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parameterized now supports deleting of parameters

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This commit is contained in:
Max Zwiessele 2014-02-21 17:53:44 +00:00
parent 2da256fa93
commit 659643038f
12 changed files with 113 additions and 83 deletions
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19
GPy/examples/dimensionality_reduction.py
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@ -74,7 +74,7 @@ def gplvm_oil_100(optimize=True, verbose=1, plot=True):
data = GPy.util.datasets.oil_100()
Y = data['X']
# create simple GP model
kernel = GPy.kern.RBF(6, ARD=True) + GPy.kern.bias(6)
kernel = GPy.kern.RBF(6, ARD=True) + GPy.kern.Bias(6)
m = GPy.models.GPLVM(Y, 6, kernel=kernel)
m.data_labels = data['Y'].argmax(axis=1)
if optimize: m.optimize('scg', messages=verbose)
@ -190,17 +190,22 @@ 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(x))
s1 = _np.vectorize(lambda x: -_np.sin(x))
s2 = _np.vectorize(lambda x: _np.cos(x))
s3 = _np.vectorize(lambda x:-_np.exp(-_np.cos(2 * x)))
sS = _np.vectorize(lambda x: _np.sin(2 * x))
sS = _np.vectorize(lambda x: x*_np.sin(x))
s1 = s1(x)
s2 = s2(x)
s3 = s3(x)
sS = sS(x)
S1 = _np.hstack([s1, sS])
s1 -= s1.mean(); s1 /= s1.std(0)
s2 -= s2.mean(); s2 /= s2.std(0)
s3 -= s3.mean(); s3 /= s3.std(0)
sS -= sS.mean(); sS /= sS.std(0)
S1 = _np.hstack([s1, s2, sS])
S2 = _np.hstack([s2, s3, sS])
S3 = _np.hstack([s3, sS])
@ -271,7 +276,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
_, _, 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)
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
if optimize:
@ -291,10 +296,10 @@ 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 = 15, 5, 8, 30, 3, 10
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 5, 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)
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
inan = _np.random.binomial(1, .6, size=Y.shape).astype(bool)
m = BayesianGPLVM(Y.copy(), Q, init="random", num_inducing=num_inducing, kernel=k)