From ca892e32e169cbd97ef30d3a66c975900564aa60 Mon Sep 17 00:00:00 2001 From: Zhenwen Dai Date: Mon, 22 Sep 2014 15:11:16 +0100 Subject: [PATCH] some changes on examples --- GPy/examples/dimensionality_reduction.py | 18 ++++++++++++------ GPy/kern/_src/psi_comp/__init__.py | 5 +---- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py index 7ea18877..a9a7cc3f 100644 --- a/GPy/examples/dimensionality_reduction.py +++ b/GPy/examples/dimensionality_reduction.py @@ -163,7 +163,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, import numpy as np _np.random.seed(0) - data = GPy.util.datasets.oil() + data = GPy.util.datasets.oil_100() kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2)) Y = data['X'][:N] @@ -189,12 +189,12 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40 from ..util.misc import param_to_array import numpy as np - _np.random.seed(0) - data = GPy.util.datasets.oil() + #_np.random.seed(0) + data = GPy.util.datasets.oil_100() kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2)) Y = data['X'][:N] - m = GPy.models.SSGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k) + m = GPy.models.SSGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, group_spike=True, **k) m.data_labels = data['Y'][:N].argmax(axis=1) if optimize: @@ -327,13 +327,19 @@ def ssgplvm_simulation(optimize=True, verbose=1, group_spike=True, Y = Ylist[0] 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="init", num_inducing=num_inducing, kernel=k, group_spike=group_spike) + m = SSGPLVM(Y, Q, init="init", num_inducing=num_inducing, kernel=k, group_spike=group_spike, learnPi=True) m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape) m.likelihood.variance = .1 if optimize: print "Optimizing model:" - m.optimize('scg', messages=verbose, max_iters=max_iters, + m.likelihood.variance[:] = 0.01 + m.likelihood.variance.fix() + m.optimize('bfgs', messages=verbose, max_iters=max_iters, + gtol=.05) + m.likelihood.variance.unfix() + m.likelihood.variance.constrain_positive() + m.optimize('bfgs', messages=verbose, max_iters=max_iters, gtol=.05) if plot: m.X.plot("SSGPLVM Latent Space 1D") diff --git a/GPy/kern/_src/psi_comp/__init__.py b/GPy/kern/_src/psi_comp/__init__.py index 3df75ff4..d9b0673d 100644 --- a/GPy/kern/_src/psi_comp/__init__.py +++ b/GPy/kern/_src/psi_comp/__init__.py @@ -31,10 +31,7 @@ class PSICOMP_RBF(Pickleable): def _setup_observers(self): pass - - def _setup_observers(self): - pass - + class PSICOMP_Linear(Pickleable): @Cache_this(limit=2, ignore_args=(0,))