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re-merged. only RA's errors (probit?) remain

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James Hensman 2013-06-04 17:38:05 +01:00
parent 29790e327a
commit 6d64559f1f
2 changed files with 6 additions and 6 deletions
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4
GPy/core/model.py
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@ -20,7 +20,7 @@ from GPy.core.domains import POSITIVE, REAL
class model(parameterised): class model(parameterised):
def __init__(self): def __init__(self):
parameterised.__init__(self) parameterised.__init__(self)
self.priors = [None for i in range(self._get_params().size)] self.priors = None
self.optimization_runs = [] self.optimization_runs = []
self.sampling_runs = [] self.sampling_runs = []
self.preferred_optimizer = 'tnc' self.preferred_optimizer = 'tnc'
@ -55,7 +55,7 @@ class model(parameterised):
if self.priors is None: if self.priors is None:
self.priors = [None for i in range(self._get_params().size)] self.priors = [None for i in range(self._get_params().size)]
which = self.grep_param_names(which) which = self.grep_param_names(regexp)
# check tied situation # check tied situation
tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie) == set(which))] tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie) == set(which))]

8
GPy/testing/prior_tests.py
View file

@ -15,12 +15,12 @@ class PriorTests(unittest.TestCase):
X, y = X[:, None], y[:, None] X, y = X[:, None], y[:, None]
m = GPy.models.GP_regression(X, y) m = GPy.models.GP_regression(X, y)
m.ensure_default_constraints() m.ensure_default_constraints()
lognormal = GPy.priors.log_Gaussian(1, 2) lognormal = GPy.priors.LogGaussian(1, 2)
m.set_prior('rbf', lognormal) m.set_prior('rbf', lognormal)
m.randomize() m.randomize()
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())
def test_gamma(self): def test_Gamma(self):
xmin, xmax = 1, 2.5*np.pi xmin, xmax = 1, 2.5*np.pi
b, C, SNR = 1, 0, 0.1 b, C, SNR = 1, 0, 0.1
X = np.linspace(xmin, xmax, 500) X = np.linspace(xmin, xmax, 500)
@ -29,8 +29,8 @@ class PriorTests(unittest.TestCase):
X, y = X[:, None], y[:, None] X, y = X[:, None], y[:, None]
m = GPy.models.GP_regression(X, y) m = GPy.models.GP_regression(X, y)
m.ensure_default_constraints() m.ensure_default_constraints()
gamma = GPy.priors.gamma(1, 1) Gamma = GPy.priors.Gamma(1, 1)
m.set_prior('rbf', gamma) m.set_prior('rbf', Gamma)
m.randomize() m.randomize()
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())