diff --git a/src/functions.py b/src/functions.py index ff75b777fe8f6844af3293195b9e19653c6532cd..a83cdbf030568d09746196131f9c88f56879e44d 100644 --- a/src/functions.py +++ b/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]) diff --git a/src/main.py b/src/main.py index e1e7cc18404fe7d6cd423e073943aeba8b1f2426..07c7a73588f75aa3d46c28433ab103597419897b 100644 --- a/src/main.py +++ b/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( diff --git a/src/main_experimentation_kerasae.py b/src/main_experimentation_kerasae.py index fd76b418a3f3b8da321175655ec5f377a36d196c..3ad742aabba07be3ba24d5e691198c67205f620d 100644 --- a/src/main_experimentation_kerasae.py +++ b/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 @@ -159,38 +160,35 @@ if __name__ == "__main__": # plt.plot(testD[ind, :, 2], label="Original") # plt.plot(reconstruction[0, :, 2], label="Reconstructed") # plt.xlabel("Time") - # plt.ylabel("Wind Speed") + # plt.ylabel("Wind Speed") # 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.plot(testD[ind, :, 0] - reconstruction[0, :, 0], label="Reconstrucion") plt.xlabel("Time") plt.ylabel("Torque Average") - plt.plot(test_y[ind*120:(ind+1)*120]) + plt.plot(test_y[ind * 120 : (ind + 1) * 120]) plt.ylim(-0.4, 0.5) plt.tight_layout() plt.figure() - plt.plot(testD[ind, :, 1]-reconstruction[0, :, 1], label="Original") + plt.plot(testD[ind, :, 1] - reconstruction[0, :, 1], label="Original") plt.xlabel("Time") plt.ylabel("Nacelle Temp") - plt.plot(test_y[ind*120:(ind+1)*120]) + plt.plot(test_y[ind * 120 : (ind + 1) * 120]) plt.ylim(-0.4, 0.5) plt.tight_layout() plt.figure() - plt.plot(testD[ind, :, 2]-reconstruction[0, :, 2], label="Original") + plt.plot(testD[ind, :, 2] - reconstruction[0, :, 2], label="Original") plt.xlabel("Time") plt.ylabel("Wind Speed") - plt.plot(test_y[ind*120:(ind+1)*120]) + plt.plot(test_y[ind * 120 : (ind + 1) * 120]) 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")