Source code for gluonts.nursery.gmm_tpp.simulation
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# Licensed under the Apache License, Version 2.0 (the "License").
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import numpy as np
[docs]def thinning_sampler(rng, lamb, xmin=0, lamb_min=1e-10):
"""
Ogata's Thinning algorithm for time-varying exponential distribution with
monotone-decreasing intensity function.
"""
while lamb(xmin) > lamb_min:
dx = -np.log(rng.rand()) / lamb(xmin)
x = xmin + dx
accept_rate = lamb(x) / lamb(xmin)
if rng.rand() < accept_rate:
return x
xmin = x
raise ValueError(
f"require lamb({xmin})>{lamb_min} to guarantee cdf(infty)=1"
)
[docs]def Hawkes(rng, background, kernel, xmin, xmax, N_max=1e6):
"""
requires int x kernel(x)<infty.
"""
X = []
while len(X) < N_max:
lamb = lambda x: background + np.sum( # noqa
[kernel(x - xi) if x >= xi else 0 for xi in X]
)
x = thinning_sampler(rng, lamb, max(X + [xmin]))
if x > xmax: # out of range
return X
X.append(x)
raise ValueError(f"N>{N_max}; check if int_0^infty kernel < inf")