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// -*- Mode: C++; tab-width: 2; -*-
// vi: set ts=2:
//
//
#include <BALL/QSAR/gpModel.h>
#include <Eigen/Dense>
namespace BALL
{
namespace QSAR
{
GPModel::GPModel(const QSARData& q, int k_type, double p1, double p2) : KernelModel(q, k_type, p1, p2)
{
type_="GP";
lambda_ = 0.001;
}
GPModel::GPModel(const QSARData& q, Eigen::VectorXd& w) : KernelModel(q, w)
{
type_="GP";
lambda_ = 0.001;
}
GPModel::GPModel(const QSARData& q, String s1, String s2) : KernelModel(q, s1, s2)
{
type_="GP";
lambda_ = 0.001;
}
GPModel::GPModel(const QSARData& q, const LinearModel& lm, int column) : KernelModel(q, lm, column)
{
type_="GP";
lambda_ = 0.001;
}
GPModel::~GPModel()
{
}
void GPModel::train()
{
if (descriptor_matrix_.cols() == 0)
{
throw Exception::InconsistentUsage(__FILE__, __LINE__, "Data must be read into the model before training!");
}
kernel->calculateKernelMatrix(descriptor_matrix_, K_);
training_result_ = K_.colPivHouseholderQr().solve(Y_); // B: one coefficient for each substance; dim: nxc
calculateOffsets();
}
Eigen::VectorXd GPModel::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!");
}
input_ = getSubstanceVector(substance, transform);
kernel->calculateKernelVector(K_, input_, descriptor_matrix_, K_t_); // dim: 1xn
Eigen::VectorXd res = K_t_*training_result_;
//if (offsets_.getSize() == res.getSize()) res -= offsets_;
if (transform && y_transformations_.cols() != 0)
{
backTransformPrediction(res);
}
return res;
}
double GPModel::calculateStdErr()
{
Eigen::MatrixXd mx;
Eigen::MatrixXd m1(1, input_.rows());
m1.row(1) = input_;
kernel->calculateKernelMatrix(m1, mx); // k(x*, x*), dim: 1x1
double sum = 0;
for (unsigned int i = 1; i <= K_t_.cols(); i++)
{
for (unsigned int j = 1; j <= K_t_.cols(); j++)
{
sum += K_t_(i)*K_t_(j)*L_(i, j); // k(x*, _i)*k(x*, x_j)*L_ij
}
}
double res = sqrt(fabs(mx(1, 1)-sum));
return res;
}
void GPModel::setParameters(vector<double>& v)
{
if (v.size() != 1)
{
String c = "Wrong number of model parameters! Needed: 1;";
c = c+" given: "+String(v.size());
throw Exception::ModelParameterError(__FILE__, __LINE__, c.c_str());
}
lambda_ = v[0];
}
vector<double> GPModel::getParameters() const
{
vector<double> d;
d.push_back(lambda_);
return d;
}
}
}
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