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BGPLVM example testing with rbf_inv

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Max Zwiessele 2013-07-17 15:22:27 +01:00
parent 03dd1ba895
commit c42cdba0e6
1 changed files with 9 additions and 6 deletions
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15
GPy/examples/dimensionality_reduction.py
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@ -18,16 +18,18 @@ def BGPLVM(seed=default_seed):
D = 10 D = 10
# generate GPLVM-like data # generate GPLVM-like data
X = np.random.rand(N, Q) X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.01) lengthscales = np.random.rand(Q)
k = GPy.kern.rbf(Q, .5, lengthscales, ARD=True) + GPy.kern.white(Q, 0.01)
K = k.K(X) K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, Q).T Y = np.random.multivariate_normal(np.zeros(N), K, Q).T
lik = Gaussian(Y, normalize=True) lik = Gaussian(Y, normalize=True)
k = GPy.kern.rbf_inv(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q) k = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001) # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
# k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001) # k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
m = GPy.models.BayesianGPLVM(lik, Q, kernel=k, num_inducing=num_inducing) m = GPy.models.BayesianGPLVM(lik, Q, kernel=k, num_inducing=num_inducing)
m.lengthscales = lengthscales
# m.constrain_positive('(rbf|bias|noise|white|S)') # m.constrain_positive('(rbf|bias|noise|white|S)')
# m.constrain_fixed('S', 1) # m.constrain_fixed('S', 1)
@ -38,8 +40,8 @@ def BGPLVM(seed=default_seed):
# m.optimize(messages = 1) # m.optimize(messages = 1)
# m.plot() # m.plot()
# pb.title('After optimisation') # pb.title('After optimisation')
m.randomize() # m.randomize()
m.checkgrad(verbose=1) # m.checkgrad(verbose=1)
return m return m
@ -143,6 +145,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=
# create simple GP model # create simple GP model
kernel = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2)) kernel = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
kernel += GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
Y = data['X'][:N] Y = data['X'][:N]
Yn = Y - Y.mean(0) Yn = Y - Y.mean(0)
Yn /= Yn.std(0) Yn /= Yn.std(0)
@ -151,7 +154,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=
m.data_labels = data['Y'][:N].argmax(axis=1) m.data_labels = data['Y'][:N].argmax(axis=1)
# m.constrain('variance|leng', logexp_clipped()) # m.constrain('variance|leng', logexp_clipped())
m['.*lengt'] = m.X.var(0).max() / m.X.var(0) # m['.*lengt'] = m.X.var(0).max() / m.X.var(0)
m['noise'] = Yn.var() / 100. m['noise'] = Yn.var() / 100.
@ -267,7 +270,7 @@ def bgplvm_simulation(optimize='scg',
max_iters=2e4, max_iters=2e4,
plot_sim=False): plot_sim=False):
# from GPy.core.transformations import logexp_clipped # from GPy.core.transformations import logexp_clipped
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 300, 23, 6 D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 300, 30, 6
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim) slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
from GPy.models import mrd from GPy.models import mrd