EoN.EBCM_pref_mix_discrete¶
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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
Arguments: - 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
Returns: - 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.