Update usage.rst - FIx other variables

docs/source/usage.rst

@@ -1336,75 +1336,75 @@ gets initialized which also allows the `prior` to be directly parametrized.
     import numpy as np
     import cebra.datasets
     import torch
-
+    
     if torch.cuda.is_available():
         device = "cuda"
     else:
         device = "cpu"
-
+    
     neural_data = cebra.load_data(file="neural_data.npz", key="neural")
-
+    
     discrete_label = cebra.load_data(
         file="auxiliary_behavior_data.h5", key="auxiliary_variables", columns=["discrete"],
     )
-
+    
     # 1. Define Cebra Dataset
     input_data = cebra.data.TensorDataset(
         torch.from_numpy(neural_data).type(torch.FloatTensor),
         discrete=torch.from_numpy(np.array(discrete_label[:, 0])).type(torch.LongTensor),
     ).to(device)
-
+    
     # 2. Define Cebra Model
     neural_model = cebra.models.init(
         name="offset10-model",
-        num_neurons=InputData.input_dimension,
+        num_neurons=input_data.input_dimension,
         num_units=32,
         num_output=2,
     ).to(device)
-
-    InputData.configure_for(neural_model)
-
+    
+    input_data.configure_for(neural_model)
+    
     # 3. Define Loss Function Criterion and Optimizer
-    Crit = cebra.models.criterions.LearnableCosineInfoNCE(
+    crit = cebra.models.criterions.LearnableCosineInfoNCE(
         temperature=0.001,
         min_temperature=0.0001
     ).to(device)
-
-    Opt = torch.optim.Adam(
-        list(neural_model.parameters()) + list(Crit.parameters()),
+    
+    opt = torch.optim.Adam(
+        list(neural_model.parameters()) + list(crit.parameters()),
         lr=0.001,
         weight_decay=0,
     )
-
+    
     # 4. Initialize Cebra Model
     solver = cebra.solver.init(
         name="single-session",
         model=neural_model,
-        criterion=Crit,
-        optimizer=Opt,
+        criterion=crit,
+        optimizer=opt,
         tqdm_on=True,
     ).to(device)
-
+    
     # 5. Define Data Loader
     loader = cebra.data.single_session.DiscreteDataLoader(
-        dataset=InputData, num_steps=10, batch_size=200, prior="uniform"
+        dataset=input_data, num_steps=10, batch_size=200, prior="uniform"
     )
-
+    
     # 6. Fit Model
     solver.fit(loader=loader)
-
+    
     # 7. Transform Embedding
-    TrainBatches = np.lib.stride_tricks.sliding_window_view(
+    train_batches = np.lib.stride_tricks.sliding_window_view(
         neural_data, neural_model.get_offset().__len__(), axis=0
     )
-
-    X_train_emb = solver.transform(
-        torch.from_numpy(TrainBatches[:]).type(torch.FloatTensor).to(device)
+    
+    x_train_emb = solver.transform(
+        torch.from_numpy(train_batches[:]).type(torch.FloatTensor).to(device)
     ).to(device)
-
+    
     # 8. Plot Embedding
     cebra.plot_embedding(
-        X_train_emb,
+        x_train_emb,
         discrete_label[neural_model.get_offset().__len__() - 1 :, 0],
         markersize=10,
     )