rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Refactor Makefile

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2023-10-05 11:45:00 +02:00
parent 5f0676691c
commit da8d018ec4
3 changed files with 33 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files

26
tests/TestBayesMetrics.cc
View File

@@ -9,7 +9,7 @@ using namespace std;
TEST_CASE("Metrics Test", "[Metrics]")
{
string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
map<string, pair<int, vector<int>>> results = {
map<string, pair<int, vector<int>>> resultsKBest = {
{"glass", {7, { 3, 2, 0, 1, 6, 7, 5 }}},
{"iris", {3, { 1, 0, 2 }} },
{"ecoli", {6, { 2, 3, 1, 0, 4, 5 }}},
@@ -31,26 +31,16 @@ TEST_CASE("Metrics Test", "[Metrics]")
{
bayesnet::Metrics metrics(XDisc, featuresDisc, classNameDisc, classNumStates);
torch::Tensor weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
vector<int> kBest = metrics.SelectKBestWeighted(weights, true, results.at(file_name).first);
REQUIRE(kBest.size() == results.at(file_name).first);
REQUIRE(kBest == results.at(file_name).second);
vector<int> kBest = metrics.SelectKBestWeighted(weights, true, resultsKBest.at(file_name).first);
REQUIRE(kBest.size() == resultsKBest.at(file_name).first);
REQUIRE(kBest == resultsKBest.at(file_name).second);
}
SECTION("Test mutualInformation")
{
// torch::Tensor samples = torch::rand({ 10, 5 });
// vector<string> features = { "feature1", "feature2", "feature3", "feature4", "feature5" };
// string className = "class1";
// int classNumStates = 2;
// bayesnet::Metrics obj(samples, features, className, classNumStates);
// torch::Tensor firstFeature = samples.select(1, 0);
// torch::Tensor secondFeature = samples.select(1, 1);
// torch::Tensor weights = torch::ones({ 10 });
// double mi = obj.mutualInformation(firstFeature, secondFeature, weights);
// REQUIRE(mi >= 0);
bayesnet::Metrics metrics(XDisc, featuresDisc, classNameDisc, classNumStates);
torch::Tensor weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
auto result = metrics.mutualInformation(dataset.index({ 1, "..." }), dataset.index({ 2, "..." }), weights);
REQUIRE(result == 0.87);
}
}