Cleanup.

diff --git a/ddpol.py b/ddpol.py
index 35d98a0..9d14d2a 100755 (executable)
--- a/ddpol.py
+++ b/ddpol.py
@@ -51,7 +51,7 @@ def fit_alpha(x, y, D, a = 0, b = 1, rho = 1e-12):
         beta = x.new_zeros(D + 1, D + 1)
         beta[2:, 2:] = (q-1) * q * (r-1) * r * (b**(q+r-3) - a**(q+r-3))/(q+r-3)
         l, U = beta.eig(eigenvectors = True)
-        Q = U @ torch.diag(l[:, 0].clamp(min = 0) ** 0.5)
+        Q = U @ torch.diag(l[:, 0].clamp(min = 0) ** 0.5) # clamp deals with ~0 negative values
         B = torch.cat((B, y.new_zeros(Q.size(0))), 0)
         M = torch.cat((M, math.sqrt(rho) * Q.t()), 0)
 
@@ -96,14 +96,14 @@ ax.set_ylabel('MSE', labelpad = 10)
 
 ax.axvline(x = args.nb_train_samples - 1,
            color = 'gray', linewidth = 0.5, linestyle = '--')
+
 ax.text(args.nb_train_samples - 1.2, 1e-4, 'Nb. params = nb. samples',
         fontsize = 10, color = 'gray',
         rotation = 90, rotation_mode='anchor')
 
 mse_train, mse_test = compute_mse(args.nb_train_samples)
-
-ax.plot(torch.arange(args.D_max + 1), mse_train, color = 'blue', label = 'Train error')
-ax.plot(torch.arange(args.D_max + 1), mse_test, color = 'red', label = 'Test error')
+ax.plot(torch.arange(args.D_max + 1), mse_train, color = 'blue', label = 'Train')
+ax.plot(torch.arange(args.D_max + 1), mse_test, color = 'red', label = 'Test')
 
 ax.legend(frameon = False)