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fix for tie print

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This commit is contained in:
Zhenwen Dai 2014-09-22 10:10:08 +01:00
parent c279da4c75
commit f0df591625
3 changed files with 6 additions and 6 deletions
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2
GPy/core/parameterization/param.py
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@ -224,7 +224,7 @@ class Param(Parameterizable, ObsAr):
def _ties_str(self):
return ['']
def _ties_for(self, ravi):
return [['N/A' if self.tie.flat[i]==0 else str(self.tie[i])] for i in xrange(ravi.size)]
return [['N/A' if self.tie.flat[i]==0 else str(self.tie.flat[i])] for i in xrange(ravi.size)]
def __repr__(self, *args, **kwargs):
name = "\033[1m{x:s}\033[0;0m:\n".format(
x=self.hierarchy_name())

8
GPy/examples/dimensionality_reduction.py
View file

@ -315,9 +315,9 @@ def bgplvm_simulation(optimize=True, verbose=1,
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
return m
def ssgplvm_simulation(optimize=True, verbose=1,
def ssgplvm_simulation(optimize=True, verbose=1, group_spike=True,
plot=True, plot_sim=False,
max_iters=2e4, useGPU=False
max_iters=2e4
):
from GPy import kern
from GPy.models import SSGPLVM
@ -325,9 +325,9 @@ def ssgplvm_simulation(optimize=True, verbose=1,
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.Linear(Q, ARD=True, useGPU=useGPU)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
#k = kern.RBF(Q, ARD=True, lengthscale=10.)
m = SSGPLVM(Y, Q, init="pca", num_inducing=num_inducing, kernel=k)
m = SSGPLVM(Y, Q, init="init", num_inducing=num_inducing, kernel=k, group_spike=group_spike)
m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
m.likelihood.variance = .1

2
GPy/models/ss_gplvm.py
View file

@ -74,7 +74,7 @@ class SSGPLVM(SparseGP_MPI):
# self.X.variance.constrain_positive()
if self.group_spike:
[self.X.gamma[:,i].tie('tieGamma'+str(i)) for i in xrange(self.X.gamma.shape[1])] # Tie columns together
[self.X.gamma[:,i].tie_together() for i in xrange(self.X.gamma.shape[1])] # Tie columns together
def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form."""