Source code for gluonts.nursery.gmm_tpp.simulation

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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")