DOC accelerate plot_kernel_ridge_regression.py
@@ -45,7 +45,7 @@ from sklearn.model_selection import learning_curve
from sklearn.kernel_ridge import KernelRidge
import matplotlib.pyplot as plt
-rng = np.random.RandomState(0)
+rng = np.random.RandomState(42)
# #############################################################################
# Generate sample data
@@ -128,10 +128,10 @@ plt.figure()
X = 5 * rng.rand(10000, 1)
y = np.sin(X).ravel()
y[::5] += 3 * (0.5 - rng.rand(X.shape[0] // 5))
-sizes = np.logspace(1, 4, 7).astype(int)
+sizes = np.logspace(1, 3.8, 7).astype(int)
for name, estimator in {
- "KRR": KernelRidge(kernel="rbf", alpha=0.1, gamma=10),
- "SVR": SVR(kernel="rbf", C=1e1, gamma=10),
+ "KRR": KernelRidge(kernel="rbf", alpha=0.01, gamma=10),
+ "SVR": SVR(kernel="rbf", C=1e2, gamma=10),
}.items():
train_time = []
test_time = []
