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some minor changes in SGD

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Nicolo Fusi 2013-02-15 14:30:52 +00:00
parent eddb677446
commit 151c426a3a
1 changed files with 5 additions and 17 deletions
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22
GPy/inference/SGD.py
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@ -175,19 +175,16 @@ class opt_SGD(Optimizer):
missing_data = True
if not sparse_matrix:
missing_data = self.check_for_missing(self.model.likelihood.Y)
self.model.likelihood.YYT = None
num_params = self.model._get_params()
step = np.zeros_like(num_params)
if self.center:
print "WARNING: centering the data"
for it in range(self.iterations):
if it == 0 or self.self_paced is False:
features = np.random.permutation(Y.shape[1])
else:
features = np.argsort(NLL)#[::-1]
features = np.argsort(NLL)
b = len(features)/self.batch_size
features = [features[i::b] for i in range(b)]
@ -198,9 +195,7 @@ 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.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)
@ -213,14 +208,8 @@ class opt_SGD(Optimizer):
self.x_opt -= step + momentum_term
if self.messages == 2:
status = "evaluating {feature: 5d}/{tot: 5d} \t f: {f: 2.3f} \t non-missing: {nm: 4d}\r".format(feature = count, tot = len(features), f = f, nm = Nj)
# TODO: remove this, it's only for debugging
if self.model.__class__.__name__ == 'Bayesian_GPLVM':
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)
noise = np.exp(self.x_opt)[-1]
status = "evaluating {feature: 5d}/{tot: 5d} \t f: {f: 2.3f} \t non-missing: {nm: 4d}\t noise: {noise: 2.4f}\r".format(feature = count, tot = len(features), f = f, nm = Nj, noise = noise)
sys.stdout.write(status)
sys.stdout.flush()
last_printed_count = count
@ -233,8 +222,7 @@ class opt_SGD(Optimizer):
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.likelihood.Y = Y
# self.model.Youter = np.dot(Y, Y.T)
self.trace.append(self.f_opt)