def test_limit(parameters2):
mu, alpha, beta = parameters2
loc_limit = 10
try:
pyhawkes.sim_exp_hawkes(mu,
alpha,
beta,
length=T,
max=loc_limit,
rseed=seed)
except RuntimeError:
assert True
else:
assert False
def test_stationarity():
mu = np.array([0.15, 0.15], dtype=float)
alpha = np.array([[100, 100], [100, 100]], dtype=float)
beta = np.array([[1, 1], [1, 1]], dtype=float)
try:
pyhawkes.sim_exp_hawkes(mu,
alpha,
beta,
length=T,
max=limit,
rseed=seed)
except RuntimeError:
assert True
else:
assert False
def hawkes_list(parameters2):
mu, alpha, beta = parameters2
hawkes = pyhawkes.sim_exp_hawkes(mu,
alpha,
beta,
length=T,
max=limit,
rseed=seed)
return hawkes
def comp_list_stat_int(pos):
mu, alpha, beta = parameters3
hawkes = pyhawkes.sim_exp_hawkes(mu,
alpha,
beta,
T,
max=limit,
rseed=seed)
comp = pyhawkes.comp_exp_hawkes(mu, alpha, beta, hawkes, T)
return comp[pos]
def test_exp_gen():
mu = np.array([0.15], dtype=float)
rho = np.array([[1]], dtype=float)
m = np.array([[1]], dtype=float)
M = np.array([[1]], dtype=int)
epsilon = np.array([[0.5]], dtype=float)
n = np.array([[0.5]], dtype=float)
alpha = np.array([[0.5]], dtype=float)
beta = np.array([[1]], dtype=float)
hawkes_gen = pyhawkes.sim_gen_hawkes(mu,
rho,
m,
M,
epsilon,
n,
pfunc=base,
length=T,
max=limit,
rseed=seed)
hawkes_exp = pyhawkes.sim_exp_hawkes(mu,
alpha,
beta,
length=T,
max=limit,
rseed=seed)
comp_gen = pyhawkes.comp_gen_hawkes(mu,
rho,
m,
M,
epsilon,
n,
events=hawkes_gen,
pfunc_int=base_int,
length=T)
comp_exp = pyhawkes.comp_exp_hawkes(mu,
alpha,
beta,
events=hawkes_exp,
length=T)
_, p_value_gen = scipy.stats.kstest(comp_gen[0], 'expon', args=(0, 1))
_, p_value_exp = scipy.stats.kstest(comp_exp[0], 'expon', args=(0, 1))
ll_gen = pyhawkes.lik_gen_hawkes(mu,
rho,
m,
M,
epsilon,
n,
pfunc=base,
pfunc_int=base_int,
events=hawkes_gen,
length=T)
ll_exp = pyhawkes.lik_exp_hawkes(mu, alpha, beta, hawkes_exp, length=T)
assert len(hawkes_exp[0]) == len(hawkes_gen[0])
assert np.round(p_value_exp, 4) == np.round(p_value_gen, 4)
assert np.abs(ll_exp - ll_gen) < 10
def test_ll(hawkes_list_stat, parameters3):
mu, alpha, beta = parameters3
mu = np.array([0.05, 0.3, 0.25])
alpha = np.array([[0.4, 0.3, 0.1], [0.3, 0.4, 0.23], [0.18, 0.43, 0.31]])
beta = np.array([[1, 2, 9], [1.2, 1.8, 1.4], [4.9, 1.15, 8.5]])
hawkes = pyhawkes.sim_exp_hawkes(mu,
alpha,
beta,
length=100000,
max=200000,
rseed=123)
log_likelihood = pyhawkes.lik_exp_hawkes(mu, alpha, beta, hawkes, T)
assert np.round(log_likelihood) == 204777
def hawkes_list_stat(parameters3):
mu, alpha, beta = parameters3
hawkes = pyhawkes.sim_exp_hawkes(mu, alpha, beta, T, max=limit, rseed=seed)
return hawkes
mu2d = np.array([0.25, 0.2, 0.25, 0.2], dtype=float)
mu3d = np.array([0.13, 0.17, 0.13, 0.17, 0.13, 0.17], dtype=float)
"""
1D
"""
# EXPONENTIAL WITH CONSTANT BASE INTENSITY
mu = np.array([0.5])
alpha = np.array([[0.4]])
beta = np.array([[0.8]])
begin = time.time()
hawkes = pyhawkes.sim_exp_hawkes(mu, alpha, beta, length=T, max=max_jumps, rseed=seed)
end = time.time()
logger.info("1D Hawkes simulation with an exponential kernel with constant base intensity of length "
"{} lasted for {} s".format(len(hawkes[0]), end - begin))
# EXPONENTIAL WITH QUADRATIC BASE INTENSITY
rho = 1 / beta
m = np.array([[1]], dtype=float)
M = np.array([[1]], dtype=int)
epsilon = np.array([[0.2]], dtype=float)
n = alpha / beta
begin = time.time()
hawkes = pyhawkes.sim_gen_hawkes(mu1d, rho, m, M, epsilon, n, length=T, max=max_jumps,
rseed=seed, pfunc=base)
end = time.time()
logger.info("1D Hawkes simulation with an exponential kernel with quadratic base intensity of length "