def simulate_hawkes(self, model_name):
self.model_name = model_name
def y_func_pos(t_values):
y_values = 0.02 * np.exp(-t_values)
return y_values
def y_func_neg(t_values):
y_values = -0.1 * np.exp(-t_values)
return y_values
if model_name == 'hawkes_neg':
y_func = y_func_neg
elif model_name == 'hawkes_pos':
y_func = y_func_pos
t_values = np.linspace(0, 101, 100)
y_values = y_func(t_values)
tf = TimeFunction([t_values, y_values],
inter_mode=TimeFunction.InterLinear,
dt=0.1)
tf_kernel = HawkesKernelTimeFunc(tf)
N_enodes = self.G_e2n.number_of_nodes() # regarded as 'N_enodes' types
base_int = 0.2
baselines = [base_int for i in range(N_enodes)]
kernels = [[] for i in range(N_enodes)]
for i in range(N_enodes):
for j in range(N_enodes):
if i == j:
# kernels[i].append(HawkesKernel0())
kernels[i].append(HawkesKernelExp(.1, 4)) # self influence
else:
if self.G_e2n.has_edge(self.idx_elabel_map[i],
self.idx_elabel_map[j]):
kernels[i].append(tf_kernel)
else:
kernels[i].append(HawkesKernel0())
hawkes = SimuHawkes(kernels=kernels,
baseline=baselines,
verbose=False,
seed=self.seed)
hawkes.threshold_negative_intensity(allow=True)
run_time = 100
hawkes.end_time = run_time
hawkes.simulate()
timestamps = hawkes.timestamps
self.save(timestamps, self.model_name)
def test_hawkes_negative_intensity(self):
"""...Test simulation with negative kernel
"""
run_time = 40
hawkes = SimuHawkes(n_nodes=1, end_time=run_time, verbose=False,
seed=1398)
kernel = HawkesKernelExp(-1.3, .8)
hawkes.set_kernel(0, 0, kernel)
hawkes.set_baseline(0, 0.3)
hawkes.threshold_negative_intensity()
dt = 0.1
hawkes.track_intensity(dt)
hawkes.simulate()
self.assertAlmostEqual(hawkes.tracked_intensity[0].min(), 0)
self.assertAlmostEqual(hawkes.tracked_intensity[0].max(),
hawkes.baseline[0])
self.assertGreater(hawkes.n_total_jumps, 1)