From 2e609bd121df88124c29d5f4f91c1ef4a7fa926e Mon Sep 17 00:00:00 2001
From: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
Date: Sun, 10 Aug 2014 17:11:18 +0200
Subject: [PATCH] TST regression test for optimum over several random-starts of
GP
---
.../tests/test_gaussian_process.py | 24 +++++++++++++++++++
1 file changed, 24 insertions(+)
diff --git a/sklearn/gaussian_process/tests/test_gaussian_process.py b/sklearn/gaussian_process/tests/test_gaussian_process.py
index ef31237bb4..3085ede550 100644
--- a/sklearn/gaussian_process/tests/test_gaussian_process.py
+++ b/sklearn/gaussian_process/tests/test_gaussian_process.py
@@ -141,3 +141,27 @@ def test_no_normalize():
gp = GaussianProcess(normalize=False).fit(X, y)
y_pred = gp.predict(X)
assert_true(np.allclose(y_pred, y))
+
+
+def test_random_starts():
+ """
+ Test that an increasing number of random-starts of GP fitting only
+ increases the reduced likelihood function of the optimal theta.
+ """
+ n_input_dims = 3
+ n_samples = 100
+ np.random.seed(0)
+ X = np.random.random(n_input_dims*n_samples).reshape(n_samples,
+ n_input_dims) * 2 - 1
+ y = np.sin(X).sum(axis=1) + np.sin(3*X).sum(axis=1)
+ best_likelihood = -np.inf
+ for random_start in range(1, 10):
+ gp = GaussianProcess(regr="constant", corr="squared_exponential",
+ theta0=[1e-0]*n_input_dims,
+ thetaL=[1e-4]*n_input_dims,
+ thetaU=[1e+1]*n_input_dims,
+ random_start=random_start, random_state=0,
+ verbose=False).fit(X, y)
+ rlf = gp.reduced_likelihood_function()[0]
+ assert_true(rlf >= best_likelihood)
+ best_likelihood = rlf
--
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