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Laplace now appears to be grad checking again

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This commit is contained in:
Alan Saul 2014-02-10 15:40:06 +00:00
parent c6d466e72d
commit 6cbf810856
6 changed files with 43 additions and 37 deletions
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34
GPy/examples/non_gaussian.py
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@ -37,39 +37,43 @@ def student_t_approx(optimize=True, plot=True):
# Kernel object
kernel1 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel2 = kernel1.copy()
kernel3 = kernel1.copy()
kernel4 = kernel1.copy()
kernel2 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel3 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel4 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
#Gaussian GP model on clean data
m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
# optimize
m1.ensure_default_constraints()
m1.constrain_fixed('white', 1e-5)
m1['white'] = 1e-5
m1['white'].constrain_fixed('white')
m1.randomize()
#Gaussian GP model on corrupt data
m2 = GPy.models.GPRegression(X, Yc.copy(), kernel=kernel2)
m2.ensure_default_constraints()
m2.constrain_fixed('white', 1e-5)
m1['white'] = 1e-5
m1['white'].constrain_fixed('white')
m2.randomize()
#Student t GP model on clean data
t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=deg_free, sigma2=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution)
m3 = GPy.models.GPRegression(X, Y.copy(), kernel3, likelihood=stu_t_likelihood)
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
m3 = GPy.core.GP(X, Y.copy(), kernel3, likelihood=t_distribution, inference_method=laplace_inf)
m3.ensure_default_constraints()
m3.constrain_bounded('t_noise', 1e-6, 10.)
m3.constrain_fixed('white', 1e-5)
m3['t_noise'].constrain_bounded(1e-6, 10.)
m3['white'] = 1e-5
m3['white'].constrain_fixed()
m3.randomize()
#Student t GP model on corrupt data
t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=deg_free, sigma2=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution)
m4 = GPy.models.GPRegression(X, Yc.copy(), kernel4, likelihood=corrupt_stu_t_likelihood)
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
m4 = GPy.core.GP(X, Yc.copy(), kernel4, likelihood=t_distribution, inference_method=laplace_inf)
m4.ensure_default_constraints()
m4.constrain_bounded('t_noise', 1e-6, 10.)
m4.constrain_fixed('white', 1e-5)
m4['t_noise'].constrain_bounded(1e-6, 10.)
m4['white'] = 1e-5
m4['white'].constrain_fixed()
m4.randomize()
if optimize:

7
GPy/examples/regression.py
View file

@ -281,11 +281,12 @@ def toy_poisson_rbf_1d_laplace(optimize=True, plot=True):
f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.rbf(1).K(X))
Y = np.array([np.random.poisson(np.exp(f)) for f in f_true])[:,None]
noise_model = GPy.likelihoods.poisson()
likelihood = GPy.likelihoods.Laplace(Y,noise_model)
kern = GPy.kern.rbf(1)
poisson_lik = GPy.likelihoods.Poisson()
laplace_inf = GPy.inference.latent_function_inference.LaplaceInference()
# create simple GP Model
m = GPy.models.GPRegression(X, Y, likelihood=likelihood)
m = GPy.core.GP(X, Y, kernel=kern, likelihood=poisson_lik, inference_method=laplace_inf)
if optimize:
m.optimize(optimizer)