43 lines
1.9 KiB
C++
43 lines
1.9 KiB
C++
#ifndef TEST_UTILS_H
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#define TEST_UTILS_H
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#include <torch/torch.h>
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#include <string>
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#include <vector>
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#include <map>
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#include <std::tuple>
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#include "ArffFiles.h"
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#include "CPPFImdlp.h"
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bool file_exists(const std::std::string& name);
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std::pair<vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<string> features);
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std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y);
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std::tuple<vector<vector<int>>, std::vector<int>, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::string& name);
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std::tuple<torch::Tensor, torch::Tensor, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last, bool discretize_dataset);
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class RawDatasets {
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public:
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RawDatasets(const std::string& file_name, bool discretize)
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{
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// Xt can be either discretized or not
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tie(Xt, yt, featurest, classNamet, statest) = loadDataset(file_name, true, discretize);
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// Xv is always discretized
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tie(Xv, yv, featuresv, classNamev, statesv) = loadFile(file_name);
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auto yresized = torch::transpose(yt.view({ yt.size(0), 1 }), 0, 1);
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dataset = torch::cat({ Xt, yresized }, 0);
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nSamples = dataset.size(1);
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weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
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weightsv = std::vector<double>(nSamples, 1.0 / nSamples);
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classNumStates = discretize ? statest.at(classNamet).size() : 0;
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}
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torch::Tensor Xt, yt, dataset, weights;
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std::vector<vector<int>> Xv;
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std::vector<double> weightsv;
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std::vector<int> yv;
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std::vector<string> featurest, featuresv;
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map<std::string, std::vector<int>> statest, statesv;
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std::string classNamet, classNamev;
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int nSamples, classNumStates;
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double epsilon = 1e-5;
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};
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#endif //TEST_UTILS_H
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