fat_right_leaf_2, _priors = type_mixture_leaf_factory(
leaf_type='pm',
leaf_meta_type=MetaType.REAL,
type_to_param_map=pm_continuous_param_map,
scope=[1],
init_weights=lf_2_init_weights)
lf_3_init_weights = {Poisson: 0.9, Categorical: 0.1}
# lf_3_init_weights = np.array([.9, .1])
fat_right_leaf_3, _priors = type_mixture_leaf_factory(
leaf_type='pm',
leaf_meta_type=MetaType.DISCRETE,
type_to_param_map=pm_discrete_param_map,
scope=[2],
init_weights=lf_3_init_weights)
r_prod.children = [fat_right_leaf_1, fat_right_leaf_2, fat_right_leaf_3]
#
# left branch one leaf and one sum nodes
rf_3_init_weights = {Categorical: 0.4, Poisson: 0.6}
# rf_3_init_weights = np.array([.6, .4])
fat_left_leaf_3, _priors = type_mixture_leaf_factory(
leaf_type='pm',
leaf_meta_type=MetaType.DISCRETE,
type_to_param_map=pm_discrete_param_map,
scope=[2],
init_weights=rf_3_init_weights)
left_sum_node = Sum()
l_prod.children = [left_sum_node, fat_left_leaf_3]
ax.plot(pdf_x, pdf_y, label="exponential")
print('Exponential Mode:', exponential.mode)
plt.axvline(x=exponential.mode, color='r')
if show_plots:
plt.show()
#
#
# testing for a product node over two leaves
rand_gen = np.random.RandomState(17)
l_1 = Gaussian(mean=0.5, stdev=2, scope=[0])
l_2 = Poisson(mean=5, scope=[1])
p = Product()
p.children = [l_1, l_2]
rebuild_scopes_bottom_up(p)
assign_ids(p)
N = 10
X_1 = rand_gen.normal(loc=0, scale=1, size=(N, 1))
X_2 = rand_gen.poisson(lam=3, size=(N, 1))
X = np.concatenate((X_1, X_2), axis=1)
print('X', X, X.shape)
X_mpe = np.copy(X)
X_mpe[np.arange(N) % 2 == 0, 0] = np.nan
X_mpe[np.arange(N) % 2 == 1, 1] = np.nan
print('X_mpe', X_mpe, X_mpe.shape)
lls = likelihood(p, X)