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[config] default stub install verbose

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mzwiessele 2015-10-10 14:56:20 +01:00
parent 6213e21ac9
commit acbd7c331f
1 changed files with 2 additions and 14 deletions
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16
GPy/testing/likelihood_tests.py
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@ -562,35 +562,23 @@ class TestNoiseModels(object):
np.random.seed(11111) np.random.seed(11111)
#Normalize #Normalize
Y = Y/Y.max() Y = Y/Y.max()
white_var = 1e-1
kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1]) kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
laplace_likelihood = GPy.inference.latent_function_inference.Laplace() 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 = GPy.core.GP(X.copy(), Y.copy(), kernel, likelihood=model, Y_metadata=Y_metadata, inference_method=laplace_likelihood)
#m['.*white'].constrain_fixed(white_var)
#Set constraints #Set constraints
for constrain_param, constraint in constraints: for constrain_param, constraint in constraints:
constraint(constrain_param, m) constraint(constrain_param, m)
#print(m)
#m.randomize()
m.randomize()
#Set params #Set params
for param_num in range(len(param_names)): for param_num in range(len(param_names)):
name = param_names[param_num] name = param_names[param_num]
m[name] = param_vals[param_num] m[name] = param_vals[param_num]
#m.optimize(max_iters=8)
#print(m)
#if not m.checkgrad(step=step):
#m.checkgrad(verbose=1, step=step)
#NOTE this test appears to be stochastic for some likelihoods (student t?)
# appears to all be working in test mode right now...
#if isinstance(model, GPy.likelihoods.StudentT):
try: try:
assert m.checkgrad(verbose=0, step=step) assert m.checkgrad(verbose=0)
except: except:
assert m.checkgrad(verbose=1) assert m.checkgrad(verbose=1)