Create XBAODE classifier

2025-02-23 19:44:13 +01:00
parent 1b26de1e38
commit 5daf7cbd69
7 changed files with 210 additions and 41 deletions
--- a/src/experimental_clfs/XBAODE.cpp
+++ b/src/experimental_clfs/XBAODE.cpp
@@ -0,0 +1,90 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "XBAODE.h"
+
+namespace platform {
+    XBAODE::XBAODE() : semaphore_{ CountingSemaphore::getInstance() }
+    {
+    }
+    XBAODE& XBAODE::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
+    {
+        aode_.fit(X, y, features, className, states, smoothing);
+        fitted = true;
+        return *this;
+    }
+    std::vector<std::vector<double>> XBAODE::predict_proba(std::vector<std::vector<int>>& test_data)
+    {
+        return aode_.predict_proba_threads(test_data);
+    }
+    std::vector<int> XBAODE::predict(std::vector<std::vector<int>>& test_data)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        return aode_.predict(test_data);
+    }
+    float XBAODE::score(std::vector<std::vector<int>>& test_data, std::vector<int>& labels)
+    {
+        return aode_.score(test_data, labels);
+    }
+
+    //
+    // statistics
+    //
+    int XBAODE::getNumberOfNodes() const
+    {
+        return aode_.getNumberOfNodes();
+    }
+    int XBAODE::getNumberOfEdges() const
+    {
+        return aode_.getNumberOfEdges();
+    }
+    int XBAODE::getNumberOfStates() const
+    {
+        return aode_.getNumberOfStates();
+    }
+    int XBAODE::getClassNumStates() const
+    {
+        return aode_.getClassNumStates();
+    }
+
+    //
+    // Fit
+    //
+    // fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
+    XBAODE& XBAODE::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
+    {
+        aode_.fit(X, y, features, className, states, smoothing);
+        return *this;
+    }
+    XBAODE& XBAODE::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
+    {
+        aode_.fit(dataset, features, className, states, smoothing);
+        return *this;
+    }
+    XBAODE& XBAODE::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
+    {
+        aode_.fit(dataset, features, className, states, weights, smoothing);
+        return *this;
+    }
+    //
+    // Predict
+    //
+    torch::Tensor XBAODE::predict(torch::Tensor& X)
+    {
+        return aode_.predict(X);
+    }
+    torch::Tensor XBAODE::predict_proba(torch::Tensor& X)
+    {
+        return aode_.predict_proba(X);
+    }
+    float XBAODE::score(torch::Tensor& X, torch::Tensor& y)
+    {
+        return aode_.score(X, y);
+    }
+
+}