rmontanana/BayesNet
rmontanana/BayesNet
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Solved Ld poor results

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This commit is contained in:
Ricardo Montañana Gómez 2023-08-12 11:49:18 +02:00
parent 3a85481a5a
commit 405887f833
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
7 changed files with 13 additions and 9 deletions
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4
.vscode/launch.json vendored
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@ -25,12 +25,12 @@
"program": "${workspaceFolder}/build/src/Platform/main",
"args": [
"-m",
"TANLd",
"SPODELd",
"-p",
"/Users/rmontanana/Code/discretizbench/datasets",
"--stratified",
"-d",
"vehicle"
"iris"
],
"cwd": "/Users/rmontanana/Code/discretizbench",
},

2
src/BayesNet/AODELd.cc
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@ -12,7 +12,7 @@ namespace bayesnet {
Xf = X_;
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
states = fit_local_discretization(y);
// 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
Ensemble::fit(dataset, features, className, states);

2
src/BayesNet/KDBLd.cc
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@ -11,7 +11,7 @@ namespace bayesnet {
Xf = X_;
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
states = fit_local_discretization(y);
// 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);

6
src/BayesNet/Proposal.cc
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@ -67,10 +67,12 @@ namespace bayesnet {
model.fit(pDataset, pFeatures, pClassName, states);
}
}
void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
map<string, vector<int>> Proposal::fit_local_discretization(torch::Tensor& y)
{
// Discretize the continuous input data and build pDataset (Classifier::dataset)
int m = Xf.size(1);
int n = Xf.size(0);
map<string, vector<int>> states;
pDataset = torch::zeros({ n + 1, m }, kInt32);
auto yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
// discretize input data by feature(row)
@ -89,6 +91,8 @@ namespace bayesnet {
auto yStates = vector<int>(n_classes);
iota(yStates.begin(), yStates.end(), 0);
states[pClassName] = yStates;
pDataset.index_put_({ n, "..." }, y);
return states;
}
torch::Tensor Proposal::prepareX(torch::Tensor& X)
{

2
src/BayesNet/Proposal.h
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@ -15,7 +15,7 @@ namespace bayesnet {
protected:
torch::Tensor prepareX(torch::Tensor& X);
void localDiscretizationProposal(map<string, vector<int>>& states, Network& model);
void fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y);
map<string, vector<int>> fit_local_discretization(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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@ -11,7 +11,7 @@ namespace bayesnet {
Xf = X_;
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
states = fit_local_discretization(y);
// 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);
@ -27,7 +27,7 @@ namespace bayesnet {
features = features_;
className = className_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
states = fit_local_discretization(y);
// 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);

2
src/BayesNet/TANLd.cc
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@ -11,7 +11,7 @@ namespace bayesnet {
Xf = X_;
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
states = fit_local_discretization(y);
// 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);