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Spodeld working with poor accuracy

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This commit is contained in:
Ricardo Montañana Gómez
2023-08-10 02:06:18 +02:00
parent 323444b74a
commit 0ad5505c16
3 changed files with 21 additions and 16 deletions
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24
src/BayesNet/SPODELd.cc
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@@ -2,11 +2,10 @@
namespace bayesnet {
using namespace std;
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) { cout << "SPODELd constructor" << endl; }
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
cout << "YOOOOOOOOOOOOOOOOOOOo" << endl;
features = features_;
className = className_;
Xf = X_;
@@ -19,15 +18,28 @@ namespace bayesnet {
localDiscretizationProposal(states, model);
return *this;
}
SPODELd& SPODELd::fit(torch::Tensor& dataset, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
cout << "Xf " << Xf.sizes() << " dtype: " << Xf.dtype() << endl;
y = dataset.index({ -1, "..." }).clone();
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_;
className = className_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
// We have discretized the input data
// 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
SPODE::fit(dataset, features, className, states);
localDiscretizationProposal(states, model);
return *this;
}
Tensor SPODELd::predict(Tensor& X)
{
auto Xt = prepareX(X);
return SPODE::predict(Xt);
}
void SPODELd::test()
{
cout << "SPODELd test" << endl;
}
vector<string> SPODELd::graph(const string& name) const
{
return SPODE::graph(name);