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mrd and bgplvm updates to conform new vardtc

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Max Zwiessele 2014-03-24 13:33:16 +00:00
parent 3db095338d
commit 1294c24a28
3 changed files with 38 additions and 21 deletions
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34
GPy/models/mrd.py
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@ -51,24 +51,25 @@ class MRD(Model):
inference_method=None, likelihood=None, name='mrd', Ynames=None):
super(MRD, self).__init__(name)
self.input_dim = input_dim
self.num_inducing = num_inducing
self.Ylist = Ylist
self._in_init_ = True
X, fracs = self._init_X(initx, Ylist)
self.Z = Param('inducing inputs', self._init_Z(initz, X))
self.num_inducing = self.Z.shape[0] # ensure M==N if M>N
# sort out the kernels
if kernel is None:
from ..kern import RBF
self.kern = [RBF(input_dim, ARD=1, name='rbf'.format(i)) for i in range(len(Ylist))]
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))]
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
self.input_dim = input_dim
self.num_inducing = num_inducing
self.Ylist = Ylist
self._in_init_ = True
X = self._init_X(initx, Ylist)
self.Z = Param('inducing inputs', self._init_Z(initz, X))
self.num_inducing = self.Z.shape[0] # ensure M==N if M>N
if X_variance is None:
X_variance = np.random.uniform(0, .1, X.shape)
@ -108,8 +109,7 @@ class MRD(Model):
self._log_marginal_likelihood = 0
self.posteriors = []
self.Z.gradient = 0.
self.X.mean.gradient = 0.
self.X.variance.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)
@ -147,14 +147,20 @@ class MRD(Model):
if Ylist is None:
Ylist = self.Ylist
if init in "PCA_concat":
X = initialize_latent('PCA', np.hstack(Ylist), self.input_dim)
X, fracs = initialize_latent('PCA', self.input_dim, np.hstack(Ylist))
fracs = [fracs]*self.input_dim
elif init in "PCA_single":
X = np.zeros((Ylist[0].shape[0], self.input_dim))
fracs = []
for qs, Y in itertools.izip(np.array_split(np.arange(self.input_dim), len(Ylist)), Ylist):
X[:, qs] = initialize_latent('PCA', Y, len(qs))
x,frcs = initialize_latent('PCA', len(qs), Y)
X[:, qs] = x
fracs.append(frcs)
else: # init == 'random':
X = np.random.randn(Ylist[0].shape[0], self.input_dim)
return X
fracs = X.var(0)
fracs = [fracs]*self.input_dim
return X, fracs
def _init_Z(self, init="permute", X=None):
if X is None: