Update version, changelog, and Xsp2de clf name

2025-03-16 18:55:24 +01:00
parent 70c7d3dd3d
commit 9ee388561f
7 changed files with 61 additions and 53 deletions
--- a/bayesnet/classifiers/XSP2DE.h
+++ b/bayesnet/classifiers/XSP2DE.h
@@ -0,0 +1,75 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XSP2DE_H
+#define XSP2DE_H
+
+#include "Classifier.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+#include <torch/torch.h>
+#include <vector>
+
+namespace bayesnet {
+
+class XSp2de : public Classifier {
+  public:
+    XSp2de(int spIndex1, int spIndex2);
+    void setHyperparameters(const nlohmann::json &hyperparameters_) override;
+    void fitx(torch::Tensor &X, torch::Tensor &y, torch::Tensor &weights_, const Smoothing_t smoothing);
+    std::vector<double> predict_proba(const std::vector<int> &instance) const;
+    std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>> &test_data) override;
+    int predict(const std::vector<int> &instance) const;
+    std::vector<int> predict(std::vector<std::vector<int>> &test_data) override;
+    torch::Tensor predict(torch::Tensor &X) override;
+    torch::Tensor predict_proba(torch::Tensor &X) override;
+
+    float score(torch::Tensor &X, torch::Tensor &y) override;
+    float score(std::vector<std::vector<int>> &X, std::vector<int> &y) override;
+    std::string to_string() const;
+    std::vector<std::string> graph(const std::string &title) const override {
+        return std::vector<std::string>({title});
+    }
+
+    int getNumberOfNodes() const override;
+    int getNumberOfEdges() const override;
+    int getNFeatures() const;
+    int getClassNumStates() const override;
+    int getNumberOfStates() const override;
+
+  protected:
+    void buildModel(const torch::Tensor &weights) override;
+    void trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing) override;
+
+  private:
+    void addSample(const std::vector<int> &instance, double weight);
+    void normalize(std::vector<double> &v) const;
+    void computeProbabilities();
+
+    int superParent1_;
+    int superParent2_;
+    int nFeatures_;
+    int statesClass_;
+    double alpha_;
+    double initializer_;
+
+    std::vector<int> states_;
+    std::vector<double> classCounts_;
+    std::vector<double> classPriors_;
+    std::vector<double> sp1FeatureCounts_, sp1FeatureProbs_;
+    std::vector<double> sp2FeatureCounts_, sp2FeatureProbs_;
+    // childOffsets_[f] will be the offset into childCounts_ for feature f.
+    // If f is either superParent1 or superParent2, childOffsets_[f] = -1
+    std::vector<int> childOffsets_;
+    // For each child f, we store p(x_f | c, sp1Val, sp2Val).  We'll store the raw
+    // counts in childCounts_, and the probabilities in childProbs_, with a
+    // dimension block of size: states_[f]* statesClass_* states_[sp1]* states_[sp2].
+    std::vector<double> childCounts_;
+    std::vector<double> childProbs_;
+    CountingSemaphore &semaphore_;
+};
+
+} // namespace bayesnet
+#endif // XSP2DE_H