def test_compute_rate():
K = 1
T = 100
dt = 1.0
true_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=K, dt=dt)
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_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)
