50 lines
2.1 KiB
C++
50 lines
2.1 KiB
C++
// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include "SPODELd.h"
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namespace bayesnet {
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SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
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SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
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{
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checkInput(X_, y_);
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Xf = X_;
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y = y_;
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return commonFit(features_, className_, states_);
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}
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SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
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{
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if (!torch::is_floating_point(dataset)) {
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throw std::runtime_error("Dataset must be a floating point tensor");
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}
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Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
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y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
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return commonFit(features_, className_, states_);
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}
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SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
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{
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features = features_;
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className = className_;
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// Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
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states = fit_local_discretization(y);
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// We have discretized the input data
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// 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
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SPODE::fit(dataset, features, className, states);
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states = localDiscretizationProposal(states, model);
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return *this;
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}
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torch::Tensor SPODELd::predict(torch::Tensor& X)
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{
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auto Xt = prepareX(X);
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return SPODE::predict(Xt);
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}
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std::vector<std::string> SPODELd::graph(const std::string& name) const
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{
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return SPODE::graph(name);
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}
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} |