rmontanana/BayesNet
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This commit is contained in:
Ricardo Montañana Gómez
2023-07-23 01:47:57 +02:00
parent 9981ad1811
commit 644b6c9be0
11 changed files with 107 additions and 118 deletions
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35
sample/sample.cc
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@@ -1,6 +1,5 @@
#include <iostream>
#include <string>
#include <torch/torch.h>
#include <thread>
#include <map>
#include <argparse/argparse.hpp>
@@ -19,20 +18,6 @@ using namespace std;
const string PATH = "../../data/";
inline constexpr auto hash_conv(const std::string_view sv)
{
unsigned long hash{ 5381 };
for (unsigned char c : sv) {
hash = ((hash << 5) + hash) ^ c;
}
return hash;
}
inline constexpr auto operator"" _sh(const char* str, size_t len)
{
return hash_conv(std::string_view{ str, len });
}
pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features)
{
vector<mdlp::labels_t>Xd;
@@ -98,15 +83,13 @@ int main(int argc, char** argv)
throw runtime_error("Model must be one of {AODE, KDB, SPODE, TAN}");
}
);
program.add_argument("--discretize").default_value(false).implicit_value(true);
bool class_last, discretize_dataset;
bool class_last;
string model_name, file_name, path, complete_file_name;
try {
program.parse_args(argc, argv);
file_name = program.get<string>("file");
path = program.get<string>("path");
model_name = program.get<string>("model");
discretize_dataset = program.get<bool>("discretize");
complete_file_name = path + file_name + ".arff";
class_last = datasets[file_name];
if (!file_exists(complete_file_name)) {
@@ -134,21 +117,21 @@ int main(int argc, char** argv)
features.push_back(feature.first);
}
// Discretize Dataset
vector<mdlp::labels_t> Xd;
map<string, int> maxes;
tie(Xd, maxes) = discretize(X, y, features);
auto [Xd, maxes] = discretize(X, y, features);
maxes[className] = *max_element(y.begin(), y.end()) + 1;
map<string, vector<int>> states;
for (auto feature : features) {
states[feature] = vector<int>(maxes[feature]);
}
states[className] = vector<int>(
maxes[className]);
double score;
auto classifiers = map<string, bayesnet::BaseClassifier*>({ { "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) }, { "SPODE", new bayesnet::SPODE(2) }, { "TAN", new bayesnet::TAN() } });
states[className] = vector<int>(maxes[className]);
auto classifiers = map<string, bayesnet::BaseClassifier*>({
{ "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) },
{ "SPODE", new bayesnet::SPODE(2) }, { "TAN", new bayesnet::TAN() }
}
);
bayesnet::BaseClassifier* clf = classifiers[model_name];
clf->fit(Xd, y, features, className, states);
score = clf->score(Xd, y);
auto score = clf->score(Xd, y);
auto lines = clf->show();
auto graph = clf->graph();
for (auto line : lines) {