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dimensionality reduction merge

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This commit is contained in:
Max Zwiessele 2013-07-17 17:45:07 +01:00
commit 7f63849dd2
9 changed files with 28 additions and 87 deletions
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10
GPy/examples/dimensionality_reduction.py
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@ -24,7 +24,7 @@ def BGPLVM(seed=default_seed):
Y = np.random.multivariate_normal(np.zeros(N), K, Q).T
lik = Gaussian(Y, normalize=True)
k = GPy.kern.rbf(Q, ARD=True) + 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, ARD = False) + GPy.kern.white(Q, 0.00001)
@ -145,6 +145,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=
# 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))
Y = data['X'][:N]
Yn = Y - Y.mean(0)
Yn /= Yn.std(0)
@ -375,11 +376,12 @@ def stick():
def stick_bgplvm(model=None):
data = GPy.util.datasets.stick()
Q = 6
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)
kernel = GPy.kern.rbf_inv(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=35,kernel=kernel)
# optimize
m.ensure_default_constraints()
m.optimize(messages=1, max_f_eval=3000, xtol=1e-300, ftol=1e-300)
m.constrain_bounded('.*rbf_inv',1e-5, 100)
m.optimize(messages=1, max_iters=3000,xtol=1e-300,ftol=1e-300)
m._set_params(m._get_params())
plt.clf, (latent_axes, sense_axes) = plt.subplots(1, 2)
plt.sca(latent_axes)

4
GPy/examples/regression.py
View file

@ -67,8 +67,8 @@ def toy_ARD(optim_iters=1000, kernel_type='linear', N=300, D=4):
X4 = np.log(np.sort(np.random.rand(N,1),0))
X = np.hstack((X1, X2, X3, X4))
Y1 = np.asarray(2*X[:,0]+3).T
Y2 = np.asarray(4*(X[:,2]-1.5*X[:,0])).T
Y1 = np.asmatrix(2*X[:,0]+3).T
Y2 = np.asmatrix(4*(X[:,2]-1.5*X[:,0])).T
Y = np.hstack((Y1, Y2))
Y = np.dot(Y, np.random.rand(2,D));