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some changes on examples
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2 changed files with 13 additions and 10 deletions
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@ -163,7 +163,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
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import numpy as np
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_np.random.seed(0)
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data = GPy.util.datasets.oil()
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data = GPy.util.datasets.oil_100()
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kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
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Y = data['X'][:N]
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@ -189,12 +189,12 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40
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from ..util.misc import param_to_array
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import numpy as np
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_np.random.seed(0)
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data = GPy.util.datasets.oil()
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#_np.random.seed(0)
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data = GPy.util.datasets.oil_100()
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kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
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Y = data['X'][:N]
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m = GPy.models.SSGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
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m = GPy.models.SSGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, group_spike=True, **k)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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if optimize:
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@ -327,13 +327,19 @@ def ssgplvm_simulation(optimize=True, verbose=1, group_spike=True,
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Y = Ylist[0]
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k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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#k = kern.RBF(Q, ARD=True, lengthscale=10.)
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m = SSGPLVM(Y, Q, init="init", num_inducing=num_inducing, kernel=k, group_spike=group_spike)
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m = SSGPLVM(Y, Q, init="init", num_inducing=num_inducing, kernel=k, group_spike=group_spike, learnPi=True)
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m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
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m.likelihood.variance = .1
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if optimize:
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print "Optimizing model:"
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m.optimize('scg', messages=verbose, max_iters=max_iters,
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m.likelihood.variance[:] = 0.01
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m.likelihood.variance.fix()
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m.optimize('bfgs', messages=verbose, max_iters=max_iters,
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gtol=.05)
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m.likelihood.variance.unfix()
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m.likelihood.variance.constrain_positive()
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m.optimize('bfgs', messages=verbose, max_iters=max_iters,
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gtol=.05)
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if plot:
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m.X.plot("SSGPLVM Latent Space 1D")
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@ -31,10 +31,7 @@ class PSICOMP_RBF(Pickleable):
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def _setup_observers(self):
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pass
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def _setup_observers(self):
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pass
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class PSICOMP_Linear(Pickleable):
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@Cache_this(limit=2, ignore_args=(0,))
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