void NormalizeComponentsWhitening::train(ModelType& model, UnlabeledData<RealVector> const& input){
std::size_t dc = dataDimension(input);
SHARK_CHECK(input.numberOfElements() >= dc + 1, "[NormalizeComponentsWhitening::train] input needs to contain more points than there are input dimensions");
SHARK_CHECK(m_targetVariance > 0.0, "[NormalizeComponentsWhitening::train] target variance must be positive");
// dense model with bias having input and output dimension equal to data dimension
model.setStructure(dc, dc, true);
RealVector mean;
RealMatrix covariance;
meanvar(input, mean, covariance);
RealMatrix whiteningMatrix = createWhiteningMatrix(covariance);
whiteningMatrix *= std::sqrt(m_targetVariance);
RealVector offset = -prod(trans(whiteningMatrix),mean);
model.setStructure(RealMatrix(trans(whiteningMatrix)), offset);
}
