void simpleTest()
{
Tgs::Random::instance()->seed(0);
KernelEstimationInterpolator uut(.1);
shared_ptr<DataFrame> dfPtr(new DataFrame());
DataFrame& df = *dfPtr;
vector<string> labels;
labels.push_back("x");
labels.push_back("y");
labels.push_back("h");
vector<int> types(3, DataFrame::Numerical);
df.setFactorLabels(labels);
df.setFactorTypes(types);
// is there a cleaner way?
vector<double> d(3);
for (size_t i = 0; i < 500; i++)
{
d[0] = Random::instance()->generateUniform() * 2 - 1;
d[1] = Random::instance()->generateUniform() * 2 - 1;
d[2] = Normal::normal(d[0], .5);
df.addDataVector("", d);
}
uut.setData(dfPtr);
vector<string> ind;
ind.push_back("x");
//ind.push_back("y");
uut.setIndependentColumns(ind);
vector<string> dep;
dep.push_back("h");
uut.setDependentColumns(dep);
uut.setSigma(0.002);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.3247, uut.estimateError(), 0.0001);
uut.setSigma(0.008);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0721615, uut.estimateError(), 0.00001);
}
shared_ptr<DelaunayInterpolator> buildRandom()
{
shared_ptr<DelaunayInterpolator> result(new DelaunayInterpolator);
DelaunayInterpolator& uut = *result;
shared_ptr<DataFrame> dfPtr(new DataFrame());
DataFrame& df = *dfPtr;
vector<string> labels;
labels.push_back("x");
labels.push_back("y");
labels.push_back("h");
vector<int> types(3, DataFrame::Numerical);
df.setFactorLabels(labels);
df.setFactorTypes(types);
// is there a cleaner way?
vector<double> d(3);
double n0 = Normal::normal(0, 0.5);
for (size_t i = 0; i < 100; i++)
{
d[0] = Random::generateUniform() * 2 - 1;
d[1] = Random::generateUniform() * 2 - 1;
double e = sqrt(d[0] * d[0] + d[1] * d[1]);
d[2] = Normal::normal(e, .5) / n0;
df.addDataVector("", d);
}
uut.setData(dfPtr);
vector<string> ind;
ind.push_back("x");
ind.push_back("y");
uut.setIndependentColumns(ind);
vector<string> dep;
dep.push_back("h");
uut.setDependentColumns(dep);
return result;
}
void simpleTest()
{
srand(0);
DelaunayInterpolator uut;
shared_ptr<DataFrame> dfPtr(new DataFrame());
DataFrame& df = *dfPtr;
vector<string> labels;
labels.push_back("x");
labels.push_back("y");
labels.push_back("h");
vector<int> types(3, DataFrame::Numerical);
df.setFactorLabels(labels);
df.setFactorTypes(types);
// is there a cleaner way?
vector<double> d(3);
for (size_t i = 0; i < 500; i++)
{
d[0] = Random::generateUniform() * 2 - 1;
d[1] = Random::generateUniform() * 2 - 1;
double e = sqrt(d[0] * d[0] + d[1] * d[1]);
d[2] = Normal::normal(e, .5);
df.addDataVector("", d);
}
uut.setData(dfPtr);
vector<string> ind;
ind.push_back("x");
ind.push_back("y");
uut.setIndependentColumns(ind);
vector<string> dep;
dep.push_back("h");
uut.setDependentColumns(dep);
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.0028, uut.estimateError(), 0.0001);
}
