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Refactor library and models to lighten data stored

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Refactro Ensemble to inherit from Classifier insted of BaseClassifier
This commit is contained in:
Ricardo Montañana Gómez
2023-08-07 12:49:37 +02:00
parent e74565ba01
commit 06db8f51ce
28 changed files with 134 additions and 189 deletions
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10
src/BayesNet/SPODELd.cc
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@@ -2,7 +2,7 @@
namespace bayesnet {
using namespace std;
SPODELd::SPODELd(int root) : SPODE(root), Proposal(SPODE::Xv, SPODE::yv, features, className) {}
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
@@ -12,15 +12,11 @@ namespace bayesnet {
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
generateTensorXFromVector();
// 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(SPODE::Xv, SPODE::yv, features, className, states);
SPODE::fit(dataset, features, className, states);
localDiscretizationProposal(states, model);
generateTensorXFromVector();
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
model.fit(SPODE::Xv, SPODE::yv, features, className);
//model.fit(SPODE::Xv, SPODE::yv, features, className);
return *this;
}
Tensor SPODELd::predict(Tensor& X)