diff --git a/hal_det_llama.py b/hal_det_llama.py
index 5baa091..5ad1882 100644
--- a/hal_det_llama.py
+++ b/hal_det_llama.py
@@ -10,6 +10,7 @@ import numpy as np
 import pickle
 # from utils import get_llama_activations_bau, tokenized_tqa, tokenized_tqa_gen, tokenized_tqa_gen_end_q
 from utils import mahalanobis_distance
+from scipy.io import savemat
 import llama_iti
 import pickle
 import argparse
@@ -411,6 +412,10 @@ def main():
                     
                     inv_C_t= torch.linalg.pinv(C_t) + torch.eye(C_t.shape[0], dtype=int).cuda() * epsilon
                     inv_C_h= torch.linalg.pinv(C_h) + torch.eye(C_h.shape[0], dtype=int).cuda() * epsilon
+                    test_t=torch.from_numpy(embed_generated[:, layer, :]).cuda()-centered_t
+                    test_h=torch.from_numpy(embed_generated[:, layer, :]).cuda()-centered_h
+                    # scores= torch.sqrt(torch.clamp(test_t @ inv_C_t @ test_t.T, min=0.0))
+                    # - torch.sqrt(torch.clamp(test_h @ inv_C_h @ test_h.T, min=0.0)) 
                     scores= torch.sqrt(torch.clamp(centered @ inv_C_t @ centered.T, min=0.0))
                     - torch.sqrt(torch.clamp(centered @ inv_C_h @ centered.T, min=0.0)) 
                     # scores= mahalanobis_distance(torch.from_numpy(embed_generated[:, layer, :]).cuda(), torch.from_numpy(mean_recorded).cuda(), C_) torch.clamp(centered @ inv_C_t @ centered.T, min=0.0)
@@ -499,11 +504,6 @@ def main():
                                   allow_pickle=True)
         else:
             assert "Not supported!"
-                # embed_generated = np.load(f'save_for_eval/{args.dataset_name}/{args.model_name}_hal_det/' + info + f'{args.model_name}_gene_embeddings_layer_wise.npy',
-                                #   allow_pickle=True)
-                # embed_generated = np.load(
-                #     f'save_for_eval/{args.dataset_name}/{args.model_name}_hal_det/' + info + f'{args.model_name}_gene_embeddings_head_wise.npy',
-                #     allow_pickle=True)
         feat_indices_wild = []
         feat_indices_eval = []
 
@@ -525,9 +525,6 @@ def main():
         else:
             embed_generated_wild = embed_generated[feat_indices_wild]
             embed_generated_eval = embed_generated[feat_indices_eval]
-                # print(embed_generated.shape)
-                # print(embed_generated_h.shape)
-                # print(embed_generated_t.shape)
             embed_generated_hal,embed_generated_tru=embed_generated_h[feat_indices_eval], embed_generated_t[feat_indices_eval]
 
 
@@ -566,7 +563,10 @@ def main():
 
         assert test_scores.shape[1] == 1
         test_scores = np.sqrt(np.sum(np.square(test_scores), axis=1))
-
+        mdic = {"gt_1": test_scores[gt_label_test == 1], "gt_0": test_scores[gt_label_test == 0], "scale_gt_1":returned_results['best_sign'] * test_scores[gt_label_test == 1],
+                "scale_gt_0": returned_results['best_sign'] *test_scores[gt_label_test == 0]
+                }
+        savemat("tqa_score.mat", mdic)
         measures = get_measures(returned_results['best_sign'] * test_scores[gt_label_test == 1],
                                  returned_results['best_sign'] *test_scores[gt_label_test == 0], plot=False)
         print_measures(measures[0], measures[1], measures[2], 'direct-projection')
@@ -612,6 +612,7 @@ def main():
 
 
                 clf.eval()
+                torch.save(clf.state_dict(), f'save_for_eval/{args.dataset_name}/{args.model_name}_hal_det/' + info+  '_{layer}_model_weights.pth')
                 output = clf(torch.from_numpy(
                     embed_generated_test[:, layer, :]).cuda())
                 pca_wild_score_binary_cls = torch.sigmoid(output)
@@ -623,7 +624,7 @@ def main():
                     breakpoint()
                 measures = get_measures(pca_wild_score_binary_cls[gt_label_test == 1],
                                         pca_wild_score_binary_cls[gt_label_test == 0], plot=False)
-
+                # print_measures(measures[0], measures[1], measures[2], 'class-acc')
                 if measures[0] > best_auroc:
                     best_auroc = measures[0]
                     best_result = [100 * measures[0]]
diff --git a/tqa_score.mat b/tqa_score.mat
new file mode 100644
index 0000000..c8be060
Binary files /dev/null and b/tqa_score.mat differ