42 lines
1.7 KiB
C++
42 lines
1.7 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 NODE_H
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#define NODE_H
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#include <unordered_set>
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#include <vector>
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#include <string>
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#include <torch/torch.h>
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namespace bayesnet {
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class Node {
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public:
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explicit Node(const std::string&);
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void clear();
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void addParent(Node*);
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void addChild(Node*);
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void removeParent(Node*);
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void removeChild(Node*);
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std::string getName() const;
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std::vector<Node*>& getParents();
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std::vector<Node*>& getChildren();
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torch::Tensor& getCPT();
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void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights);
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int getNumStates() const;
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void setNumStates(int);
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unsigned minFill();
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std::vector<std::string> graph(const std::string& clasName); // Returns a std::vector of std::strings representing the graph in graphviz format
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double getFactorValue(std::map<std::string, int>&);
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private:
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std::string name;
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std::vector<Node*> parents;
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std::vector<Node*> children;
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int numStates = 0; // number of states of the variable
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torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
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std::vector<int64_t> dimensions; // dimensions of the cpTable
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std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
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};
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}
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#endif |