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Copy pathCARLOS_TRAINING_TARGET.jl
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CARLOS_TRAINING_TARGET.jl
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using MAT
using ForwardDiff
using DiffBase
include("pro_anti.jl")
include("pro_anti_opto.jl")
function JJ_opto_plot(nPro, nAnti; opto_targets=[0.9 0.7], theta1=0.025, theta2=0.035, cbeta=0.003, verbose=false, pre_string="", zero_last_sigmas=0, seedrand=NaN, rule_and_delay_periods = [0.4], target_periods = [0.1], post_target_periods = [0.5], opto_periods = [-1 -1],opto_strength=1, nderivs=0, difforder=0,plot_conditions=false,model_details=false, model_params...) #set opto defaults!
if ~(size(opto_targets) == size(opto_periods)); error("opto parameters are bad"); end
nruns = length(rule_and_delay_periods)*length(target_periods)*length(post_target_periods)*size(opto_periods)[1]
nruns_each = length(rule_and_delay_periods)*length(target_periods)*length(post_target_periods)
cost1s = ForwardDiffZeros(size(opto_periods)[1], nruns_each, nderivs=nderivs, difforder=difforder)
cost2s = ForwardDiffZeros(size(opto_periods)[1], nruns_each, nderivs=nderivs, difforder=difforder)
hP = zeros(size(opto_periods)[1], nruns_each);
hA = zeros(size(opto_periods)[1], nruns_each);
dP = zeros(size(opto_periods)[1], nruns_each);
dA = zeros(size(opto_periods)[1], nruns_each);
hBP = zeros(size(opto_periods)[1], nruns_each);
hBA = zeros(size(opto_periods)[1], nruns_each);
if model_details
proVall = [];
antiVall = [];
opto_fraction = [];
pro_input = [];
anti_input = [];
end
n = totHitsP = totHitsA = totDiffsP = totDiffsA =nopto= 0
for kk=1:size(opto_periods)[1] # iterate over each opto inactivation period
nopto = 0;
# reset random number generator for each opto period, so it cant over fit noise samples
if ~isnan(seedrand); srand(seedrand); end
for i in rule_and_delay_periods
for j in target_periods
for k = post_target_periods
nopto += 1
# include this opto inactivation in the parameters to pass on
my_params = make_dict(["rule_and_delay_period", "target_period", "post_target_period","opto_period","opto_strength"], [i, j, k, opto_periods[kk,:], opto_strength], Dict(model_params))
# print("model params is " ); print(model_params); print("\n")
if typeof(plot_conditions)==Bool && ~plot_conditions
proVs, antiVs, proVall, antiVall, opto_fraction,pro_input,anti_input = run_ntrials_opto(nPro, nAnti; nderivs=nderivs, difforder=difforder, my_params...)
elseif typeof(plot_conditions)==Bool
proVs, antiVs, proVall, antiVall, opto_fraction,pro_input,anti_input = run_ntrials_opto(nPro, nAnti; plot_list=1:10, nderivs=nderivs, difforder=difforder, my_params...)
elseif plot_conditions[kk]
proVs, antiVs, proVall, antiVall, opto_fraction,pro_input,anti_input = run_ntrials_opto(nPro, nAnti; plot_list=1:10, nderivs=nderivs, difforder=difforder, my_params...)
else
proVs, antiVs, proVall, antiVall, opto_fraction,pro_input,anti_input = run_ntrials_opto(nPro, nAnti; nderivs=nderivs, difforder=difforder, my_params...)
