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[setxy] was bugged

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This commit is contained in:
mzwiessele 2016-05-11 15:02:44 +01:00
parent 22d7e20429
commit 375f3e0d6c
2 changed files with 8 additions and 6 deletions
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8
GPy/core/gp.py
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@ -148,14 +148,16 @@ class GP(Model):
# LVM models # LVM models
if isinstance(self.X, VariationalPosterior): if isinstance(self.X, VariationalPosterior):
assert isinstance(X, type(self.X)), "The given X must have the same type as the X in the model!" assert isinstance(X, type(self.X)), "The given X must have the same type as the X in the model!"
index = self.X._parent_index_
self.unlink_parameter(self.X) self.unlink_parameter(self.X)
self.X = X self.X = X
self.link_parameter(self.X) self.link_parameter(self.X, index=index)
else: else:
index = self.X._parent_index_
self.unlink_parameter(self.X) self.unlink_parameter(self.X)
from ..core import Param from ..core import Param
self.X = Param('latent mean',X) self.X = Param('latent mean', X)
self.link_parameter(self.X) self.link_parameter(self.X, index=index)
else: else:
self.X = ObsAr(X) self.X = ObsAr(X)
self.update_model(True) self.update_model(True)

6
GPy/testing/gp_tests.py
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@ -24,9 +24,9 @@ class Test(unittest.TestCase):
k = GPy.kern.RBF(1) k = GPy.kern.RBF(1)
m = GPy.models.BayesianGPLVM(self.Y, 1, kernel=k) m = GPy.models.BayesianGPLVM(self.Y, 1, kernel=k)
mu, var = m.predict(m.X) mu, var = m.predict(m.X)
X = m.X.copy() X = m.X
Xnew = NormalPosterior(m.X.mean[:10].copy(), m.X.variance[:10].copy()) Xnew = NormalPosterior(m.X.mean[:10].copy(), m.X.variance[:10].copy())
m.set_XY(Xnew, m.Y[:10]) m.set_XY(Xnew, m.Y[:10].copy())
assert(m.checkgrad()) assert(m.checkgrad())
m.set_XY(X, self.Y) m.set_XY(X, self.Y)
mu2, var2 = m.predict(m.X) mu2, var2 = m.predict(m.X)
@ -40,7 +40,7 @@ class Test(unittest.TestCase):
mu, var = m.predict(m.X) mu, var = m.predict(m.X)
X = m.X.copy() X = m.X.copy()
Xnew = X[:10].copy() Xnew = X[:10].copy()
m.set_XY(Xnew, m.Y[:10]) m.set_XY(Xnew, m.Y[:10].copy())
assert(m.checkgrad()) assert(m.checkgrad())
m.set_XY(X, self.Y) m.set_XY(X, self.Y)
mu2, var2 = m.predict(m.X) mu2, var2 = m.predict(m.X)