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pickling and caching

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Max Zwiessele 2014-03-31 12:45:09 +01:00
parent 60a071f18f
commit f3b74fa85f
28 changed files with 481 additions and 686 deletions
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13
GPy/models/mrd.py
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@ -65,14 +65,17 @@ class MRD(Model):
from ..kern import RBF
self.kern = [RBF(input_dim, ARD=1, lengthscale=fracs[i], name='rbf'.format(i)) for i in range(len(Ylist))]
elif isinstance(kernel, Kern):
self.kern = [kernel.copy(name='{}'.format(kernel.name, i)) for i in range(len(Ylist))]
self.kern = []
for i in range(len(Ylist)):
k = kernel.copy()
self.kern.append(k)
else:
assert len(kernel) == len(Ylist), "need one kernel per output"
assert all([isinstance(k, Kern) for k in kernel]), "invalid kernel object detected!"
self.kern = kernel
if X_variance is None:
X_variance = np.random.uniform(0, .1, X.shape)
X_variance = np.random.uniform(0.1, 0.2, X.shape)
self.variational_prior = NormalPrior()
self.X = NormalPosterior(X, X_variance)
@ -108,8 +111,8 @@ class MRD(Model):
def parameters_changed(self):
self._log_marginal_likelihood = 0
self.posteriors = []
self.Z.gradient = 0.
self.X.gradient = 0.
self.Z.gradient[:] = 0.
self.X.gradient[:] = 0.
for y, k, l, i in itertools.izip(self.Ylist, self.kern, self.likelihood, self.inference_method):
posterior, lml, grad_dict = i.inference(k, self.X, self.Z, l, y)
@ -160,6 +163,8 @@ class MRD(Model):
X = np.random.randn(Ylist[0].shape[0], self.input_dim)
fracs = X.var(0)
fracs = [fracs]*self.input_dim
X -= X.mean()
X /= X.std()
return X, fracs
def _init_Z(self, init="permute", X=None):