# Test for bandwidth parameter # To see if our output dictionary has the means to approximate well the output functions muout = 0.1 testridge = rffridge.RFFRidge(muout, rffs) i = 6 testridge.fit(Vtrain["x"][i], Vtrain["y"][i]) pred = testridge.predict(timevec.reshape((501, 1))) plt.figure() plt.plot(timevec, pred, label="predicted") plt.plot(Vtrain["x"][i], Vtrain["y"][i], label="real") plt.legend() # Fit # Build regressor l2 = losses.L2Loss() lamb = 0.001 reg = coefsoncoefs.CoefsOnCoefs(kernels.GaussianKernel(sigma=3), rffsx, musmoothing, rffs, muout, lamb) # Fit regressor reg.fit(Xtrain, Vtrain) # Predict pred = reg.predict(Xtest, timevec) i = 4 # Pred on test set plt.figure() plt.plot(timevec.flatten(), pred[i, :], label="predicted") plt.plot(timevec.flatten(), Vtest["y"][i], label="real")
test = np.array([data["y"][i][20] for i in range(data.get_T())]) # # Kernels for convolution # gausskerx = kernels.GaussianKernel(sigma=10) # gausskery = kernels.GaussianKernel(sigma=0.2) # # Compute kernel matrices # Kx = gausskerx.compute_K(data["x_flat"]) # Ky = gausskery.compute_K(data["y_flat"]) # convkers = kernels.ConvKernel(gausskerx, gausskery, Kx, Ky) # # # Compute convolution kernel matrix # Ks = convkers.compute_K_from_mat(Ms) # Define loss loss = losses.L2Loss() # Define regularizers and regularization params spacereg = regularizers.TikhonovSpace() timereg = regularizers.TikhonovTime() mu = 0.001 lamb = 0.001 # Train/Test split ntrain = 10 Strain_input = data.extract_subseq(0, 10) Strain_output = data.extract_subseq(1, 11) Strain = data.extract_subseq(0, 11) # Kernels for convolution gausskerx = kernels.GaussianKernel(sigma=10)
