35 lines
1.3 KiB
C++
35 lines
1.3 KiB
C++
// ***************************************************************
|
|
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
|
|
// SPDX-FileType: SOURCE
|
|
// SPDX-License-Identifier: MIT
|
|
// ***************************************************************
|
|
|
|
#include "KDBLd.h"
|
|
|
|
namespace bayesnet {
|
|
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
|
|
KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
|
|
{
|
|
checkInput(X_, y_);
|
|
features = features_;
|
|
className = className_;
|
|
Xf = X_;
|
|
y = y_;
|
|
// Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & 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);
|
|
states = localDiscretizationProposal(states, model);
|
|
return *this;
|
|
}
|
|
torch::Tensor KDBLd::predict(torch::Tensor& X)
|
|
{
|
|
auto Xt = prepareX(X);
|
|
return KDB::predict(Xt);
|
|
}
|
|
std::vector<std::string> KDBLd::graph(const std::string& name) const
|
|
{
|
|
return KDB::graph(name);
|
|
}
|
|
} |