Am 23.03.20 um 15:25 schrieb gege:
> Dear professor Peixoto,
> Thank you for reply!
>
> The dynamic example in the document sets 100 initial infected points and
> iterates for 10 times simultaneously. So the epidemic process is ongoing on
> a network and time T belongs to [0,9]. Then the time series is copied to a
> same but masked network. Am I correct?
In the example in the documentation the time series is copied to an
empty graph, which will be the starting point of the reconstruction.
> But I still wonder how to control the
> number of infected events per node. I noted that infected nodes are randomly
> selected.
This is not controlled explicitly; after you generate the time series
you count the number of times each node flipped, and you average.
> Moreover, Should I set like this for the Ising model?
> "
> for i in range(1000):
> si_state = gt.IsingGlauberState(g, beta=.02)
> s = [si_state.get_state().copy()]
> si_state.iterate_async()
> s.append(si_state.get_state().copy())
> # Each time series should be represented as a single vector-valued
> # vertex property map with the states for each note at each time.
> s = gt.group_vector_property(s)
> ss.append(s)
> "
Since the Ising reconstruction expects uncorrelated samples, I think
it's best to use only one "time series", i.e.
si_state = gt.IsingGlauberState(g, beta=.02)
ss = [si_state.get_state().copy()]
for i in range(1000):
si_state.iterate_async()
ss.append(si_state.get_state().copy())
ss = gt.group_vector_property(ss)
Best,
Tiago
--
Tiago de Paula Peixoto <ti...@skewed.de>
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