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hierarchy edits. adding removing parameters withing hierarchy

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Max Zwiessele 2014-02-28 16:18:47 +00:00
parent c87bda9e49
commit 47e4026141
11 changed files with 106 additions and 64 deletions
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10
GPy/examples/dimensionality_reduction.py
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@ -187,10 +187,10 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
_np.random.seed(1234)
x = _np.linspace(0, 4 * _np.pi, N)[:, None]
s1 = _np.vectorize(lambda x: -_np.sin(_np.exp(x)))
s1 = _np.vectorize(lambda x: _np.sin(x))
s2 = _np.vectorize(lambda x: _np.cos(x)**2)
s3 = _np.vectorize(lambda x:-_np.exp(-_np.cos(2 * x)))
sS = _np.vectorize(lambda x: x*_np.sin(x))
sS = _np.vectorize(lambda x: _np.cos(x))
s1 = s1(x)
s2 = s2(x)
@ -202,7 +202,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
s3 -= s3.mean(); s3 /= s3.std(0)
sS -= sS.mean(); sS /= sS.std(0)
S1 = _np.hstack([s1, s2, sS])
S1 = _np.hstack([s1, sS])
S2 = _np.hstack([s2, s3, sS])
S3 = _np.hstack([s3, sS])
@ -270,7 +270,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
from GPy import kern
from GPy.models import BayesianGPLVM
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9
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)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
@ -294,7 +294,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
from GPy.models import BayesianGPLVM
from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 7, 9
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)