end
hitsP = 0.5*(1 + tanh.((proVs[1,:]-proVs[4,:,])/theta1))
diffsP = tanh.((proVs[1,:,]-proVs[4,:])/theta2).^2
hitsA = 0.5*(1 + tanh.((antiVs[4,:]-antiVs[1,:,])/theta1))
diffsA = tanh.((antiVs[4,:,]-antiVs[1,:])/theta2).^2
# set up storage
hP[kk,nopto] = mean(hitsP);
hA[kk,nopto] = mean(hitsA);
dP[kk,nopto] = mean(diffsP);
dA[kk,nopto] = mean(diffsA);
hBP[kk,nopto] = sum(proVs[1,:] .>= proVs[4,:,])/nPro;
hBA[kk,nopto] = sum(proVs[4,:] .> proVs[1,:,])/nAnti;
if nPro>0 && nAnti>0
cost1s[kk,nopto] = (nPro*(mean(hitsP) - opto_targets[kk,1]).^2 + nAnti*(mean(hitsA) - opto_targets[kk,2]).^2)/(nPro+nAnti)
cost2s[kk,nopto] = -cbeta*(nPro*mean(diffsP) + nAnti*mean(diffsA))/(nPro+nAnti)
elseif nPro>0
cost1s[kk,nopto] = (mean(hitsP) - opto_targets[kk,1]).^2
cost2s[kk,nopto] = -cbeta*mean(diffsP)
else
cost1s[kk,nopto] = (mean(hitsA) - opto_targets[kk,2]).^2
cost2s[kk,nopto] = -cbeta*mean(diffsA)
end
end
end
end
end
cost1 = mean(cost1s)
cost2 = mean(cost2s)
if model_details
return cost1 + cost2, cost1s, cost2s, hP,hA,dP,dA,hBP,hBA, proVall, antiVall, opto_fraction, pro_input, anti_input
else
return cost1 + cost2, cost1s, cost2s, hP,hA,dP,dA,hBP,hBA
end
end
# find the best run from this farm
farmName = "LA";
farmnum=3;
# load farm and do a run
F = matread("goodfarms/farm_"*farmName*lpad(farmnum[1],4,0)*".mat")
model_params = symbol_key_ize(F["model_params"])
# define training noise function
train_func = (;params...) -> JJ_opto_plot(model_params[:nPro],model_params[:nAnti]; rule_and_delay_periods=F["rule_and_delay_periods"], theta1=model_params[:theta1], theta2=model_params[:theta2], post_target_periods=F["post_target_periods"], seedrand=F["sr"], cbeta=F["cb"], verbose=true,plot_conditions=[false, false, false,false,true], merge(make_dict(F["args"],F["pars"], merge(model_params, Dict(params))))...)
t_opto_scost, t_opto_scost1, t_opto_scost2, t_opto_hitsP,t_opto_hitsA, t_opto_diffsP, t_opto_diffsA, t_opto_bP, t_opto_bA = train_func(;:start_pro=>[-0.5,-0.5,-0.5,-0.5],:start_anti=>[-0.5,-0.5,-0.5,-0.5]);
#define test noise function
#test_func = (;params...) -> JJ_opto_plot(100,100; rule_and_delay_periods=F["rule_and_delay_periods"], theta1=model_params[:theta1], theta2=model_params[:theta2], post_target_periods=F["post_target_periods"], seedrand=F["test_sr"], cbeta=F["cb"], verbose=true,plot_conditions=[true,false,false,false,false],merge(make_dict(F["args"],F["pars"], merge(model_params, Dict(params))))...)
# run the model and make sure to include initial conditions
#opto_scost, opto_scost1, opto_scost2, opto_hitsP,opto_hitsA, opto_diffsP, opto_diffsA, opto_bP, opto_bA = test_func(;:start_pro=>[-0.5,-0.5,-0.5,-0.5],:start_anti=>[-0.5,-0.5,-0.5,-0.5] )
# look at results
epochs = ["control"; "full"; "rule"; "delay"; "target"]
[epochs round(t_opto_hitsP*100)/100 round(t_opto_hitsA*100)/100]
# Plot a hits% plot for this farm, compared to the targets
figure()
plot([1,2,3,4,5],t_opto_hitsP,color="red")
plot([1,2,3,4,5],t_opto_hitsA,color="blue")
plot([1,2,3,4,5],model_params[:opto_targets][:,1],color="red",linestyle="--")
plot([1,2,3,4,5],model_params[:opto_targets][:,2],color="blue",linestyle="--")
p_err = round(1.96*sqrt((1/1000)*t_opto_hitsP.*(1-t_opto_hitsP))*1000)/1000
a_err = round(1.96*sqrt((1/1000)*t_opto_hitsA.*(1-t_opto_hitsA))*1000)/1000
for i=1:5
plot([i,i], t_opto_hitsP[i]+[p_err[i], -p_err[i]],color="red")
plot([i,i], t_opto_hitsA[i]+[a_err[i], -a_err[i]],color="blue")
end
ylabel("hits %")
xlabel("condition")