def get_datasource(self):
return data.DSIsoGaussianMixture(self.means, self.variances, self.pmix)
def get_pqsource(prob_label):
"""
Return (p, ds), a tuple of
- p: a Density representing the distribution p
- ds: a DataSource, each corresponding to one parameter setting.
The DataSource generates sample from q.
"""
prob2tuples = {
# H0 is true. vary d. P = Q = N(0, I)
'sg5':
(density.IsotropicNormal(np.zeros(5),
1), data.DSIsotropicNormal(np.zeros(5), 1)),
# P = N(0, I), Q = N( (0.2,..0), I)
'gmd5': (density.IsotropicNormal(np.zeros(5), 1),
data.DSIsotropicNormal(np.hstack((0.2, np.zeros(4))), 1)),
'gmd1': (density.IsotropicNormal(np.zeros(1), 1),
data.DSIsotropicNormal(np.ones(1) * 0.2, 1)),
# P = N(0, I), Q = N( (1,..0), I)
'gmd100': (density.IsotropicNormal(np.zeros(100), 1),
data.DSIsotropicNormal(np.hstack((1, np.zeros(99))), 1)),
# Gaussian variance difference problem. Only the variance
# of the first dimenion differs. d varies.
'gvd5': (density.Normal(np.zeros(5), np.eye(5)),
data.DSNormal(np.zeros(5), np.diag(np.hstack(
(2, np.ones(4)))))),
'gvd10': (density.Normal(np.zeros(10), np.eye(10)),
data.DSNormal(np.zeros(10),
np.diag(np.hstack((2, np.ones(9)))))),
# Gaussian Bernoulli RBM. dx=50, dh=10. H0 is true
'gbrbm_dx50_dh10_v0':
gaussbern_rbm_tuple(0, dx=50, dh=10, n=sample_size),
# Gaussian Bernoulli RBM. dx=5, dh=3. H0 is true
'gbrbm_dx5_dh3_v0':
gaussbern_rbm_tuple(0, dx=5, dh=3, n=sample_size),
# Gaussian Bernoulli RBM. dx=50, dh=10.
'gbrbm_dx50_dh10_v1em3':
gaussbern_rbm_tuple(1e-3, dx=50, dh=10, n=sample_size),
# Gaussian Bernoulli RBM. dx=5, dh=3. Perturb with noise = 1e-2.
'gbrbm_dx5_dh3_v5em3':
gaussbern_rbm_tuple(5e-3, dx=5, dh=3, n=sample_size),
# Gaussian mixture of two components. Uniform mixture weights.
# p = 0.5*N(0, 1) + 0.5*N(3, 0.01)
# q = 0.5*N(-3, 0.01) + 0.5*N(0, 1)
'gmm_d1': (density.IsoGaussianMixture(np.array([[0], [3.0]]),
np.array([1, 0.01])),
data.DSIsoGaussianMixture(np.array([[-3.0], [0]]),
np.array([0.01, 1]))),
# p = N(0, 1)
# q = 0.1*N([-10, 0,..0], 0.001) + 0.9*N([0,0,..0], 1)
'g_vs_gmm_d5': (density.IsotropicNormal(np.zeros(5), 1),
data.DSIsoGaussianMixture(np.vstack((np.hstack(
(0.0, np.zeros(4))), np.zeros(5))),
np.array([0.0001, 1]),
pmix=[0.1, 0.9])),
'g_vs_gmm_d2': (density.IsotropicNormal(np.zeros(2), 1),
data.DSIsoGaussianMixture(np.vstack((np.hstack(
(0.0, np.zeros(1))), np.zeros(2))),
np.array([0.01, 1]),
pmix=[0.1, 0.9])),
'g_vs_gmm_d1': (density.IsotropicNormal(np.zeros(1), 1),
data.DSIsoGaussianMixture(np.array([[0.0], [0]]),
np.array([0.01, 1]),
pmix=[0.1, 0.9])),
}
if prob_label not in prob2tuples:
raise ValueError('Unknown problem label. Need to be one of %s' %
str(prob2tuples.keys()))
return prob2tuples[prob_label]