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workin gon linear kernel

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This commit is contained in:
James Hensman 2014-02-21 10:38:47 +00:00
parent 0c92fca31a
commit 8b2f39450b
6 changed files with 83 additions and 140 deletions
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11
GPy/examples/dimensionality_reduction.py
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@ -21,10 +21,11 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan
# generate GPLVM-like data
X = _np.random.rand(num_inputs, input_dim)
lengthscales = _np.random.rand(input_dim)
k = (GPy.kern.RBF(input_dim, .5, lengthscales, ARD=True)
#+ GPy.kern.white(input_dim, 0.01)
)
#lengthscales = _np.random.rand(input_dim)
#k = (GPy.kern.RBF(input_dim, .5, lengthscales, ARD=True)
##+ GPy.kern.white(input_dim, 0.01)
#)
k = GPy.kern.Linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, (output_dim,)).T
@ -48,7 +49,7 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan
# randomly obstruct data with percentage p
#===========================================================================
#m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
m.lengthscales = lengthscales
#m.lengthscales = lengthscales
if plot:
import matplotlib.pyplot as pb