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

Implement Conditional Mutual Information

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2024-05-15 00:48:02 +02:00
parent 56b62a67cc
commit e2e0fb0c40
3 changed files with 145 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
tests/TestBayesMetrics.cc
View File

@@ -83,4 +83,32 @@ TEST_CASE("Select all features ordered by Mutual Information", "[Metrics]")
auto kBest = metrics.SelectKBestWeighted(raw.weights, true, 0);
REQUIRE(kBest.size() == raw.features.size());
REQUIRE(kBest == std::vector<int>({ 1, 0, 3, 2 }));
}
TEST_CASE("Entropy Test", "[Metrics]")
{
auto raw = RawDatasets("iris", true);
bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
auto result = metrics.entropy(raw.dataset.index({ 0, "..." }), raw.weights);
REQUIRE(result == Catch::Approx(0.9848175048828125).epsilon(raw.epsilon));
auto data = torch::tensor({ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 }, torch::kInt32);
auto weights = torch::tensor({ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }, torch::kFloat32);
result = metrics.entropy(data, weights);
REQUIRE(result == Catch::Approx(0.61086434125900269).epsilon(raw.epsilon));
data = torch::tensor({ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 }, torch::kInt32);
result = metrics.entropy(data, weights);
REQUIRE(result == Catch::Approx(0.693147180559945).epsilon(raw.epsilon));
}
TEST_CASE("Conditional Entropy", "[Metrics]")
{
auto raw = RawDatasets("iris", true);
bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
auto feature0 = raw.dataset.index({ 0, "..." });
auto feature1 = raw.dataset.index({ 1, "..." });
auto feature2 = raw.dataset.index({ 2, "..." });
auto feature3 = raw.dataset.index({ 3, "..." });
auto labels = raw.dataset.index({ 4, "..." });
auto result = metrics.conditionalEntropy(feature0, feature1, labels, raw.weights);
auto result2 = metrics.conditionalEntropy2(feature0, feature1, labels, raw.weights);
std::cout << "Result=" << result << "\n";
std::cout << "Result2=" << result2 << "\n";
}