Add notes to Classifier & use them in BoostAODE
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@ -115,6 +115,7 @@ namespace bayesnet {
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significanceModels.push_back(1.0);
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significanceModels.push_back(1.0);
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n_models++;
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n_models++;
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}
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}
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notes.push_back("Used features in initialization: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + algorithm);
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delete featureSelector;
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delete featureSelector;
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return featuresUsed;
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return featuresUsed;
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}
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}
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@ -197,6 +198,7 @@ namespace bayesnet {
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exitCondition = n_models >= maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
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exitCondition = n_models >= maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
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}
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}
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if (featuresUsed.size() != features.size()) {
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if (featuresUsed.size() != features.size()) {
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notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()));
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status = WARNING;
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status = WARNING;
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}
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}
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}
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}
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@ -19,6 +19,7 @@ namespace bayesnet {
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std::map<std::string, std::vector<int>> states;
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std::map<std::string, std::vector<int>> states;
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torch::Tensor dataset; // (n+1)xm tensor
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torch::Tensor dataset; // (n+1)xm tensor
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status_t status = NORMAL;
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status_t status = NORMAL;
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std::vector<std::string> notes; // Used to store messages occurred during the fit process
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void checkFitParameters();
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void checkFitParameters();
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virtual void buildModel(const torch::Tensor& weights) = 0;
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virtual void buildModel(const torch::Tensor& weights) = 0;
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void trainModel(const torch::Tensor& weights) override;
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void trainModel(const torch::Tensor& weights) override;
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