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#include <BALL/QSAR/knnModel.h>
using namespace std;
namespace BALL
{
namespace QSAR
{
// ALLModel::kw_ will not be used so it can be set to an arbitrary value
KNNModel::KNNModel(const QSARData& q, int k)
: ALLModel(q, 1)
{
type_ = "KNN";
k_ = k;
default_no_opt_steps_ = 20;
}
void KNNModel::calculateWeights(Eigen::MatrixXd& dist, Eigen::VectorXd& w)
{
// set first k entries of similarity vector w to 1
// and all other entries to 0
// == > KNN instead of ALL
w.resize(dist.cols());
w.setConstant(1.0);
std::multiset<pair<double, int> > ranking;
for (int i = 0; i < dist.cols(); i++)
{
ranking.insert(make_pair(dist(0, i), i));
}
std::multiset<pair<double, int> >::iterator r_it = ranking.begin();
for (int i = 0; i < k_ && r_it != ranking.end(); i++, ++r_it)
{ } // skip the k nearest neighbors
for (; r_it != ranking.end(); ++r_it)
{
w(r_it->second) = 0;
}
}
void KNNModel::setParameters(vector<double>& v)
{
if (v.size() != 2)
{
String c = "Wrong number of model parameters! Needed: 2;";
c = c+" given: "+String(v.size());
throw Exception::ModelParameterError(__FILE__, __LINE__, c.c_str());
}
k_ = (int) v[0];
lambda_ = v[1];
}
vector<double> KNNModel::getParameters() const
{
vector<double> d;
d.push_back(k_);
d.push_back(lambda_);
return d;
}
bool KNNModel::optimizeParameters(int d, int no_steps)
{
double best_q2 = 0;
int best_no = 1;
for (int i = 1; i <= no_steps && i <= (int)data->getNoSubstances(); i++)
{
k_ = i;
validation->crossValidation(d);
if (validation->getQ2() > best_q2)
{
best_q2 = validation->getQ2();
best_no = i;
}
if (validation->getQ2() < best_q2*0.5)
{
break;
}
}
k_ = best_no;
validation->setQ2(best_q2);
return 1;
}
}
}
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