Closed YUJINGl3 closed 2 years ago
ChrisRackauckas
commented
2 years ago @everywhere function Generating_data(n,k, param, rn)
timept = 10:div(3000, k):3000
u₀ = [105,110,100,5]
tspan = (0.0,3000.0)
dprob = DiscreteProblem(rn, u₀, tspan, param)
jprob = JumpProblem(rn, dprob, Direct())
ensprob = EnsembleProblem(jprob)
...that's not a good idea. You should just create a single JumpProblem and remake it from there.
YUJINGl3
commented
2 years ago @everywhere function Generating_data(n,k, param, rn) timept = 10:div(3000, k):3000 u₀ = [105,110,100,5] tspan = (0.0,3000.0) dprob = DiscreteProblem(rn, u₀, tspan, param) jprob = JumpProblem(rn, dprob, Direct()) ensprob = EnsembleProblem(jprob) ...that's not a good idea. You should just create a single JumpProblem and remake it from there.
Thanks! I will have a try with that.
YUJINGl3
commented
2 years ago Following the advise from @ChrisRackauckas
p = [0.005, 0.008, 0.36, 1, 0.1, 0.00135, 0.01, 0.01, 1, 1, 0.01, 5, 1, 0.005, 1, 0.01] timept = 10:div(3000, 10):3000 u₀ = [105,110,100,5] tspan = (0.0,3000.0) dprob = DiscreteProblem(rn, u₀, tspan, p) jprob = JumpProblem(rn, dprob, Direct(),save_positions=(false,false)) function Generating_data(n,k, param) ensprob = EnsembleProblem(jprob,prob_func=(p,i,r)->remake(p,p = param), safetycopy=false) sol = solve(ensprob, SSAStepper(),EnsembleThreads(), trajectories=n,saveat=1) .... end
The code works perfectly well. I will close the issue. Thanks a lot!
I'm using Approximate Bayes computation to inference parameters using data points driven from the Jump problems. The simulated works for the first few turns but runs into error after large number of simulations. Here is my code for simulating data and a quite long error.