtension, "obj")) { readObjFile(is); loaded=true; }
if(!strcmp(fileExtension, "off")) { readOffFile(is); loaded=true; }
if(!strcmp(fileExtension, "ply")) { readPlyFile(is); loaded=true; }
if(!strcmp(fileExtension, "tri")) { readTriFile(is); loaded=true; }
if(!strcmp(fileExtension, "stl")) { readStlFile(is); loaded=true; }
if(!loaded) HALT("can't read fileExtension '" <<fileExtension <<"'");
}
void test_conlitron_learner_3d_rotation_approximate_knn()
{
C2A_Model* P = NULL;
C2A_Model* Q = NULL;
readOffFile(P, "../data/cup.off");
readOffFile(Q, "../data/spoon.off");
P->ComputeRadius();
Q->ComputeRadius();
ContactSpaceSE3Euler2 contactspace(P, Q, 0.05 * (P->radius + Q->radius));
std::vector<ContactSpaceSampleData> contactspace_samples = contactspace.uniform_sample(10000);
std::ofstream out("space_test_3d_rotation.txt");
asciiWriter(out, contactspace_samples);
DataVector w(6);
w[0] = 1; w[1] = 1; w[2] = 1; w[3] = 1; w[4] = 1; w[5] = 1;
MulticonlitronLearner learner(w, 0.01);
///////////////////////////////////////
learner.use_approximate_dist = true;
///////////////////////////////////////
tools::Profiler::Begin("learn with approximate knn");
learner.learn(contactspace_samples, 6);
tools::Profiler::End("learn with approximate knn");
std::cout << learner.model.numOfHyperPlanes() << std::endl;
std::cout << contactspace_samples.size() << ": " << empiricalErrorRatio(contactspace_samples, learner) << " " << errorRatioOnGrid(contactspace, learner, 5) << std::endl;
MulticonlitronLearner learner2(w, 0.01, 1, 1);
///////////////////////////////////////
learner2.use_approximate_dist = true;
///////////////////////////////////////
tools::Profiler::Begin("learn with approximate knn");
learner2.learn(contactspace_samples, 6);
tools::Profiler::End("learn with approximate knn");
std::cout << learner2.model.numOfHyperPlanes() << std::endl;
std::cout << contactspace_samples.size() << ": " << empiricalErrorRatio(contactspace_samples, learner2) << " " << errorRatioOnGrid(contactspace, learner2, 5) << std::endl;
MulticonlitronLearner learner3(w, 0.01, 5, 5);
///////////////////////////////////////
learner3.use_approximate_dist = true;
///////////////////////////////////////
tools::Profiler::Begin("learn with approximate knn");
learner3.learn(contactspace_samples, 6);
tools::Profiler::End("learn with approximate knn");
std::cout << learner3.model.numOfHyperPlanes() << std::endl;
std::cout << contactspace_samples.size() << ": " << empiricalErrorRatio(contactspace_samples, learner3) << " " << errorRatioOnGrid(contactspace, learner3, 5) << std::endl;
MulticonlitronLearner learner4(w, 0.01, 10, 10);
///////////////////////////////////////
learner4.use_approximate_dist = true;
///////////////////////////////////////
tools::Profiler::Begin("learn with approximate knn");
learner4.learn(contactspace_samples, 6);
tools::Profiler::End("learn with approximate knn");
std::cout << learner4.model.numOfHyperPlanes() << std::endl;
std::cout << contactspace_samples.size() << ": " << empiricalErrorRatio(contactspace_samples, learner4) << " " << errorRatioOnGrid(contactspace, learner4, 5) << std::endl;
delete P;
delete Q;
}
