50 lines
1.8 KiB
C++
50 lines
1.8 KiB
C++
#ifndef NETWORK_H
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#define NETWORK_H
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#include "Node.h"
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#include <map>
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#include <vector>
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namespace bayesnet {
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class Network {
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private:
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map<string, Node*> nodes;
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map<string, vector<int>> dataset;
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float maxThreads;
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int classNumStates;
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vector<string> features;
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string className;
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int laplaceSmoothing;
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torch::Tensor samples;
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bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
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vector<double> predict_sample(const vector<int>&);
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vector<double> exactInference(map<string, int>&);
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double computeFactor(map<string, int>&);
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double mutual_info(torch::Tensor&, torch::Tensor&);
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double entropy(torch::Tensor&);
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double conditionalEntropy(torch::Tensor&, torch::Tensor&);
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double mutualInformation(torch::Tensor&, torch::Tensor&);
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public:
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Network();
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Network(float, int);
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Network(float);
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Network(Network&);
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~Network();
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torch::Tensor& getSamples();
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float getmaxThreads();
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void addNode(string, int);
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void addEdge(const string, const string);
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map<string, Node*>& getNodes();
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vector<string> getFeatures();
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int getStates();
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int getClassNumStates();
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string getClassName();
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void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
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vector<int> predict(const vector<vector<int>>&);
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//Computes the conditional edge weight of variable index u and v conditioned on class_node
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torch::Tensor conditionalEdgeWeight();
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vector<vector<double>> predict_proba(const vector<vector<int>>&);
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double score(const vector<vector<int>>&, const vector<int>&);
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inline string version() { return "0.1.0"; }
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};
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}
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#endif |