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var dtc missing] memory efficiency greatly improved

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This commit is contained in:
mzwiessele 2014-07-02 09:17:04 -07:00
parent 89f7f2dc73
commit eb9fb180fb
2 changed files with 14 additions and 9 deletions
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14
GPy/inference/latent_function_inference/var_dtc.py
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@ -237,10 +237,13 @@ class VarDTCMissingData(LatentFunctionInference):
ind = slice(None)
self._subarray_indices.append([v,ind])
logger.info('preparing subarrays Y')
Ys = [Y[v, :][:, ind] for v, ind in self._subarray_indices]
#Ys = [Y[v, :][:, ind] for v, ind in self._subarray_indices]
logger.info('preparing traces Y')
traces = [np.einsum('ij,ij->', y,y) for y in Ys]
return Ys, traces
def trace(y, v, ind):
y = y[v,:][:,ind]
return np.einsum('ij,ij->', y,y)
traces = [trace(Y, v, ind) for v, ind in self._subarray_indices]
return traces
else:
self._subarray_indices = [[slice(None),slice(None)]]
return [Y], [(Y**2).sum()]
@ -257,7 +260,7 @@ class VarDTCMissingData(LatentFunctionInference):
psi1_all = kern.K(X, Z)
psi2_all = None
Ys, traces = self._Y(Y)
traces = self._Y(Y)
beta_all = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
het_noise = beta_all.size != 1
@ -288,7 +291,8 @@ class VarDTCMissingData(LatentFunctionInference):
#logger.info('computing dimension-wise likelihood and derivatives')
#size = len(Ys)
for y, trYYT, [v, ind] in itertools.izip(Ys, traces, self._subarray_indices):
for trYYT, [v, ind] in itertools.izip(traces, self._subarray_indices):
y = Y[v,:][:,ind]
#logger.info('{:.3%} dimensions:{}'.format((i+1.)/size, ind))
if het_noise: beta = beta_all[ind]
else: beta = beta_all