Begin implementing KDB

src/BaseClassifier.cc

@@ -0,0 +1,90 @@
+#include "BaseClassifier.h"
+
+namespace bayesnet {
+    using namespace std;
+    using namespace torch;
+
+    BaseClassifier::BaseClassifier(Network model) : model(model), m(0), n(0) {}
+    BaseClassifier& BaseClassifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
+    {
+
+        dataset = torch::cat({ X, y.view({150, 1}) }, 1);
+        this->features = features;
+        this->className = className;
+        this->states = states;
+        cout << "Checking fit parameters" << endl;
+        checkFitParameters();
+        train();
+        return *this;
+    }
+    BaseClassifier& BaseClassifier::fit(Tensor& X, Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
+    {
+        this->X = X;
+        this->y = y;
+        return build(features, className, states);
+    }
+    BaseClassifier& BaseClassifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
+    {
+        this->X = torch::zeros({ static_cast<int64_t>(X[0].size()), static_cast<int64_t>(X.size()) }, kInt64);
+        for (int i = 0; i < X.size(); ++i) {
+            this->X.index_put_({ "...", i }, torch::tensor(X[i], kInt64));
+        }
+        this->y = torch::tensor(y, kInt64);
+        return build(features, className, states);
+    }
+    void BaseClassifier::checkFitParameters()
+    {
+        auto sizes = X.sizes();
+        m = sizes[0];
+        n = sizes[1];
+        if (m != y.size(0)) {
+            throw invalid_argument("X and y must have the same number of samples");
+        }
+        if (n != features.size()) {
+            throw invalid_argument("X and features must have the same number of features");
+        }
+        if (states.find(className) == states.end()) {
+            throw invalid_argument("className not found in states");
+        }
+        for (auto feature : features) {
+            if (states.find(feature) == states.end()) {
+                throw invalid_argument("feature [" + feature + "] not found in states");
+            }
+        }
+    }
+    vector<vector<int>> tensorToVector(const torch::Tensor& tensor)
+    {
+        // convert mxn tensor to nxm vector
+        vector<vector<int>> result;
+        auto tensor_accessor = tensor.accessor<int, 2>();
+
+        // Iterate over columns and rows of the tensor
+        for (int j = 0; j < tensor.size(1); ++j) {
+            vector<int> column;
+            for (int i = 0; i < tensor.size(0); ++i) {
+                column.push_back(tensor_accessor[i][j]);
+            }
+            result.push_back(column);
+        }
+
+        return result;
+    }
+    Tensor BaseClassifier::predict(Tensor& X)
+    {
+        auto m_ = X.size(0);
+        auto n_ = X.size(1);
+        vector<vector<int>> Xd(n_, vector<int>(m_, 0));
+        for (auto i = 0; i < n_; i++) {
+            auto temp = X.index({ "...", i });
+            Xd[i] = vector<int>(temp.data_ptr<int>(), temp.data_ptr<int>() + m_);
+        }
+        auto yp = model.predict(Xd);
+        auto ypred = torch::tensor(yp, torch::kInt64);
+        return ypred;
+    }
+    float BaseClassifier::score(Tensor& X, Tensor& y)
+    {
+        Tensor y_pred = predict(X);
+        return (y_pred == y).sum().item<float>() / y.size(0);
+    }
+}