Improve three_nodes experiment

three_nodes_classification/dataset.py

@@ -8,14 +8,16 @@ def generate_three_nodes_dataset(n: int, device="cpu") -> list[Data]:
 
     node_values = torch.empty(n, 3, dtype=torch.float, device=device)
 
-    # Set the two side nodes to something between 0 and 10
+    # Set the two side nodes to something between 0 and 9
     node_values[:, 1:] = torch.randint(10, (n, 2)).float()
 
     # Set the root node to the sum of the two side nodes
     node_values[:, 0] = torch.sum(node_values[:, 1:], 1)
 
     # we replace half of the root values with random values
     mask = torch.rand(n) > 0.5
+
+    # random from 0 to 18
     node_values[mask, 0] = torch.randint(10 * 2 - 1, (mask.sum(),)).float()
 
     # We need to reshape the tensor to have the correct shape

three_nodes_regression/summary.jl

@@ -8,3 +8,9 @@ results = CSV.read(joinpath(results_folder, "runs.csv"), DataFrame)
 
 normalised = filter(row -> row.normalise, results)
 not_normalised = filter(row -> !row.normalise, results)
+
+print("Normalised")
+print(describe(normalised))
+
+print("Not normalised")
+print(describe(not_normalised))