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// -*- Mode: weights_++; tab-width: 2; -*-
// vi: set ts=2:
//
//
#include <BALL/QSAR/kplsModel.h>
#include <BALL/QSAR/mlrModel.h>
#include <Eigen/Dense>
namespace BALL
{
namespace QSAR
{
KPLSModel::KPLSModel(const QSARData& q, int k_type, double p1, double p2) : KernelModel(q, k_type, p1, p2)
{
no_components_ = 1;
type_="KPLS";
}
KPLSModel::KPLSModel(const QSARData& q, Eigen::VectorXd& w) : KernelModel(q, w)
{
no_components_ = 1;
type_="KPLS";
}
KPLSModel::KPLSModel(const QSARData& q, String s1, String s2) : KernelModel(q, s1, s2)
{
no_components_ = 1;
type_="KPLS";
}
KPLSModel::KPLSModel(const QSARData& q, const LinearModel& lm, int column) : KernelModel(q, lm, column)
{
no_components_ = 1;
type_="KPLS";
}
KPLSModel::~KPLSModel()
{
}
int KPLSModel::getNoComponents()
{
return no_components_;
}
void KPLSModel::setNoComponents(int d)
{
no_components_ = d;
}
void KPLSModel::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());
}
no_components_ = (int) v[0];
}
vector<double> KPLSModel::getParameters() const
{
vector<double> d;
d.push_back(no_components_);
return d;
}
void KPLSModel::train()
{
if (descriptor_matrix_.cols() == 0)
{
throw Exception::InconsistentUsage(__FILE__, __LINE__, "Data must be read into the model before training!");
}
// if (descriptor_matrix_.cols() < no_components_)
// {
// throw Exception::TooManyPLSComponents(__FILE__, __LINE__, no_components_, descriptor_matrix_.cols());
// }
kernel->calculateKernelMatrix(descriptor_matrix_, K_);
Eigen::MatrixXd P; // Matrix P saves all vectors p
int cols = K_.cols();
// determine the number of components that are to be created.
// no_components_ contains the number of components desired by the user,
// but obviously we cannot calculate more PLS components than features
int features = descriptor_matrix_.cols();
unsigned int components_to_create = no_components_;
if (features < no_components_) components_to_create = features;
U_.resize(K_.rows(), components_to_create);
loadings_.resize(cols, components_to_create);
weights_.resize(Y_.cols(), components_to_create);
latent_variables_.resize(K_.rows(), components_to_create);
P.resize(cols, components_to_create);
Eigen::VectorXd w;
Eigen::VectorXd t;
Eigen::VectorXd c;
Eigen::VectorXd u = Y_.col(0);
Eigen::VectorXd u_old;
for (unsigned int j = 0; j < components_to_create; j++)
{
for (int i = 0; i < 10000 ; i++)
{
w = K_.transpose()*u / Statistics::scalarProduct(u);
w = w / Statistics::euclNorm(w);
t = K_*w;
c = Y_.transpose()*t / Statistics::scalarProduct(t);
u_old = u;
u = Y_*c / Statistics::scalarProduct(c);
if (Statistics::euclDistance(u, u_old)/Statistics::euclNorm(u) > 0.0000001)
{
continue;
}
else // if u has converged
{
break;
}
}
Eigen::VectorXd p = K_.transpose() * t / Statistics::scalarProduct(t);
K_ -= t * p.transpose();
U_.col(j) = u;
loadings_.col(j) = w;
weights_.col(j) = c;
P.col(j) = p;
latent_variables_.col(j) = t;
}
// try // p's are not orthogonal to each other, so that in rare cases P.t()*loadings_ is not invertible
// {
// loadings_ = loadings_*(P.t()*loadings_).i();
// }
// catch(BALL::Exception::MatrixIsSingular e)
// {
// Eigen::MatrixXd I; I.setToIdentity(P.cols());
// I *= 0.001;
// loadings_ = loadings_*(P.t()*loadings_+I).i();
// }
Eigen::MatrixXd m = P.transpose()*loadings_;
training_result_ = loadings_*m.colPivHouseholderQr().solve(weights_.transpose());
calculateOffsets();
}
bool KPLSModel::optimizeParameters(int k, int no_steps)
{
double best_q2 = 0;
int best_no = 1;
for (int i = 1; i <= no_steps && i <= (int)data->getNoDescriptors() && (descriptor_IDs_.empty() || i < (int)descriptor_IDs_.size()); i++)
{
no_components_ = i;
validation->crossValidation(k);
if (validation->getQ2() > best_q2)
{
best_q2 = validation->getQ2();
best_no = i;
}
else if (validation->getQ2() < 0.75*best_q2)
{
break;
}
}
no_components_ = best_no;
validation->setQ2(best_q2);
return 1;
}
const Eigen::MatrixXd* KPLSModel::getU()
{
return &U_;
}
}
}
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