70 lines
4.1 KiB
C++
70 lines
4.1 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 NETWORK_H
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#define NETWORK_H
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#include <map>
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#include <vector>
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#include "bayesnet/config.h"
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#include "Node.h"
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namespace bayesnet {
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enum class Smoothing_t {
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NONE = -1,
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ORIGINAL = 0,
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LAPLACE,
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CESTNIK
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};
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class Network {
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public:
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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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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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std::vector<std::string> getFeatures() const;
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int getStates() const;
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std::vector<std::pair<std::string, std::string>> getEdges() const;
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int getNumEdges() const;
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int getClassNumStates() const;
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std::string getClassName() const;
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/*
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Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.
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*/
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void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
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void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
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void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
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std::vector<int> predict(const std::vector<std::vector<int>>&); // Return mx1 std::vector of predictions
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torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
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torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
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std::vector<std::vector<double>> predict_proba(const std::vector<std::vector<int>>&); // Return mxn std::vector of probabilities
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torch::Tensor predict_proba(const torch::Tensor&); // Return mxn tensor of probabilities
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double score(const std::vector<std::vector<int>>&, const std::vector<int>&);
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std::vector<std::string> topological_sort();
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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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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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bool fitted;
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int classNumStates;
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std::vector<std::string> features; // Including classname
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std::string className;
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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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std::vector<double> exactInference(std::map<std::string, int>&);
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void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
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void checkFitData(int n_samples, int n_features, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
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void setStates(const std::map<std::string, std::vector<int>>&);
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};
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}
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#endif |