41 lines
1.4 KiB
C++
41 lines
1.4 KiB
C++
#include "AODELd.h"
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#include "Models.h"
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namespace bayesnet {
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using namespace std;
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AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
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AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
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{
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checkInput(X_, y_);
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features = features_;
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className = className_;
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Xf = X_;
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y = y_;
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// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
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states = fit_local_discretization(y);
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// We have discretized the input data
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// 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
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Ensemble::fit(dataset, features, className, states);
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return *this;
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}
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void AODELd::buildModel(const torch::Tensor& weights)
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{
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models.clear();
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for (int i = 0; i < features.size(); ++i) {
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models.push_back(std::make_unique<SPODELd>(i));
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}
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n_models = models.size();
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significanceModels = vector<double>(n_models, 1.0);
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}
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void AODELd::trainModel(const torch::Tensor& weights)
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{
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for (const auto& model : models) {
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model->fit(Xf, y, features, className, states);
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}
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}
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vector<string> AODELd::graph(const string& name) const
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{
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return Ensemble::graph(name);
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}
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} |