Eigen::VectorXd FitModel::predict(const vector<double> & substance, bool transform)
{
if (training_result_.cols() == 0)
{
throw Exception::InconsistentUsage(__FILE__, __LINE__, "Model must be trained before it can predict the activitiy of substances!");
}
Eigen::VectorXd v = getSubstanceVector(substance, transform);
Eigen::VectorXd res(Y_.cols());
String var="";
// replace all x-values for the current substance
for (unsigned int j = 0; j < v.rows(); j++)
{
var = var+"x"+String(j)+"="+String(v(j))+";";
}
//calculated all activities for given substance
for (int c = 0; c < Y_.cols(); c++)
{
String coeff="";
// get optimized coefficients
for (int m = 0; m < training_result_.rows(); m++)
{
coeff = coeff+"b"+String(m)+"="+String(training_result_(m, c))+";";
}
ParsedFunction<float> f = coeff+var+allEquations_[c];
res(c) = f(0);
}
if (transform && y_transformations_.cols() != 0)
{
backTransformPrediction(res);
}
return res;
}
Eigen::VectorXd ALLModel::predict(const vector<double> & substance, bool transform)
{
if (descriptor_matrix_.cols() == 0)
{
throw Exception::InconsistentUsage(__FILE__, __LINE__, "Training data must be read into the ALL-model before the activity of a substance can be predicted!");
}
Eigen::VectorXd v0 = getSubstanceVector(substance, transform);
Eigen::MatrixXd v(1, v0.rows());
v.row(0) = v0;
Eigen::MatrixXd dist;
// calculate distances between the given substance and the substances of X
// dimension of dist: 1xn
calculateEuclDistanceMatrix(v, descriptor_matrix_, dist);
Eigen::VectorXd w;
calculateWeights(dist, w);
Eigen::MatrixXd XX;
// calculate X.t()*X as cross-products weighted by the similarity of the given substance to the respective row of X
calculateXX(w, XX);
Eigen::MatrixXd XY;
// calculate X.t()*Y_ as cross-products weighted by the similarity of the given substance to the respective row of X
calculateXY(w, XY);
// rigde regression in order to be able to cope with linearly dependent columns, i.e. singular matrices
Eigen::MatrixXd im(XX.rows(), XX.rows());
im.setIdentity();
im *= lambda_;
XX += im;
Eigen::MatrixXd train = XX.colPivHouseholderQr().solve(XY);
Eigen::VectorXd res(Y_.cols());
res = v0.transpose()*train;
if (transform && y_transformations_.cols() != 0)
{
backTransformPrediction(res);
}
return res;
}
Eigen::VectorXd LinearModel::predict(const vector<double> & substance, bool transform)
{
if (training_result_.rows() == 0)
{
throw Exception::InconsistentUsage(__FILE__, __LINE__, "Model must be trained before it can predict the activitiy of substances!");
}
Eigen::VectorXd v = getSubstanceVector(substance, transform);
Eigen::VectorXd res = v.transpose()*training_result_;
//if (offsets_.getSize() == res.getSize()) res -= offsets_;
if (transform && y_transformations_.cols() != 0)
{
backTransformPrediction(res);
}
return res;
}
