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array core and bgplvm working > changes due to __i<op>__ will now be reported

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Max Zwiessele 2014-02-11 16:54:33 +00:00
parent 4cfc13d5fc
commit 9d262936c6
10 changed files with 177 additions and 54 deletions
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11
GPy/examples/dimensionality_reduction.py
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@ -3,7 +3,7 @@
import numpy as _np
default_seed = _np.random.seed(123344)
def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False, output_dim=1e4):
"""
model for testing purposes. Samples from a GP with rbf kernel and learns
the samples with a new kernel. Normally not for optimization, just model cheking
@ -18,7 +18,7 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
input_dim = 3
else:
input_dim = 1
output_dim = 25
output_dim = output_dim
# generate GPLVM-like data
X = _np.random.rand(num_inputs, input_dim)
@ -27,7 +27,7 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
#+ GPy.kern.white(input_dim, 0.01)
)
K = k.K(X)
Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, output_dim).T
Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, (output_dim,)).T
# 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.linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
@ -266,11 +266,10 @@ def bgplvm_simulation(optimize=True, verbose=1,
Y = Ylist[0]
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)
m.Gaussian_noise = Y.var() / 100.
if optimize:
print "Optimizing model:"
m.optimize('scg', messages=verbose, max_iters=max_iters,
m.optimize('bfgs', messages=verbose, max_iters=max_iters,
gtol=.05)
if plot:
m.q.plot("BGPLVM Latent Space 1D")