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Removed unnecessary laplace examples

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This commit is contained in:
Alan Saul 2013-10-24 15:19:09 +01:00
parent 8c222bef86
commit 9ce51e94f6
1 changed files with 1 additions and 55 deletions
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56
GPy/examples/laplace_approximations.py
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@ -142,54 +142,6 @@ def student_t_approx():
return m
def gaussian_f_check():
plt.close('all')
X = np.linspace(0, 1, 50)[:, None]
real_std = 0.2
noise = np.random.randn(*X.shape)*real_std
Y = np.sin(X*2*np.pi) + noise
kernelgp = GPy.kern.rbf(X.shape[1]) # + GPy.kern.white(X.shape[1])
mgp = GPy.models.GPRegression(X, Y, kernel=kernelgp)
mgp.ensure_default_constraints()
mgp.randomize()
mgp.optimize()
print "Gaussian"
print mgp
kernelg = kernelgp.copy()
#kernelst += GPy.kern.bias(X.shape[1])
N, D = X.shape
g_distribution = GPy.likelihoods.noise_model_constructors.gaussian(variance=0.1, N=N, D=D)
g_likelihood = GPy.likelihoods.Laplace(Y.copy(), g_distribution)
m = GPy.models.GPRegression(X, Y, kernelg, likelihood=g_likelihood)
m.likelihood.X = X
#m['rbf_v'] = mgp._get_params()[0]
#m['rbf_l'] = mgp._get_params()[1] + 1
m.ensure_default_constraints()
#m.constrain_fixed('rbf_v', mgp._get_params()[0])
#m.constrain_fixed('rbf_l', mgp._get_params()[1])
#m.constrain_bounded('t_no', 2*real_std**2, 1e3)
#m.constrain_positive('bias')
m.constrain_positive('noise_var')
#m['noise_variance'] = 0.1
#m.likelihood.X = X
m.randomize()
import ipdb; ipdb.set_trace() # XXX BREAKPOINT
plt.figure()
ax = plt.subplot(211)
m.plot(ax=ax)
m.optimize()
ax = plt.subplot(212)
m.plot(ax=ax)
print "final optimised gaussian"
print m
print "real GP"
print mgp
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
def boston_example():
import sklearn
from sklearn.cross_validation import KFold
@ -337,7 +289,7 @@ def boston_example():
ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
alpha=0.5)
ax.set_axisbelow(True)
return score_folds, pred_density
return mstu
def precipitation_example():
import sklearn
@ -359,9 +311,3 @@ def precipitation_example():
for n, (train, test) in enumerate(kf):
X_train, X_test, Y_train, Y_test = X[train], X[test], Y[train], Y[test]
print "Fold {}".format(n)
def plot_f_approx(model):
plt.figure()
model.plot(ax=plt.gca())
plt.plot(model.X, model.likelihood.f_hat, c='g')