from trees.util import plot_tree, plot_tree_2d
from trees.ddt import DirichletDiffusionTree, Inverse, GaussianLikelihoodModel
from trees.mcmc import MetropolisHastingsSampler
from tqdm import tqdm
if __name__ == "__main__":
D = 2
N = 100
X = np.random.multivariate_normal(mean=np.zeros(D), cov=np.eye(D), size=N).astype(np.float32)
df = Inverse(c=1)
lm = GaussianLikelihoodModel(sigma=np.eye(D) / 4.0, mu0=np.zeros(D), sigma0=np.eye(D))
ddt = DirichletDiffusionTree(df=df,
likelihood_model=lm)
mh = MetropolisHastingsSampler(ddt, X)
mh.initialize_assignments()
for _ in tqdm(xrange(1000)):
mh.sample()
plt.figure()
plt.plot(mh.likelihoods)
plt.figure()
plot_tree(mh.tree)
plt.figure()
plot_tree_2d(mh.tree, X)
plt.show()
from trees.util import plot_tree
from trees import Tree, TreeNode, TreeLeaf
import matplotlib.pyplot as plt
if __name__ == "__main__":
leaf1 = TreeLeaf(1)
leaf2 = TreeLeaf(2)
leaf3 = TreeLeaf(3)
node1 = TreeNode()
node1.add_child(leaf1)
node1.add_child(leaf2)
node2 = TreeNode()
node2.add_child(node1)
node2.add_child(leaf3)
tree = Tree(node2)
plot_tree(tree)
plt.show()
p = leaf1.detach()
plot_tree(tree)
plt.show()
leaf3.attach(p)
plot_tree(tree)
plt.show()
