containing the parameters to be estimated (alpha, beta), and the
BinaryStateModel class instance as the second argument.
"""
if model.model == 'ARD':
model.alpha, model.beta = params
else:
model.alpha = params[0]
model.set_qmat()
liks = model.pruning_algorithm()
return -np.log(liks).sum()
if __name__ == "__main__":
import ipcoal
from hogtie.utils import set_loglevel
set_loglevel("DEBUG")
import os
#
#print("important-------")
#print('toytree', toytree.__version__)
#print('ipcoal', ipcoal.__version__)
#print("----------------")
#
## GENERATE SOME BINARY DATA
TREE = toytree.rtree.baltree(ntips=12, treeheight=1e6)
#MODEL = ipcoal.Model(TREE, Ne=20000, mut=1e-8, seed=123)
#MODEL.sim_snps(10)
#
## DATA is in alphanumeric tipname order
#DATA = MODEL.write_vcf().iloc[:, 9:]
#DATA[(DATA == 2) | (DATA == 3)] = 1
if model.model == 'ARD':
model.alpha, model.beta = params
lik = model.pruning_algorithm()
else:
model.alpha = params[0]
lik = model.pruning_algorithm()
return -lik
if __name__ == "__main__":
import time
from hogtie.utils import set_loglevel
set_loglevel("INFO")
TREE = toytree.rtree.imbtree(ntips=10, treeheight=1000)
DATA = np.array([1, 1, 0, 0, 1, 0, 0, 0, 0, 1])
start = time.time()
mod = BinaryStateModel(TREE, DATA, 'ER')
mod.optimize()
print(f"\n---- TIME: {time.time() - start:.5f} seconds")
#DATA = np.array([1, 0, 1, 0, 1, 0, 1, 0, 1, 0])
#od = BinaryStateModel(TREE, DATA, 'ARD')
#mod.optimize()
#DATA = np.array([1, 1, 1, 1, 1, 1, 0, 0, 0, 0])
#mod = BinaryStateModel(TREE, DATA, 'ER')