|
20 | 20 |
|
21 | 21 | from sklearn.utils.testing import (assert_equal, assert_almost_equal,
|
22 | 22 | assert_not_equal, assert_array_equal,
|
23 |
| - assert_array_almost_equal) |
| 23 | + assert_array_almost_equal, assert_true) |
24 | 24 |
|
25 | 25 |
|
26 | 26 | X = np.random.RandomState(0).normal(0, 1, (5, 2))
|
|
41 | 41 | 4.0 * Matern(length_scale=[0.5, 0.5], nu=2.5),
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42 | 42 | RationalQuadratic(length_scale=0.5, alpha=1.5),
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43 | 43 | ExpSineSquared(length_scale=0.5, periodicity=1.5),
|
44 |
| - DotProduct(sigma_0=2.0), DotProduct(sigma_0=2.0) ** 2] |
| 44 | + DotProduct(sigma_0=2.0), DotProduct(sigma_0=2.0) ** 2, |
| 45 | + RBF(length_scale=[2.0]), Matern(length_scale=[2.0])] |
45 | 46 | for metric in PAIRWISE_KERNEL_FUNCTIONS:
|
46 | 47 | if metric in ["additive_chi2", "chi2"]:
|
47 | 48 | continue
|
@@ -306,10 +307,8 @@ def test_set_get_params():
|
306 | 307 | index += 1
|
307 | 308 |
|
308 | 309 |
|
309 |
| -def test_repr_kernels_isotropic_1D_length_scale(): |
310 |
| - """Test that repr works on isotropic kernels with a 1-D length_scale""" |
311 |
| - matern = Matern(length_scale=[1.2]) |
312 |
| - assert_equal(repr(matern), "Matern(length_scale=1.2, nu=1.5)") |
| 310 | +def test_repr_kernels(): |
| 311 | + """Smoke-test for repr in kernels.""" |
313 | 312 |
|
314 |
| - rbf = RBF(length_scale=[1.2]) |
315 |
| - assert_equal(repr(rbf), "RBF(length_scale=1.2)") |
| 313 | + for kernel in kernels: |
| 314 | + kernel_repr = repr(kernel) |
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