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fix: Fixed numpy 1.12 indexing and shape preservation

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mzwiessele 2017-02-23 14:45:18 +00:00 • committed by Akash Kumar Dhaka
parent a7a2e53f6d
commit 96cc0838eb
9 changed files with 26 additions and 25 deletions
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3
GPy/testing/likelihood_tests.py
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@ -119,7 +119,7 @@ class TestNoiseModels(object):
self.integer_Y = np.where(tmp > 0, tmp, 0)
self.ns = np.random.poisson(50, size=self.N)[:, None]
p = np.abs(np.cos(2*np.pi*self.X + np.random.normal(scale=.2, size=(self.N, self.D)))).mean(1)
self.binomial_Y = np.array([np.random.binomial(self.ns[i], p[i]) for i in range(p.shape[0])])[:, None]
self.binomial_Y = np.array([np.random.binomial(int(self.ns[i]), p[i]) for i in range(p.shape[0])])[:, None]
self.var = 0.2
self.deg_free = 4.0
@ -570,7 +570,6 @@ class TestNoiseModels(object):
white_var = 1e-4
kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
laplace_likelihood = GPy.inference.latent_function_inference.Laplace()
m = GPy.core.GP(X.copy(), Y.copy(), kernel, likelihood=model, Y_metadata=Y_metadata, inference_method=laplace_likelihood)
m.kern.white.constrain_fixed(white_var)

2
GPy/testing/model_tests.py
View file

@ -54,7 +54,7 @@ class MiscTests(unittest.TestCase):
m.randomize()
m.optimize()
# Compute the mean of model prediction on 1e5 Monte Carlo samples
Xp = np.random.uniform(size=(1e5,2))
Xp = np.random.uniform(size=(int(1e5),2))
Xp[:,0] = Xp[:,0]*15-5
Xp[:,1] = Xp[:,1]*15
_, var = m.predict(Xp)