import numpy as np
from pyhawkes.models import DiscreteTimeNetworkHawkesModelSpikeAndSlab
def test_compute_rate():
K = 1
T = 100
dt = 1.0
network_hypers = {'c': np.zeros(K, dtype=np.int), 'p': 1.0, 'kappa': 10.0, 'v': 10*5.0}
true_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=K, dt=dt,
network_hypers=network_hypers)
S,R = true_model.generate(T=T)
print("Expected number of events: ", np.trapz(R, dt * np.arange(T), axis=0))
print("Actual number of events: ", S.sum(axis=0))
print("Lambda0: ", true_model.bias_model.lambda0)
print("W: ", true_model.weight_model.W)
print("")
R_test = true_model.compute_rate()
assert np.allclose(R, R_test)
def test_generate_statistics():
K = 1
T = 100
dt = 1.0
network_hypers = {'c': np.zeros(K, dtype=np.int), 'p': 1.0, 'kappa': 10.0, 'v': 10*5.0}
true_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=K, dt=dt,
network_hypers=network_hypers)
S,R = true_model.generate(T=T)
E_N = np.trapz(R, dt * np.arange(T), axis=0)
std_N = np.sqrt(E_N)
N = S.sum(axis=0)
assert np.all(N >= E_N - 3*std_N), "N less than 3std below mean"
assert np.all(N <= E_N + 3*std_N), "N more than 3std above mean"
print("Expected number of events: ", E_N)
print("Actual number of events: ", S.sum(axis=0))
test_compute_rate()
test_generate_statistics()
