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Merge branch 'master' of github.com:SheffieldML/GPy

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James Hensman 2013-02-19 14:44:50 +00:00
commit 7f1503da4a
5 changed files with 7 additions and 6 deletions
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3
GPy/core/model.py
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@ -294,7 +294,8 @@ class model(parameterised):
strs = [str(p) if p is not None else '' for p in self.priors] strs = [str(p) if p is not None else '' for p in self.priors]
width = np.array(max([len(p) for p in strs] + [5])) + 4 width = np.array(max([len(p) for p in strs] + [5])) + 4
s[0] = 'Marginal log-likelihood: {0:.3e}\n'.format(self.log_likelihood()) + s[0] MLL = self.log_likelihood() + self.log_prior()
s[0] = 'Marginal log-likelihood: {0:.3e}\n'.format(MLL) + s[0]
s[0] += "|{h:^{col}}".format(h = 'Prior', col = width) s[0] += "|{h:^{col}}".format(h = 'Prior', col = width)
s[1] += '-'*(width + 1) s[1] += '-'*(width + 1)

4
GPy/models/BGPLVM.py
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@ -40,8 +40,8 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
sparse_GP.__init__(self, X, Gaussian(Y), kernel, Z=Z, X_uncertainty=S, **kwargs) sparse_GP.__init__(self, X, Gaussian(Y), kernel, Z=Z, X_uncertainty=S, **kwargs)
def _get_param_names(self): def _get_param_names(self):
X_names = sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[]) X_names = sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
S_names = sum([['S_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[]) S_names = sum([['S_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
return (X_names + S_names + sparse_GP._get_param_names(self)) return (X_names + S_names + sparse_GP._get_param_names(self))
def _get_params(self): def _get_params(self):

2
GPy/models/GPLVM.py
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@ -38,7 +38,7 @@ class GPLVM(GP):
return np.random.randn(Y.shape[0], Q) return np.random.randn(Y.shape[0], Q)
def _get_param_names(self): def _get_param_names(self):
return sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[]) + GP._get_param_names(self) return sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[]) + GP._get_param_names(self)
def _get_params(self): def _get_params(self):
return np.hstack((self.X.flatten(), GP._get_params(self))) return np.hstack((self.X.flatten(), GP._get_params(self)))

2
GPy/models/sparse_GP.py
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@ -148,7 +148,7 @@ class sparse_GP(GP):
return np.hstack([self.Z.flatten(),GP._get_params(self)]) return np.hstack([self.Z.flatten(),GP._get_params(self)])
def _get_param_names(self): def _get_param_names(self):
return sum([['iip_%i_%i'%(i,j) for i in range(self.Z.shape[0])] for j in range(self.Z.shape[1])],[]) + GP._get_param_names(self) return sum([['iip_%i_%i'%(i,j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])],[]) + GP._get_param_names(self)
def log_likelihood(self): def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """ """ Compute the (lower bound on the) log marginal likelihood """

2
GPy/models/sparse_GPLVM.py
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@ -28,7 +28,7 @@ class sparse_GPLVM(sparse_GP_regression, GPLVM):
sparse_GP_regression.__init__(self, X, Y, **kwargs) sparse_GP_regression.__init__(self, X, Y, **kwargs)
def _get_param_names(self): def _get_param_names(self):
return (sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[]) return (sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
+ sparse_GP_regression._get_param_names(self)) + sparse_GP_regression._get_param_names(self))
def _get_params(self): def _get_params(self):