How can I get f(x) value when GP (num_rand_basis = 0) was condcuted?

[sanosyu]

Hello developers,

I have a question about extraction of fmean and fcov after learning.

fmean = policy.predictor.get_post_fmean(policy.training, policy.test)
fcov = policy.predictor.get_post_fcov(policy.training, policy.test)

The above scripts worked when num_rand_basis was not set at 0.
However, it did not work when num_rand_basis was set at 0.

How can I get fmean and fcov values when GP (num_rand_basis = 0) was conducted?

The error message was as below

ValueError Traceback (most recent call last)
in ()
----> 1 fmean = policy.predictor.get_post_fmean(policy.training, policy.test)
2 fcov = policy.predictor.get_post_fcov(policy.training, policy.test)

/usr/local/lib/python2.7/dist-packages/combo/gp/predictor.pyc in get_post_fmean(self, training, test)
32 if self.model.stats is None:
33 self.prepare( training )
---> 34 return self.model.get_post_fmean( training.X, test.X )
35
36 def get_post_fcov( self, training, test, diag = True ):

/usr/local/lib/python2.7/dist-packages/combo/gp/core/model.pyc in get_post_fmean(self, X, Z, params)
102
103 if self.inf is 'exact':
--> 104 post_fmu = inf.exact.get_post_fmean(self, X, Z, params)
105
106 return post_fmu

/usr/local/lib/python2.7/dist-packages/combo/gp/inf/exact.pyc in get_post_fmean(gp, X, Z, params)
92 G = gp.prior.get_cov( X=Z, Z=X, params = prior_params )
93
---> 94 return G.dot(alpha) + fmu
95
96 def get_post_fcov(gp, X, Z, params = None, diag = True ):

ValueError: shapes (81,22) and (21,) not aligned: 22 (dim 1) != 21 (dim 0)