def compute_observation_prob(params, samples):
# choose a set of param
for key in params.keys():
print("#### TREE : " + key)
parameters = params[key]
for j in [1, 2, 3, 4, 5]:
t = Tree()
t.load_params(parameters)
samples_name = key + '_sample_' + str(j)
try:
sample = samples[samples_name]
except:
break
t.load_sample(sample)
compute_posterior(t)
print("\tsample_" + str(j) + " : " +
str(compute_leaves_probability(t)))
def sample_given_trees(params, samples):
# choose a set of param
for key in params.keys():
print("#### TREE : " + key)
parameters = params[key]
for j in [1, 2, 3, 4, 5]:
t = Tree()
t.load_params(parameters)
samples_name = key + '_sample_' + str(j)
try:
sample = samples[samples_name]
except:
break
t.load_sample(sample)
compute_posterior(t)
# sample_tree(t)
t.print_tree(True, True, True)
tree.root.sample = 1
return tree
if __name__ == "__main__":
t = Tree()
my_data_path = ''
# get data to load into the tree
params = np.load(my_data_path + 'tree_params.npy').tolist()
samples = np.load(my_data_path + 'tree_samples.npy').tolist()
# choose a set of param
params_name = params.keys()[0]
parameters = params[params_name]
# choose samples
samples_name = params_name + '_sample_2'
sample = samples[samples_name]
# Load params into tree and samples
t.load_params(parameters)
t.load_sample(sample)
print("### printing loaded tree")
t.print_tree(True, True)
t = custom_tree()
t.print_tree(True, True)
print(compute_probability_observation(t))
"""
print("### printing the tree with samples:")
print_tree(root, print_sample=True)
print(root.cat)
"""
Use tree object:
"""
t = Tree()
my_data_path = ''
with open(my_data_path + 'tree_params.pickle', 'rb') as handle:
params = pickle.load(handle)
key = params.keys()[0]
"""
Load params into tree
"""
t.load_params(params[key])
print("### printing loaded tree")
t.print_tree(False, True)
"""
Generate a random tree
"""
print("### generating random tree")
t.create_random_tree(3)
t.print_tree()