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lots of hacking on RBF

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This commit is contained in:
James Hensman 2014-02-26 10:38:09 +00:00
parent f6da5345d9
commit 60e8f88b9b
2 changed files with 80 additions and 98 deletions
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8
GPy/examples/dimensionality_reduction.py
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@ -16,16 +16,16 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan
output_dim = 1
input_dim = 3
else:
input_dim = 1
input_dim = 2
output_dim = output_dim
# 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)
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, ARD=1)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
#k = GPy.kern.Linear(input_dim, ARD=1)# + 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