apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-18 13:55:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Replaced Q by input_dim

Browse source
This commit is contained in:
Alan Saul 2013-06-05 11:17:15 +01:00
parent 312cfebcb1
commit 97f3357b6d
22 changed files with 271 additions and 271 deletions
Download patch file Download diff file Expand all files Collapse all files

18
GPy/inference/SGD.py
View file

@ -95,11 +95,11 @@ class opt_SGD(Optimizer):
i = 0
for s in param_shapes:
N, Q = s
X = x[i:i+N*Q].reshape(N, Q)
N, input_dim = s
X = x[i:i+N*input_dim].reshape(N, input_dim)
X = X[samples]
subset = np.append(subset, X.flatten())
i += N*Q
i += N*input_dim
subset = np.append(subset, x[i:])
@ -167,17 +167,17 @@ class opt_SGD(Optimizer):
# self.model.constrained_positive_indices = p
self.model.constrained_indices = c
def get_param_shapes(self, N = None, Q = None):
def get_param_shapes(self, N = None, input_dim = None):
model_name = self.model.__class__.__name__
if model_name == 'GPLVM':
return [(N, Q)]
return [(N, input_dim)]
if model_name == 'Bayesian_GPLVM':
return [(N, Q), (N, Q)]
return [(N, input_dim), (N, input_dim)]
else:
raise NotImplementedError
def step_with_missing_data(self, f_fp, X, step, shapes):
N, Q = X.shape
N, input_dim = X.shape
if not sp.sparse.issparse(self.model.likelihood.Y):
Y = self.model.likelihood.Y
@ -269,7 +269,7 @@ class opt_SGD(Optimizer):
self.model.likelihood._offset = 0.0
self.model.likelihood._scale = 1.0
N, Q = self.model.X.shape
N, input_dim = self.model.X.shape
D = self.model.likelihood.Y.shape[1]
num_params = self.model._get_params()
self.trace = []
@ -302,7 +302,7 @@ class opt_SGD(Optimizer):
self.model.likelihood._set_params(sigma)
if missing_data:
shapes = self.get_param_shapes(N, Q)
shapes = self.get_param_shapes(N, input_dim)
f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes)
else:
self.model.likelihood.YYT = np.dot(self.model.likelihood.Y, self.model.likelihood.Y.T)