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Remove using namespace from Library

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This commit is contained in:
Ricardo Montañana Gómez
2023-11-08 18:45:35 +01:00
parent 92820555da
commit f9258e43b9
96 changed files with 1316 additions and 1340 deletions
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50
tests/TestUtils.cc
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@@ -1,19 +1,17 @@
#include "TestUtils.h"
using namespace std;
using namespace torch;
class Paths {
public:
static string datasets()
static std::string datasets()
{
return "../../data/";
}
};
pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features)
pair<std::vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<std::string> features)
{
vector<mdlp::labels_t> Xd;
map<string, int> maxes;
std::vector<mdlp::labels_t> Xd;
map<std::string, int> maxes;
auto fimdlp = mdlp::CPPFImdlp();
for (int i = 0; i < X.size(); i++) {
fimdlp.fit(X[i], y);
@@ -24,9 +22,9 @@ pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t
return { Xd, maxes };
}
vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::labels_t& y)
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
{
vector<mdlp::labels_t> Xd;
std::vector<mdlp::labels_t> Xd;
auto fimdlp = mdlp::CPPFImdlp();
for (int i = 0; i < X.size(); i++) {
fimdlp.fit(X[i], y);
@@ -36,7 +34,7 @@ vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::label
return Xd;
}
bool file_exists(const string& name)
bool file_exists(const std::std::string& name)
{
if (FILE* file = fopen(name.c_str(), "r")) {
fclose(file);
@@ -46,30 +44,30 @@ bool file_exists(const string& name)
}
}
tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last, bool discretize_dataset)
tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::std::string& name, bool class_last, bool discretize_dataset)
{
auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff", class_last);
// Get Dataset X, y
vector<mdlp::samples_t>& X = handler.getX();
std::vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t& y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
vector<string> features;
std::vector<std::string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
Tensor Xd;
auto states = map<string, vector<int>>();
torch::Tensor Xd;
auto states = map<std::string, std::vector<int>>();
if (discretize_dataset) {
auto Xr = discretizeDataset(X, y);
Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
for (int i = 0; i < features.size(); ++i) {
states[features[i]] = vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
auto item = states.at(features[i]);
iota(begin(item), end(item), 0);
Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
}
states[className] = vector<int>(*max_element(y.begin(), y.end()) + 1);
states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
iota(begin(states.at(className)), end(states.at(className)), 0);
} else {
Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
@@ -80,27 +78,27 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
}
tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name)
tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::std::string& name)
{
auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<string>(name) + ".arff");
handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff");
// Get Dataset X, y
vector<mdlp::samples_t>& X = handler.getX();
std::vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t& y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
vector<string> features;
std::vector<std::string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
// Discretize Dataset
vector<mdlp::labels_t> Xd;
map<string, int> maxes;
std::vector<mdlp::labels_t> Xd;
map<std::string, int> maxes;
tie(Xd, maxes) = discretize(X, y, features);
maxes[className] = *max_element(y.begin(), y.end()) + 1;
map<string, vector<int>> states;
map<std::string, std::vector<int>> states;
for (auto feature : features) {
states[feature] = vector<int>(maxes[feature]);
states[feature] = std::vector<int>(maxes[feature]);
}
states[className] = vector<int>(maxes[className]);
states[className] = std::vector<int>(maxes[className]);
return { Xd, y, features, className, states };
}