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diff --git a/data/processed_data/npy_image/data_training_contrastive/Serratia marcescens/SERMAR14_AER.npy b/data/processed_data/npy_image/data_training_contrastive/Serratia marcescens/SERMAR14_AER.npy
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diff --git a/data/processed_data/npy_image/data_training_contrastive/Serratia marcescens/SERMAR15_AER.npy b/data/processed_data/npy_image/data_training_contrastive/Serratia marcescens/SERMAR15_AER.npy
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diff --git a/data/processed_data/npy_image/data_training_contrastive/Serratia marcescens/SERMAR15_ANA.npy b/data/processed_data/npy_image/data_training_contrastive/Serratia marcescens/SERMAR15_ANA.npy
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diff --git a/image_ref/dataset_ref.py b/image_ref/dataset_ref.py
index 849426be3bdebf316f9fdb6bb4ac4ab10ef18198..8d7f1fb0abca6ae3171cb70a42f8e2b6b8d3a866 100644
--- a/image_ref/dataset_ref.py
+++ b/image_ref/dataset_ref.py
@@ -1,3 +1,6 @@
+import random
+from cProfile import label
+
 import numpy as np
 import torch
 import torchvision
@@ -112,7 +115,7 @@ def make_dataset_custom(
 
 class ImageFolderDuo(data.Dataset):
     def __init__(self, root, transform=None, target_transform=None,
-                 flist_reader=make_dataset_custom, loader=npy_loader, ref_dir = None):
+                 flist_reader=make_dataset_custom, loader=npy_loader, ref_dir = None, positive_prop=None):
         self.root = root
         self.imlist = flist_reader(root)
         self.transform = transform
@@ -120,12 +123,20 @@ class ImageFolderDuo(data.Dataset):
         self.loader = loader
         self.classes = torchvision.datasets.folder.find_classes(root)[0]
         self.ref_dir = ref_dir
+        self.positive_prop = positive_prop
 
     def __getitem__(self, index):
         impathAER, impathANA, target = self.imlist[index]
         imgAER = self.loader(impathAER)
         imgANA = self.loader(impathANA)
-        label_ref = np.random.randint(0,len(self.classes)-1)
+        if self.positive_prop is not None:
+            i = random.randint(0,100)
+            if i < self.positive_prop:
+                label_ref = target
+            else :
+                label_ref = np.random.randint(0,len(self.classes)-1) #can be postive too (border effect)
+        else :
+            label_ref = np.random.randint(0,len(self.classes)-1)
         class_ref = self.classes[label_ref]
         path_ref = self.ref_dir +'/'+ class_ref + '.npy'
         img_ref = self.loader(path_ref)
@@ -139,7 +150,7 @@ class ImageFolderDuo(data.Dataset):
     def __len__(self):
         return len(self.imlist)
 
-def load_data_duo(base_dir, batch_size, shuffle=True, noise_threshold=0, ref_dir = None):
+def load_data_duo(base_dir, batch_size, shuffle=True, noise_threshold=0, ref_dir = None, positive_prop=None):
     train_transform = transforms.Compose(
         [transforms.Resize((224, 224)),
          Threshold_noise(noise_threshold),
@@ -154,8 +165,8 @@ def load_data_duo(base_dir, batch_size, shuffle=True, noise_threshold=0, ref_dir
          transforms.Normalize(0.5, 0.5)])
     print('Default val transform')
 
-    train_dataset = ImageFolderDuo(root=base_dir, transform=train_transform, ref_dir = ref_dir)
-    val_dataset = ImageFolderDuo(root=base_dir, transform=val_transform, ref_dir = ref_dir)
+    train_dataset = ImageFolderDuo(root=base_dir, transform=train_transform, ref_dir = ref_dir, positive_prop=positive_prop)
+    val_dataset = ImageFolderDuo(root=base_dir, transform=val_transform, ref_dir = ref_dir, positive_prop=positive_prop)
     generator1 = torch.Generator().manual_seed(42)
     indices = torch.randperm(len(train_dataset), generator=generator1)
     val_size = len(train_dataset) // 5
diff --git a/image_ref/main.py b/image_ref/main.py
index 738ea392d06c6400a2394e5c6d5f47201f740277..8fadee97c46b88a953b88014e46d80721cd61777 100644
--- a/image_ref/main.py
+++ b/image_ref/main.py
@@ -192,7 +192,8 @@ def test_duo(model, data_test, loss_function, epoch):
 
 def run_duo(args):
     #load data
-    data_train, data_test = load_data_duo(base_dir=args.dataset_dir, batch_size=args.batch_size, ref_dir=args.dataset_ref_dir)
+    data_train, data_test = load_data_duo(base_dir=args.dataset_dir, batch_size=args.batch_size,
+                                          ref_dir=args.dataset_ref_dir, positive_prop=args.positive_prop)
     #load model
     model = Classification_model_duo_contrastive(model = args.model, n_class=2)
     model.double()