Add tests to reach 90% coverage
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@@ -10,10 +10,10 @@ namespace bayesnet {
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public:
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Network();
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explicit Network(float);
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explicit Network(Network&);
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explicit Network(const Network&);
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~Network() = default;
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torch::Tensor& getSamples();
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float getmaxThreads();
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float getMaxThreads() const;
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void addNode(const std::string&);
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void addEdge(const std::string&, const std::string&);
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std::map<std::string, std::unique_ptr<Node>>& getNodes();
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@@ -39,7 +39,7 @@ namespace bayesnet {
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std::vector<std::string> show() const;
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std::vector<std::string> graph(const std::string& title) const; // Returns a std::vector of std::strings representing the graph in graphviz format
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void initialize();
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void dump_cpt() const;
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std::string dump_cpt() const;
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inline std::string version() { return { project_version.begin(), project_version.end() }; }
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private:
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std::map<std::string, std::unique_ptr<Node>> nodes;
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@@ -49,7 +49,7 @@ namespace bayesnet {
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std::vector<std::string> features; // Including classname
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std::string className;
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double laplaceSmoothing;
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torch::Tensor samples; // nxm tensor used to fit the model
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torch::Tensor samples; // n+1xm tensor used to fit the model
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bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
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std::vector<double> predict_sample(const std::vector<int>&);
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std::vector<double> predict_sample(const torch::Tensor&);
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