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made it clear that we are working with -f(x)

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Nicolo Fusi 2013-02-01 14:53:48 +00:00
parent 4c963d1a67
commit 643abdcec7
1 changed files with 6 additions and 6 deletions
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12
GPy/inference/SGD.py
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@ -165,18 +165,18 @@ class opt_SGD(Optimizer):
if it == 0 or self.self_paced is False: if it == 0 or self.self_paced is False:
features = np.random.permutation(Y.shape[1]) features = np.random.permutation(Y.shape[1])
else: else:
features = np.argsort(LL)[::-1] features = np.argsort(NLL)#[::-1]
b = len(features)/self.batch_size b = len(features)/self.batch_size
features = [features[i::b] for i in range(b)] features = [features[i::b] for i in range(b)]
LL = [] NLL = []
count = 0 count = 0
for j in features: for j in features:
count += 1 count += 1
self.model.D = len(j) self.model.D = len(j)
self.model.Y = Y[:, j] self.model.Y = Y[:, j]
self.model.trYYT = np.sum(np.square(self.model.Y)) # self.model.trYYT = np.sum(np.square(self.model.Y))
if missing_data: if missing_data:
shapes = self.get_param_shapes(N, Q) shapes = self.get_param_shapes(N, Q)
f, step, Nj = self.step_with_missing_data(f_fp, X, Y, step, shapes) f, step, Nj = self.step_with_missing_data(f_fp, X, Y, step, shapes)
@ -188,14 +188,14 @@ class opt_SGD(Optimizer):
self.x_opt -= step + momentum_term self.x_opt -= step + momentum_term
if self.messages == 2: 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) 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)
sys.stdout.write(status) sys.stdout.write(status)
sys.stdout.flush() sys.stdout.flush()
LL.append(-f) NLL.append(f)
# should really be a sum(), but earlier samples in the iteration will have a very crappy ll # should really be a sum(), but earlier samples in the iteration will have a very crappy ll
self.f_opt = np.mean(LL) self.f_opt = np.mean(NLL)
self.model.N = N self.model.N = N
self.model.Y = Y self.model.Y = Y
self.model.X = X self.model.X = X