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removed climin dependency unless actually needed

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James Hensman 2015-03-03 17:04:15 +00:00
parent 06d7e690f3
commit 965f1aa2cb
1 changed files with 13 additions and 12 deletions
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25
GPy/core/svgp.py
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@ -25,20 +25,21 @@ class SVGP(SparseGP):
Hensman, Matthews and Ghahramani, Scalable Variational GP Classification, ArXiv 1411.2005
"""
if batchsize is None:
batchsize = X.shape[0]
self.X_all, self.Y_all = X, Y
# how to rescale the batch likelihood in case of minibatches
self.batchsize = batchsize
batch_scale = float(self.X_all.shape[0])/float(self.batchsize)
#KL_scale = 1./np.float64(self.mpi_comm.size)
KL_scale = 1.0
if batchsize is None:
X_batch, Y_batch = X, Y
KL_scale, batch_scale = 1., 1.
import climin.util
#Make a climin slicer to make drawing minibatches much quicker
self.slicer = climin.util.draw_mini_slices(self.X_all.shape[0], self.batchsize)
X_batch, Y_batch = self.new_batch()
else:
self.X_all, self.Y_all = X, Y
# how to rescale the batch likelihood in case of minibatches
batch_scale = float(self.X_all.shape[0])/float(self.batchsize)
KL_scale = 1.0
import climin.util
#Make a climin slicer to make drawing minibatches much quicker
self.slicer = climin.util.draw_mini_slices(self.X_all.shape[0], self.batchsize)
X_batch, Y_batch = self.new_batch()
#create the SVI inference method
inf_method = svgp_inf(KL_scale=KL_scale, batch_scale=batch_scale)