[baseline] tests
|
|
@ -124,12 +124,16 @@ def test_threed():
|
|||
f = .2 * np.sin(1.3*X[:,[0]]) + 1.3*np.cos(2*X[:,[1]])
|
||||
Y = f+np.random.normal(0, .1, f.shape)
|
||||
m = GPy.models.GPRegression(X, Y)
|
||||
m.optimize()
|
||||
m.plot_data(projection='3d')
|
||||
m.plot_mean(projection='3d')
|
||||
m.likelihood.variance = .1
|
||||
#m.optimize()
|
||||
m.plot_samples(projection='3d', samples=1)
|
||||
m.plot_samples(projection='3d', plot_raw=False, samples=1)
|
||||
for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean", "samples", "samples_lik"]], extensions=extensions):
|
||||
plt.close('all')
|
||||
m.plot_data(projection='3d')
|
||||
m.plot_mean(projection='3d')
|
||||
for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean",
|
||||
#"samples", "samples_lik"
|
||||
]], extensions=extensions):
|
||||
yield (do_test, )
|
||||
|
||||
def test_sparse():
|
||||
|
|
|
|||
|
Before Width: | Height: | Size: 23 KiB After Width: | Height: | Size: 22 KiB |
|
Before Width: | Height: | Size: 22 KiB |
|
Before Width: | Height: | Size: 25 KiB |
|
Before Width: | Height: | Size: 9.7 KiB After Width: | Height: | Size: 9.7 KiB |
|
Before Width: | Height: | Size: 13 KiB After Width: | Height: | Size: 13 KiB |
|
Before Width: | Height: | Size: 25 KiB After Width: | Height: | Size: 25 KiB |
|
Before Width: | Height: | Size: 24 KiB After Width: | Height: | Size: 24 KiB |