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Added reward values to bart_ewmv #2

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Added reward values to bart_ewmv #2

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b87a6cb
Added reward values to bart_ewmv
mkdir-eherms Mar 5, 2023
17308ae
changing value of balloon to reward
mkdir-eherms Mar 18, 2023
2f531d2
Fix bart_ewmv formula to properly include reward
mkdir-eherms Jan 27, 2024
31b5194
Add in u_lose_array to bart_ewmv
mkdir-eherms Jan 29, 2024
d379248
Update missed function
mkdir-eherms Jan 29, 2024
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28 changes: 19 additions & 9 deletions commons/stan_files/bart_ewmv.stan
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Original file line number Diff line number Diff line change
Expand Up @@ -71,17 +71,21 @@ model {
real p_burst = phi[j];

for (k in 1:Tsubj[j]) {
real u_gain = 1;
// real u_gain = 1;
// Reward values based on pump number
row_vector[11] u_gain_array = [0.05, 0.10, 0.10, 0.30, 0.40, .50, .60, .70, .70, .80, .90];
row_vector[11] u_lose_array = [0, 0.05, 0.15, 0.25, 0.55, 0.95, 1.45, 2.05, 2.75, 3.45, 4.25, 5.15];
real u_loss;
real u_pump;
real u_stop = 0;
real delta_u;

for (l in 1:(pumps[j, k] + 1 - explosion[j, k])) {
u_loss = (l - 1);

u_pump = (1 - p_burst) * u_gain - lambda[j] * p_burst * u_loss +
rho[j] * p_burst * (1 - p_burst) * (u_gain + lambda[j] * u_loss)^2;
// Because our reward gain is not constant across the trail, we need to use the summation of the previous rewards.
// This is pre-populated in the u_lose_array
// u_loss = (l - 1);

u_pump = (1 - p_burst) * u_gain_array[l] - lambda[j] * p_burst * u_lose_array[l] + rho[j] * p_burst * (1 - p_burst) * (u_gain_array[l] + lambda[j] * u_lose_array[l])^2;
// u_stop always equals 0.

delta_u = u_pump - u_stop;
Expand Down Expand Up @@ -131,18 +135,24 @@ generated quantities {
log_lik[j] = 0;

for (k in 1:Tsubj[j]) {
real u_gain = 1;
// real u_gain = 1;
// Reward values based on pump number
row_vector[11] u_gain_array = [0.05, 0.10, 0.10, 0.30, 0.40, .50, .60, .70, .70, .80, .90];
row_vector[11] u_lose_array = [0, 0.05, 0.15, 0.25, 0.55, 0.95, 1.45, 2.05, 2.75, 3.45, 4.25, 5.15];
real u_loss;
real u_pump;
real u_stop = 0;
real delta_u;

for (l in 1:(pumps[j, k] + 1 - explosion[j, k])) {
// u_gain always equals r ^ rho.
u_loss = (l - 1);

// Because our reward gain is not constant across the trail, we need to use the summation of the previous rewards.
// This is pre-populated in the u_lose_array
// u_loss = (l - 1);

u_pump = (1 - p_burst) * u_gain - lambda[j] * p_burst * u_loss +
rho[j] * p_burst * (1 - p_burst) * (u_gain + lambda[j] * u_loss)^2;
// Updated to use u_gain_array values
u_pump = (1 - p_burst) * u_gain_array[l] - lambda[j] * p_burst * u_lose_array[l] + rho[j] * p_burst * (1 - p_burst) * (u_gain_array[l] + lambda[j] * u_lose_array[l])^2;
// u_stop always equals 0.

delta_u = u_pump - u_stop;
Expand Down