Hi I am new to Julia, sorry for this basic question, I am doing some
parameter fitting, and would like to use a binary indicator variable (0/1)
to differentiate groups from my data. I have the following code:
f_Eprod = @ode_def Eprod begin dE = p1*exp(t*(p2+p2d*Ind_Var)) end p1 p2 p2d Ind_Var p=[20,-0.02,0.005,1.0] prob = ODEProblem(f_Eprod,[0.0],[0.0,7],p) sol=solve(prob)
Below I substitute the value of 1 in the parameter array by IndVar[i] to account for
grouping in the data. This array contains 0 and 1’s.
function prob_func(prob,i,repeat) ODEProblem(prob.f,initial_conditions[i],tspan[i],[20,-0.02,0.005,IndVar[i],saveat=tspan[i]) end monte_prob = MonteCarloProblem(prob,prob_func=prob_func)
After setting a loss function, I am not sure how to set Optim.optimizer to avoid having Ind_Var being treated as a parameter to be optimized. Is there a way to handle this prior to the optimization? Thank you.