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working on SGD merge

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Nicolo Fusi 2013-02-15 12:39:45 +00:00
parent 7efe14c329
commit c40c83a191
2 changed files with 11 additions and 8 deletions
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13
GPy/inference/SGD.py
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@ -198,8 +198,9 @@ class opt_SGD(Optimizer):
for j in features:
count += 1
self.model.D = len(j)
self.model.likelihood.D = self.model.D
self.model.likelihood.Y = Y[:, j]
# self.model.trYYT = np.sum(np.square(self.model.Y))
self.model.likelihood.YYT = np.dot(self.model.likelihood.Y, self.model.likelihood.Y.T)
if missing_data or sparse_matrix:
shapes = self.get_param_shapes(N, Q)
f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes, sparse_matrix)
@ -216,8 +217,8 @@ class opt_SGD(Optimizer):
# TODO: remove this, it's only for debugging
if self.model.__class__.__name__ == 'Bayesian_GPLVM':
beta = np.exp(self.x_opt)[-8]
status = "evaluating {feature: 5d}/{tot: 5d} \t f: {f: 2.3f} \t non-missing: {nm: 4d} \t inv_bbeta: {beta: 1.5f}\r".format(feature = count, tot = len(features), f = f, nm = Nj, beta = 1./beta)
beta = np.exp(self.x_opt)[-1]
status = "evaluating {feature: 5d}/{tot: 5d} \t f: {f: 2.3f} \t non-missing: {nm: 4d} \t inv_bbeta: {beta: 1.5f}\r".format(feature = count, tot = len(features), f = f, nm = Nj, beta = beta)
sys.stdout.write(status)
sys.stdout.flush()
@ -228,10 +229,12 @@ class opt_SGD(Optimizer):
# should really be a sum(), but earlier samples in the iteration will have a very crappy ll
self.f_opt = np.mean(NLL)
self.model.N = N
self.model.likelihood.N = N
self.model.likelihood.Y = Y
self.model.X = X
self.model.D = D
self.model.likelihood.N = N
self.model.likelihood.N = N
self.model.likelihood.D = D
# self.model.Youter = np.dot(Y, Y.T)
self.trace.append(self.f_opt)
if self.messages != 0:

6
GPy/models/BGPLVM.py
View file

@ -22,8 +22,9 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
:type init: 'PCA'|'random'
"""
def __init__(self, Y, Q, init='PCA', M=10, Z=None, kernel=None, **kwargs):
X = self.initialise_latent(init, Q, Y)
def __init__(self, Y, Q, X = None, init='PCA', M=10, Z=None, kernel=None, **kwargs):
if X == None:
X = self.initialise_latent(init, Q, Y)
if Z is None:
Z = np.random.permutation(X.copy())[:M]
@ -70,4 +71,3 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
def _log_likelihood_gradients(self):
return np.hstack((self.dL_dmuS().flatten(), sparse_GP._log_likelihood_gradients(self)))