EoN.EBCM_pref_mix_discrete¶

EoN.EBCM_pref_mix_discrete(N, Pk, Pnk, p, rho=None, tmin=0, tmax=100, return_full_data=False)[source]¶

Encodes the discrete version of exercise 6.21 of Kiss, Miller, & Simon. Please cite the book if using this algorithm.

I anticipate eventually adding an option so that the initial condition is not uniformly distributed. So could give rho_k

N positive integer: number of nodes.
Pk dict (could also be an array or a list): Pk[k] is the probability a random node has degree k.
Pnk dict of dicts (possibly array/list): Pnk[k1][k2] is the probability a neighbor of a degree k1 node has degree k2.
p positive float (0 <= p <= 1): transmission probability
rho number (optional): initial proportion infected. Defaults to 1/N.
tmin number (default 0): minimum time
tmax number (default 100): maximum time
tcount integer (default 1001): number of time points for data (including end points)
return_full_data boolean (default False): whether to return theta or not

if return_full_data == False:: returns t, S, I, R, all numpy arrays
if …== True: returns t, S, I, R and theta, where theta is a dict and theta[k] is the thetas for given k.