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merged in kern chancges

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James Hensman 2014-02-20 14:07:54 +00:00
commit 847fdef9c8
54 changed files with 914 additions and 1280 deletions
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26
GPy/examples/dimensionality_reduction.py
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@ -22,18 +22,18 @@ 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)
k = (GPy.kern.RBF(input_dim, .5, lengthscales, ARD=True)
#+ GPy.kern.white(input_dim, 0.01)
)
K = k.K(X)
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)
# k = GPy.kern.rbf(input_dim, ARD = False) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.rbf(input_dim, .3, _np.ones(input_dim) * .2, ARD=True)
# k = GPy.kern.rbf(input_dim, .5, 2., ARD=0) + GPy.kern.rbf(input_dim, .3, .2, ARD=0)
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=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.linear(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(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.RBF(input_dim, .3, _np.ones(input_dim) * .2, ARD=True)
# k = GPy.kern.RBF(input_dim, .5, 2., ARD=0) + GPy.kern.RBF(input_dim, .3, .2, ARD=0)
# k = GPy.kern.RBF(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=True)
p = .3
@ -73,7 +73,7 @@ def gplvm_oil_100(optimize=True, verbose=1, plot=True):
data = GPy.util.datasets.oil_100()
Y = data['X']
# create simple GP model
kernel = GPy.kern.rbf(6, ARD=True) + GPy.kern.bias(6)
kernel = GPy.kern.RBF(6, ARD=True) + GPy.kern.bias(6)
m = GPy.models.GPLVM(Y, 6, kernel=kernel)
m.data_labels = data['Y'].argmax(axis=1)
if optimize: m.optimize('scg', messages=verbose)
@ -88,7 +88,7 @@ def sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_induci
Y = Y - Y.mean(0)
Y /= Y.std(0)
# Create the model
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q)
kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.bias(Q)
m = GPy.models.SparseGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing)
m.data_labels = data['Y'][:N].argmax(axis=1)
@ -138,7 +138,7 @@ def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=15, Q=4
(1 - var))) + .001
Z = _np.random.permutation(X)[:num_inducing]
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2)) + GPy.kern.white(Q, _np.exp(-2))
kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2)) + GPy.kern.white(Q, _np.exp(-2))
m = BayesianGPLVM(Y, Q, X=X, X_variance=S, num_inducing=num_inducing, Z=Z, kernel=kernel)
m.data_colors = c
@ -164,7 +164,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
_np.random.seed(0)
data = GPy.util.datasets.oil()
kernel = GPy.kern.rbf_inv(Q, 1., [.1] * Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2))
kernel = GPy.kern.RBF_inv(Q, 1., [.1] * Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2))
Y = data['X'][:N]
Yn = Gaussian(Y, normalize=True)
m = GPy.models.BayesianGPLVM(Yn, Q, kernel=kernel, num_inducing=num_inducing, **k)
@ -439,7 +439,7 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
data = GPy.util.datasets.osu_run1()
# optimize
back_kernel=GPy.kern.rbf(data['Y'].shape[1], lengthscale=5.)
back_kernel=GPy.kern.RBF(data['Y'].shape[1], lengthscale=5.)
mapping = GPy.mappings.Kernel(X=data['Y'], output_dim=2, kernel=back_kernel)
m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
@ -474,7 +474,7 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
data = GPy.util.datasets.osu_run1()
Q = 6
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2)) + GPy.kern.white(Q, _np.exp(-2))
kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2)) + GPy.kern.white(Q, _np.exp(-2))
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
# optimize
m.ensure_default_constraints()