Publish repository: Thu Nov 20 10:23:04 UTC 2025

Author: github-actions[bot]

.github/workflows/public_merge_check.yml

@@ -26,7 +26,7 @@ jobs:
           git remote add public https://x-access-token:${{ secrets.PRIVATE_REPO_ARLDM }}@github.com/utiasDSL/ARLDM-Advanced-Robot-Learning-And-Decision-Making.git
           git fetch public ${{ github.ref_name }}
 
-          git checkout -b public_sync-${{ github.ref_name }} public/main
+          git checkout -b public_sync-${{ github.ref_name }} origin/main
 
       - name: Pick new commits (since lat repository publish)
         if: ${{ github.repository == 'utiasDSL/ARLDM-Advanced-Robot-Learning-And-Decision-Making' }}
@@ -68,7 +68,7 @@ jobs:
 
       # Testing
       - name: Run pytest (Development)
-        if: ${{ github.repository == 'utiasDSL/ARLDM-Advanced-Robot-Learning-And-Decision-Making' }}
+        if: ${{ github.repository == 'utiasDSL/ARLDM-Advanced-Robot-Learning-And-Decision-Making' && github.event.inputs.admin_key != secrets.ADMIN_KEY }}
         working-directory: Advanced-Robot-Learning-and-Decision-Making-Programming-Exercises
         run: |
           if [[ "${{ github.event.inputs.admin_key }}" == "${{ secrets.ADMIN_KEY }}" ]]; then
@@ -91,7 +91,7 @@ jobs:
             "
 
       - name: Run pytest (Application)
-        if: ${{ github.repository == 'utiasDSL/ARLDM-Advanced-Robot-Learning-And-Decision-Making' }}
+        if: ${{ github.repository == 'utiasDSL/ARLDM-Advanced-Robot-Learning-And-Decision-Making' && github.event.inputs.admin_key != secrets.ADMIN_KEY }}
         working-directory: Advanced-Robot-Learning-and-Decision-Making-Programming-Exercises
         run: |
           if [[ "${{ github.event.inputs.admin_key }}" == "${{ secrets.ADMIN_KEY }}" ]]; then

src/exercise04/exercise04/gaussian_process.py

@@ -231,6 +231,7 @@ def fit(self, X, y):
             idx = np.random.choice(X.shape[0], self.max_samples, replace=False)
             X = X[idx]
             y = y[idx]
+            self.X_train_subset = X # for prediction
         ########################################################################
         # Task 1.2
         # TODO:
@@ -281,10 +282,10 @@ def predict(self, X_test, return_std=False) -> Union[np.ndarray, Tuple[np.ndarra
         #    returned by default.
         ########################################################################
         # Show relevant variables
-        L = self.L  
-        X_train = self.X_train 
-        alphas = self.alphas 
-        kernel = self.kernel 
+        L = self.L
+        X_train = self.X_train if not hasattr(self, "X_train_subset") else self.X_train_subset
+        alphas = self.alphas
+        kernel = self.kernel
         output_dim = self.output_dim
         mean, std = None, None  # Compute those
         ########################################################################

src/exercise04/exercise04/outputs/nn_checkpoint_ex04.ckpt

@@ -118,7 +118,7 @@ def setUp(self):
         self.X_train = np.random.rand(20, 5)
         self.y_train = np.random.rand(20, 2)
 
-    def test_init(self):
+    def test_init_SVGP(self):
         """Test if the SVGP model initializes correctly."""
         try:
             gp = SVGPStudent(X_train=self.X_train, num_tasks=self.y_train.shape[1], num_inducing_points=self.X_train.shape[0]//2)
@@ -166,7 +166,7 @@ def setUp(self):
         output_path = os.path.join(import_prefix, "outputs")
         self.checkpoint_path = os.path.join(output_path, "nn_checkpoint_ex04.ckpt")
 
-    def test_init(self):
+    def test_init_NN(self):
         """Test if the NeuralNetwork class can be initialized with a checkpoint."""
         try:
             model = NeuralNetwork(