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Fixing bernoulli likelihood for Laplace, fixing Zep for EP, and starting working on quadrature limits

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This commit is contained in:
Alan Saul 2015-10-19 19:29:57 +01:00
parent 6b6938bd11
commit 5b4abf4c34
8 changed files with 70 additions and 39 deletions
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9
GPy/testing/likelihood_tests.py
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@ -113,6 +113,7 @@ class TestNoiseModels(object):
self.Y = (np.sin(self.X[:, 0]*2*np.pi) + noise)[:, None]
self.f = np.random.rand(self.N, 1)
self.binary_Y = np.asarray(np.random.rand(self.N) > 0.5, dtype=np.int)[:, None]
self.binary_Y[self.binary_Y == 0.0] = -1.0
self.positive_Y = np.exp(self.Y.copy())
tmp = np.round(self.X[:, 0]*3-3)[:, None] + np.random.randint(0,3, self.X.shape[0])[:, None]
self.integer_Y = np.where(tmp > 0, tmp, 0)
@ -561,12 +562,14 @@ class TestNoiseModels(object):
print("\n{}".format(inspect.stack()[0][3]))
np.random.seed(111)
#Normalize
Y = Y/Y.max()
# Y = Y/Y.max()
white_var = 1e-5
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['.*white'].constrain_fixed(white_var)
m.randomize()
#Set constraints
@ -591,7 +594,7 @@ class TestNoiseModels(object):
print("\n{}".format(inspect.stack()[0][3]))
#Normalize
Y = Y/Y.max()
white_var = 1e-6
white_var = 1e-5
kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
ep_inf = GPy.inference.latent_function_inference.EP()