plots

src/functions.py

@@ -216,7 +216,7 @@ def majority_vote_for_instances(labels_list):
     return majority_votes
 
 
-def plot_reconstructions(test_x, reconstruction, ind= 0):
+def plot_reconstructions(test_x, test_y, reconstruction, ind= 0):
         plt.rcParams["font.size"] = 24
 
         # plt.figure()
@@ -243,7 +243,6 @@ def plot_reconstructions(test_x, reconstruction, ind= 0):
         # plt.ylabel("Wind Speed")
         # plt.tight_layout()
 
-        ind=27
 
         plt.figure()
         plt.plot(test_x[ind, :, 0].numpy()-reconstruction[0, :, 0])

src/main.py

@@ -198,7 +198,7 @@ if __name__ == "__main__":
         )
 
 
-        plot_reconstructions(test_x=  testD, reconstruction=reconstruction)
+        plot_reconstructions(test_x=  testD, test_y= test_y, reconstruction=reconstruction)
 
         if model_name == "basic_autoencoder":
             loss_test = loss_test.reshape(

src/main_experimentation_kerasae.py

@@ -136,7 +136,8 @@ if __name__ == "__main__":
         recons_train = model.predict(trainD)
         reconstruction = model.predict(testD)
 
-        ind = 27
+        ind = 0
+        # ind = 27
 
         plt.rcParams["font.size"] = 24
 
@@ -163,8 +164,6 @@ if __name__ == "__main__":
         # plt.plot(test_y[ind*120:(ind+1)*120])
         # plt.tight_layout()
 
-
-
         plt.figure()
         plt.plot(testD[ind, :, 0] - reconstruction[0, :, 0], label="Reconstrucion")
         plt.xlabel("Time")
@@ -190,7 +189,6 @@ if __name__ == "__main__":
         plt.ylim(-0.4, 0.5)
         plt.tight_layout()
 
-
         loss_test = np.mean(np.abs(reconstruction - testD), axis=2).ravel()
 
         # plt.style.use("seaborn-v0_8")