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trying to fix the likelihood.Y madness

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Nicolo Fusi 2013-02-15 12:17:06 +00:00
parent 2abba8cf14
commit 7efe14c329
1 changed files with 17 additions and 16 deletions
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33
GPy/inference/SGD.py
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@ -129,28 +129,29 @@ class opt_SGD(Optimizer):
def step_with_missing_data(self, f_fp, X, step, shapes, sparse_matrix):
N, Q = X.shape
if not sparse_matrix:
samples = self.non_null_samples(self.model.Y)
samples = self.non_null_samples(self.model.likelihood.Y)
self.model.N = samples.sum()
self.model.Y = self.model.Y[samples]
self.model.likelihood.Y = self.model.likelihood.Y[samples]
else:
samples = self.model.Y.nonzero()[0]
samples = self.model.likelihood.Y.nonzero()[0]
self.model.N = len(samples)
self.model.Y = np.asarray(self.model.Y[samples].todense(), dtype = np.float64)
self.model.likelihood.Y = np.asarray(self.model.likelihood.Y[samples].todense(), dtype = np.float64)
self.model.likelihood.N = self.model.N
j = self.subset_parameter_vector(self.x_opt, samples, shapes)
self.model.X = X[samples]
if self.model.N == 0 or self.model.Y.std() == 0.0:
if self.model.N == 0 or self.model.likelihood.Y.std() == 0.0:
return 0, step, self.model.N
if self.center:
self.model.Y -= self.model.Y.mean()
self.model.Y /= self.model.Y.std()
self.model.likelihood.Y -= self.model.likelihood.Y.mean()
self.model.likelihood.Y /= self.model.likelihood.Y.std()
model_name = self.model.__class__.__name__
if model_name == 'Bayesian_GPLVM':
self.model.trYYT = np.sum(np.square(self.model.Y))
self.model.likelihood.trYYT = np.sum(np.square(self.model.likelihood.Y))
b, p = self.shift_constraints(j)
@ -166,16 +167,15 @@ class opt_SGD(Optimizer):
def opt(self, f_fp=None, f=None, fp=None):
self.x_opt = self.model._get_params_transformed()
X, Y = self.model.X.copy(), self.model.Y.copy()
X, Y = self.model.X.copy(), self.model.likelihood.Y.copy()
N, Q = self.model.X.shape
D = self.model.Y.shape[1]
D = self.model.likelihood.Y.shape[1]
self.trace = []
sparse_matrix = sp.sparse.issparse(self.model.Y)
sparse_matrix = sp.sparse.issparse(self.model.likelihood.Y)
missing_data = True
if not sparse_matrix:
missing_data = self.check_for_missing(self.model.Y)
self.model.Youter = None # this is probably not very efficient
self.model.YYT = None
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)
@ -198,7 +198,7 @@ class opt_SGD(Optimizer):
for j in features:
count += 1
self.model.D = len(j)
self.model.Y = Y[:, j]
self.model.likelihood.Y = Y[:, j]
# self.model.trYYT = np.sum(np.square(self.model.Y))
if missing_data or sparse_matrix:
shapes = self.get_param_shapes(N, Q)
@ -228,7 +228,8 @@ 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.Y = Y
self.model.likelihood.N = N
self.model.likelihood.Y = Y
self.model.X = X
self.model.D = D
# self.model.Youter = np.dot(Y, Y.T)