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[normalizer] only mean, because variance could be not Gaussian...

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mzwiessele 2014-08-27 15:47:41 -07:00
parent a3c8739f9e
commit 8cf11257b0
2 changed files with 9 additions and 12 deletions
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6
GPy/core/gp.py
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@ -15,7 +15,7 @@ from parameterization.variational import VariationalPosterior
from scipy.sparse.base import issparse
import logging
from GPy.util.normalizer import GaussianNorm
from GPy.util.normalizer import MeanNorm
logger = logging.getLogger("GP")
class GP(Model):
@ -31,7 +31,7 @@ class GP(Model):
:param Norm normalizer:
normalize the outputs Y.
Prediction will be un-normalized using this normalizer.
If normalizer is None, we will normalize using GaussianNorm.
If normalizer is None, we will normalize using MeanNorm.
If normalizer is False, no normalization will be done.
.. Note:: Multiple independent outputs are allowed using columns of Y
@ -52,7 +52,7 @@ class GP(Model):
logger.info("initializing Y")
if normalizer is None:
self.normalizer = GaussianNorm()
self.normalizer = MeanNorm()
elif normalizer is False:
self.normalizer = None
else: