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added BGPLVM in parameterized

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Max Zwiessele 2013-11-06 15:15:13 +00:00
parent 3316d29341
commit 8c02e4af36
6 changed files with 103 additions and 72 deletions
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18
GPy/examples/dimensionality_reduction.py
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@ -25,9 +25,9 @@ def BGPLVM(seed=default_seed):
Y = np.random.multivariate_normal(np.zeros(N), K, D).T
lik = Gaussian(Y, normalize=True)
k = GPy.kern.rbf_inv(input_dim, .5, np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
# k = GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf(input_dim, ARD = False) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf_inv(input_dim, .5, np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
k = GPy.kern.rbf(input_dim, ARD=1)
# k = GPy.kern.rbf(input_dim, ARD = False)
m = GPy.models.BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing)
m.lengthscales = lengthscales
@ -158,15 +158,15 @@ def BGPLVM_oil(optimize=True, N=200, input_dim=7, num_inducing=40, max_iters=100
# m.constrain('variance|leng', LogexpClipped())
# m['.*lengt'] = m.X.var(0).max() / m.X.var(0)
m['noise'] = Yn.Y.var() / 100.
m['gaussian'] = Yn.Y.var() / 100.
# optimize
if optimize:
m.constrain_fixed('noise')
m.gaussian.variance.fix() # m.constrain_fixed('noise')
m.optimize('scg', messages=1, max_iters=200, gtol=.05)
m.constrain_positive('noise')
m.constrain_bounded('white', 1e-7, 1)
m.gaussian.variance.constrain_positive() # m.constrain_positive('noise')
#m.constrain_bounded('white', 1e-7, 1)
m.optimize('scg', messages=1, max_iters=max_iters, gtol=.05)
if plot:
@ -265,7 +265,7 @@ def bgplvm_simulation_matlab_compare():
# X_variance=S,
_debug=False)
m.auto_scale_factor = True
m['noise'] = Y.var() / 100.
m['gaussian'] = Y.var() / 100.
m['linear_variance'] = .01
return m
@ -287,7 +287,7 @@ def bgplvm_simulation(optimize='scg',
m = BayesianGPLVM(Y, input_dim, init="PCA", num_inducing=num_inducing, kernel=k)
# m.constrain('variance|noise', LogexpClipped())
m['noise'] = Y.var() / 100.
m['gaussian'] = Y.var() / 100.
if optimize:
print "Optimizing model:"