fit with fixed beta

CovidAlertVaccinationModel/src/ABM/parameter_optimization.jl

@@ -15,15 +15,13 @@ function solve_w_parameters(default_p, p_names, new_p_list)
 end
 
 function fit_distribution_parameters(p_tuple)
-    p_names = (:ω,:β_y,:β_m,:β_o,:π_base_y,:π_base_m,:π_base_o)
+    p_names = (:ω,:π_base_y,:π_base_m,:π_base_o,:ζ)
     priors = Factored(
         Uniform(0.0,0.01),
-        Uniform(0.0,0.5),  
-        Uniform(0.0,0.5),  
-        Uniform(0.0,1.0),  
         Uniform(-5.0,3.0),
         Uniform(-5.0,3.0),
-        Uniform(-5.0,3.0),  
+        Uniform(-5.0,3.0), 
+        Uniform(1.0,2.0),   
     )
     #simulation begins in july
     #60 days for opinion dynamics to stabilize, then immunization begins in september,
@@ -61,15 +59,15 @@ function fit_distribution_parameters(p_tuple)
     end 
     #    display(cost((0.005,0.00075, 0.00063,0.75,-1.4,-1.4,-0.95)))
     
-    out = smc(priors,cost; verbose = true, nparticles = 1000, parallel = true)
+    out = smc(priors,cost; verbose = true, nparticles = 250, parallel = true)
     return NamedTuple{p_names}(ntuple(i -> out.P[i].particles,length(p_names)))
 end
 function fit_parameters(default_parameters)
     # pre_inf_behaviour_parameters_path =joinpath(PACKAGE_FOLDER,"abm_parameter_fits","pre_inf_behaviour_parameters.dat")
     # post_inf_behaviour_parameters_path = joinpath(PACKAGE_FOLDER,"abm_parameter_fits","post_inf_behaviour_parameters.dat")
     fit_all_parameters_path = joinpath(PACKAGE_FOLDER,"abm_parameter_fits","fit_all_parameters.dat")
-    # output = fit_distribution_parameters(default_parameters)
-    # serialize(fit_all_parameters_path,output)
+    output = fit_distribution_parameters(default_parameters)
+    serialize(fit_all_parameters_path,output)
 
     
     fitted_parameter_tuple = deserialize(fit_all_parameters_path)
@@ -88,13 +86,13 @@ function plot_fitting_posteriors(fname,particles_tuple,parameters)
     avg_populations = [0.0,0.0,0.0]
     names = keys(particles_tuple)
     samples = collect(zip(particles_tuple...))
-    for p_set in sample(samples,1)
+    for p_set in sample(samples,30)
         p_set_named = NamedTuple{names}(p_set)
         sol = abm(merge(parameters,p_set_named),output_recorder)
         avg_populations .+= length.(sol.index_vectors)
         fit!(stat_recorder,output_recorder)
     end
-    avg_populations ./= 1
+    avg_populations ./= 30
     p = plot_model(nothing,[nothing],[stat_recorder],parameters.infection_introduction_day,parameters.immunization_begin_day)
     savefig(p, "$fname.pdf")