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Update plotting_tests.py

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Max Zwiessele 2015-10-04 00:55:22 +01:00
parent cb18d151fb
commit c07f3dbe98
1 changed files with 57 additions and 0 deletions
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GPy/testing/plotting_tests.py
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@ -77,6 +77,9 @@ def testPlot():
m.optimize()
m.plot_data(ax=ax)
m.plot_mean(ax=ax)
m.plot_mean(ax=ax, plot_raw=True)
m.plot_mean(ax=ax, apply_link=True)
m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
m.plot_confidence(ax=ax)
m.plot_density(ax=ax)
return ax
@ -93,8 +96,17 @@ def testPlotClassification():
m.optimize()
m.plot_data(ax=ax)
m.plot_mean(ax=ax)
m.plot_mean(ax=ax, plot_raw=True)
m.plot_mean(ax=ax, apply_link=True)
m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
m.plot_confidence(ax=ax)
m.plot_confidence(ax=ax, plot_raw=True)
m.plot_confidence(ax=ax, apply_link=True)
m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
m.plot_density(ax=ax)
m.plot_density(ax=ax, plot_raw=True)
m.plot_density(ax=ax, apply_link=True)
m.plot_density(ax=ax, plot_raw=True, apply_link=True)
return ax
@image_comparison(baseline_images=['sparse_gp_class'], extensions=['pdf','png'])
@ -108,8 +120,18 @@ def testPlotSparseClassification():
m.optimize()
m.plot_data(ax=ax)
m.plot_mean(ax=ax)
m.plot_mean(ax=ax, plot_raw=True)
m.plot_mean(ax=ax, apply_link=True)
m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
m.plot_confidence(ax=ax)
m.plot_confidence(ax=ax, plot_raw=True)
m.plot_confidence(ax=ax, apply_link=True)
m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
m.plot_density(ax=ax)
m.plot_density(ax=ax, plot_raw=True)
m.plot_density(ax=ax, apply_link=True)
m.plot_density(ax=ax, plot_raw=True, apply_link=True)
m.plot_inducing(ax=ax)
return ax
@image_comparison(baseline_images=['sparse_gp'], extensions=['pdf','png'])
@ -123,6 +145,41 @@ def testPlotSparse():
m.optimize()
m.plot_data(ax=ax)
m.plot_mean(ax=ax)
m.plot_mean(ax=ax, plot_raw=True)
m.plot_mean(ax=ax, apply_link=True)
m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
m.plot_confidence(ax=ax)
m.plot_confidence(ax=ax, plot_raw=True)
m.plot_confidence(ax=ax, apply_link=True)
m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
m.plot_density(ax=ax)
m.plot_density(ax=ax, plot_raw=True)
m.plot_density(ax=ax, apply_link=True)
m.plot_density(ax=ax, plot_raw=True, apply_link=True)
m.plot_inducing(ax=ax)
return ax
@image_comparison(baseline_images=['sparse_latent'], extensions=['pdf','png'])
def testPlotSparse():
fig, ax = plt.subplots()
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
Y = f+np.random.normal(0, .1, f.shape)
m = GPy.models.SparseGPRegression(X, Y)
m.optimize()
m.plot_data(ax=ax)
m.plot_mean(ax=ax)
m.plot_mean(ax=ax, plot_raw=True)
m.plot_mean(ax=ax, apply_link=True)
m.plot_mean(ax=ax, plot_raw=True, apply_link=True)
m.plot_confidence(ax=ax)
m.plot_confidence(ax=ax, plot_raw=True)
m.plot_confidence(ax=ax, apply_link=True)
m.plot_confidence(ax=ax, plot_raw=True, apply_link=True)
m.plot_density(ax=ax)
m.plot_density(ax=ax, plot_raw=True)
m.plot_density(ax=ax, apply_link=True)
m.plot_density(ax=ax, plot_raw=True, apply_link=True)
m.plot_inducing(ax=ax)
return ax