Fix adult numeric features mistake

tests/TestBayesModels.cc

@@ -531,6 +531,11 @@ TEST_CASE("Test Dataset Loading", "[Datasets]")
         }
         std::cout << "| " << dataset.yt[sample].item<int>() << std::endl;
     }
+    auto features = dataset.features;
+    std::cout << "States:" << std::endl;
+    for (int i = 0; i < 14; i++) {
+        std::cout << i << " has " << dataset.states.at(features[i]).size() << " states." << std::endl;
+    }
     dataset = RawDatasets("adult", false);
     std::cout << "Dataset adult raw " << std::endl;
     for (int sample = 0; sample < max_sample; sample++) {
@@ -539,4 +544,10 @@ TEST_CASE("Test Dataset Loading", "[Datasets]")
         }
         std::cout << "| " << dataset.yt[sample].item<int>() << std::endl;
     }
+    std::cout << "States:" << std::endl;
+    for (int i = 0; i < 14; i++) {
+        std::cout << i << " has " << dataset.states.at(features[i]).size() << " states." << std::endl;
+    }
+    auto clf = bayesnet::TANLd();
+    clf.fit(dataset.Xt, dataset.yt, dataset.features, dataset.className, dataset.states, dataset.smoothing);
 }

tests/TestUtils.cc

@@ -213,6 +213,8 @@ void RawDatasets::loadDataset(const std::string& name, bool class_last)
             if (!is_numeric.at(i)) {
                 states[features[i]] = std::vector<int>(maxValues[features[i]]);
                 iota(begin(states.at(features[i])), end(states.at(features[i])), 0);
+            } else {
+                states[features[i]] = std::vector<int>();
             }
         }
         yt = torch::tensor(yv, torch::kInt32);

tests/data/all.txt

@@ -1,4 +1,4 @@
-adult;class;[0,2,4,11,12,13]
+adult;class;[0,2,4,10,11,12]
 balance-scale;class; all
 breast-w;Class; all
 diabetes;class; all