Update.

diff --git a/ddpol.py b/ddpol.py
index db587fa..9a1bbc9 100755 (executable)
--- a/ddpol.py
+++ b/ddpol.py
@@ -39,10 +39,7 @@ def fit_alpha(x, y, D, a = 0, b = 1, rho = 1e-12):
 ######################################################################
 
 def phi(x):
-    # return 4 * (x - 0.6) ** 2 * (x >= 0.6) - 4 * (x - 0.4) ** 2 * (x <= 0.4) + 0.5
-    # return 4 * (x - 0.5) ** 2 * (x >= 0.5)
     return torch.abs(torch.abs(x - 0.4) - 0.2) + x/2 - 0.1
-    # return x/2 - torch.sign(x-0.4) * 0.3
 
 ######################################################################
 
@@ -53,7 +50,6 @@ mse_test = torch.zeros(nb_runs, D_max + 1)
 
 for k in range(nb_runs):
     x_train = torch.rand(nb_train_samples, dtype = torch.float64)
-    # x_train = torch.linspace(0, 1, nb_train_samples, dtype = torch.float64)
     y_train = phi(x_train)
     if train_noise_std > 0:
         y_train = y_train + torch.empty_like(y_train).normal_(0, train_noise_std)
@@ -90,10 +86,9 @@ fig.savefig('dd-mse.pdf', bbox_inches='tight')
 ######################################################################
 # Plot some examples of train / test
 
-torch.manual_seed(5) # I picked that for pretty
+torch.manual_seed(9) # I picked that for pretty
 
 x_train = torch.rand(nb_train_samples, dtype = torch.float64)
-# x_train = torch.linspace(0, 1, nb_train_samples, dtype = torch.float64)
 y_train = phi(x_train)
 if train_noise_std > 0:
     y_train = y_train + torch.empty_like(y_train).normal_(0, train_noise_std)
@@ -106,8 +101,8 @@ for D in range(D_max + 1):
     ax = fig.add_subplot(1, 1, 1)
     ax.set_title(f'Degree {D}')
     ax.set_ylim(-0.1, 1.1)
-    ax.plot(x_test, y_test, color = 'blue', label = 'Test values')
-    ax.scatter(x_train, y_train, color = 'blue', label = 'Training examples')
+    ax.plot(x_test, y_test, color = 'black', label = 'Test values')
+    ax.scatter(x_train, y_train, color = 'blue', label = 'Train samples')
 
     alpha = fit_alpha(x_train, y_train, D)
     ax.plot(x_test, pol_value(alpha, x_test), color = 'red', label = 'Fitted polynomial')