78 lines
3.2 KiB
C++
78 lines
3.2 KiB
C++
// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#ifndef XSPODE_H
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#define XSPODE_H
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#include <vector>
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#include <torch/torch.h>
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#include "Classifier.h"
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#include "bayesnet/utils/CountingSemaphore.h"
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namespace bayesnet {
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class XSpode : public Classifier {
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public:
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explicit XSpode(int spIndex);
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std::vector<double> predict_proba(const std::vector<int>& instance) const;
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std::vector<std::vector<double>> predict_proba(const std::vector<std::vector<int>>& test_data);
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int predict(const std::vector<int>& instance) const;
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void normalize(std::vector<double>& v) const;
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std::string to_string() const;
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int getNFeatures() const;
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int getNumberOfNodes() const override;
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int getNumberOfEdges() const override;
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int getNumberOfStates() const override;
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int getClassNumStates() const override;
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std::vector<int>& getStates();
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std::vector<std::string> graph(const std::string& title) const override { return std::vector<std::string>({title}); }
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void fit(std::vector<std::vector<int>>& X, std::vector<int>& y, torch::Tensor& weights_, const Smoothing_t smoothing);
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void setHyperparameters(const nlohmann::json& hyperparameters_) override;
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//
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// Classifier interface
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//
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torch::Tensor predict(torch::Tensor& X) override;
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std::vector<int> predict(std::vector<std::vector<int>>& X) override;
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torch::Tensor predict_proba(torch::Tensor& X) override;
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std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
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protected:
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void buildModel(const torch::Tensor& weights) override;
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void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
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private:
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void addSample(const std::vector<int>& instance, double weight);
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void computeProbabilities();
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int superParent_;
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int nFeatures_;
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int statesClass_;
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std::vector<int> states_; // [states_feat0, ..., states_feat(N-1)] (class not included in this array)
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const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
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// Class counts
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std::vector<double> classCounts_; // [c], accumulative
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std::vector<double> classPriors_; // [c], after normalization
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// For p(x_sp = spVal | c)
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std::vector<double> spFeatureCounts_; // [spVal * statesClass_ + c]
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std::vector<double> spFeatureProbs_; // same shape, after normalization
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// For p(x_child = childVal | x_sp = spVal, c)
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// childCounts_ is big enough to hold all child features except sp:
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// For each child f, we store childOffsets_[f] as the start index, then
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// childVal, spVal, c => the data.
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std::vector<double> childCounts_;
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std::vector<double> childProbs_;
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std::vector<int> childOffsets_;
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double alpha_ = 1.0;
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double initializer_; // for numerical stability
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CountingSemaphore& semaphore_;
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};
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}
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#endif // XSPODE_H
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