rmontanana/BayesNet
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Add states as result in Proposal methods

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This commit is contained in:
Ricardo Montañana Gómez 2023-08-12 16:16:17 +02:00
parent 405887f833
commit 182b52a887
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
5 changed files with 10 additions and 8 deletions
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2
src/BayesNet/KDBLd.cc
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@ -15,7 +15,7 @@ namespace bayesnet {
// We have discretized the input data
// 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
KDB::fit(dataset, features, className, states);
localDiscretizationProposal(states, model);
states = localDiscretizationProposal(states, model);
return *this;
}
Tensor KDBLd::predict(Tensor& X)

6
src/BayesNet/Proposal.cc
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@ -9,12 +9,13 @@ namespace bayesnet {
delete value;
}
}
void Proposal::localDiscretizationProposal(map<string, vector<int>>& states, Network& model)
map<string, vector<int>> Proposal::localDiscretizationProposal(const map<string, vector<int>>& oldStates, Network& model)
{
// order of local discretization is important. no good 0, 1, 2...
// although we rediscretize features after the local discretization of every feature
auto order = model.topological_sort();
auto& nodes = model.getNodes();
map<string, vector<int>> states = oldStates;
vector<int> indicesToReDiscretize;
bool upgrade = false; // Flag to check if we need to upgrade the model
for (auto feature : order) {
@ -66,8 +67,9 @@ namespace bayesnet {
}
model.fit(pDataset, pFeatures, pClassName, states);
}
return states;
}
map<string, vector<int>> Proposal::fit_local_discretization(torch::Tensor& y)
map<string, vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y)
{
// Discretize the continuous input data and build pDataset (Classifier::dataset)
int m = Xf.size(1);

4
src/BayesNet/Proposal.h
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@ -14,8 +14,8 @@ namespace bayesnet {
virtual ~Proposal();
protected:
torch::Tensor prepareX(torch::Tensor& X);
void localDiscretizationProposal(map<string, vector<int>>& states, Network& model);
map<string, vector<int>> fit_local_discretization(torch::Tensor& y);
map<string, vector<int>> localDiscretizationProposal(const map<string, vector<int>>& states, Network& model);
map<string, vector<int>> fit_local_discretization(const torch::Tensor& y);
torch::Tensor Xf; // X continuous nxm tensor
torch::Tensor y; // y discrete nx1 tensor
map<string, mdlp::CPPFImdlp*> discretizers;

4
src/BayesNet/SPODELd.cc
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@ -15,7 +15,7 @@ namespace bayesnet {
// We have discretized the input data
// 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
SPODE::fit(dataset, features, className, states);
localDiscretizationProposal(states, model);
states = localDiscretizationProposal(states, model);
return *this;
}
SPODELd& SPODELd::fit(torch::Tensor& dataset, vector<string>& features_, string className_, map<string, vector<int>>& states_)
@ -31,7 +31,7 @@ namespace bayesnet {
// We have discretized the input data
// 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
SPODE::fit(dataset, features, className, states);
localDiscretizationProposal(states, model);
states = localDiscretizationProposal(states, model);
return *this;
}

2
src/BayesNet/TANLd.cc
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@ -15,7 +15,7 @@ namespace bayesnet {
// We have discretized the input data
// 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
TAN::fit(dataset, features, className, states);
localDiscretizationProposal(states, model);
states = localDiscretizationProposal(states, model);
return *this;
}