Functions declared twice with two draws2data2draws in one model #14
Description
Semantic error in '/Users/hyunjimoon/Dropbox/15879-Fall2022/Homeworks/HW7/stanify/stan_files/Inven_6est/Inven_6est_functions.stan', line 65, column 0, included from
'/Users/hyunjimoon/Dropbox/15879-Fall2022/Homeworks/HW7/stanify/stan_files/Inven_6est/Inven_6est_draws2data.stan', line 2, column 0:
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63: }
64:
65: real lookupFunc__table_for_order_fulfillment(real x){
^
66: // x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.0)
67: // y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0, 1.0)
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Function 'lookupFunc__table_for_order_fulfillment' has already been declared to for signature
(real) => real
make: *** [/Users/hyunjimoon/Dropbox/15879-Fall2022/Homeworks/HW7/stanify/stan_files/Inven_6est/Inven_6est_draws2data.hpp] Error 1
Command ['make', 'STANCFLAGS+=--include-paths=/Users/hyunjimoon/Dropbox/15879-Fall2022/Homeworks/HW7/stanify/stan_files/Inven_6est', '/Users/hyunjimoon/Dropbox/15879-Fall2022/Homeworks/HW7/stanify/stan_files/Inven_6est/Inven_6est_draws2data']
error during processing No such file or directory
when I checked stan function file, functions were declared twice
real lookupFunc__table_for_order_fulfillment(real x){
// x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.0)
// y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0, 1.0)
real slope;
real intercept;
if(x <= 0.2){
intercept = 0.0;
slope = (0.2 - 0.0) / (0.2 - 0.0);
return intercept + slope * (x - 0.0);
}
...
else if(x <= 2.0){
intercept = 1.0;
slope = (1.0 - 1.0) / (2.0 - 2.0);
return intercept + slope * (x - 2.0);
}
return 1.0;
}
real lookupFunc__table_for_order_fulfillment(real x){
// x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.0)
// y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0, 1.0)
real slope;
real intercept;
if(x <= 0.2){
intercept = 0.0;
slope = (0.2 - 0.0) / (0.2 - 0.0);
return intercept + slope * (x - 0.0);
}
...
else if(x <= 2.0){
intercept = 1.0;
slope = (1.0 - 1.0) / (2.0 - 2.0);
return intercept + slope * (x - 2.0);
}
return 1.0;
}
// Begin ODE declaration
real dataFunc__customer_order_rate(real time, real time_step){
// DataStructure for variable customer_order_rate
real slope;
real intercept;
if(time <= time_step * 1){
intercept = 146376;
real local_time_step = time_step * 1 - time_step * 0;
slope = (147079 - 146376) / local_time_step;
return intercept + slope * (time - time_step * 0);
}
...
else if(time <= time_step * 19){
intercept = 183682;
real local_time_step = time_step * 19 - time_step * 18;
slope = (183318 - 183682) / local_time_step;
return intercept + slope * (time - time_step * 18);
}
return 183318;
}
real dataFunc__process_noise_uniform_driving(real time, real time_step){
// DataStructure for variable process_noise_uniform_driving
real slope;
real intercept;
if(time <= time_step * 1){
intercept = 0.1236860198383215;
real local_time_step = time_step * 1 - time_step * 0;
slope = (-0.35346438681712344 - 0.1236860198383215) / local_time_step;
return intercept + slope * (time - time_step * 0);
...
else if(time <= time_step * 19){
intercept = 0.38848170528082115;
real local_time_step = time_step * 19 - time_step * 18;
slope = (-0.4071190492640847 - 0.38848170528082115) / local_time_step;
return intercept + slope * (time - time_step * 18);
}
return -0.4071190492640847;
}
real dataFunc__customer_order_rate(real time, real time_step){
// DataStructure for variable customer_order_rate
real slope;
real intercept;
if(time <= time_step * 1){
intercept = 146376;
real local_time_step = time_step * 1 - time_step * 0;
slope = (147079 - 146376) / local_time_step;
return intercept + slope * (time - time_step * 0);
}
...
else if(time <= time_step * 19){
intercept = 0.38848170528082115;
real local_time_step = time_step * 19 - time_step * 18;
slope = (-0.4071190492640847 - 0.38848170528082115) / local_time_step;
return intercept + slope * (time - time_step * 18);
}
return -0.4071190492640847;
}
vector vensim_ode_func(real time, vector outcome, real time_step, real wip_adjustment_time, real safety_stock_coverage, real target_delivery_delay, real minimum_order_processing_time, real process_noise_scale, real manufacturing_cycle_time, real inventory_adjustment_time){
vector[7] dydt; // Return vector of the ODE function
// State variables
real expected_order_rate = outcome[1];
real process_noise = outcome[2];
real work_in_process_inventory = outcome[3];
real backlog = outcome[4];
real production_rate_stocked = outcome[5];
real inventory = outcome[6];
real production_start_rate_stocked = outcome[7];
real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage;
real desired_inventory = desired_inventory_coverage * expected_order_rate;
real adjustment_from_inventory = (desired_inventory - inventory) / inventory_adjustment_time;
real desired_production = fmax(0, expected_order_rate + adjustment_from_inventory);
real desired_wip = manufacturing_cycle_time * desired_production;
real adjustment_for_wip = (desired_wip - work_in_process_inventory) / wip_adjustment_time;
real desired_production_start_rate = fmax(0, desired_production + adjustment_for_wip);
real production_start_rate = fmax(0, desired_production_start_rate);
real desired_minus_shadow_psr = production_start_rate - production_start_rate_stocked;
real production_start_rate_stocked_change_rate = desired_minus_shadow_psr / time_step;
real desired_shipment_rate = backlog / target_delivery_delay;
real production_rate = work_in_process_inventory / manufacturing_cycle_time * fmax(0, 1 + process_noise);
real desired_minus_shadow_pr = production_rate - production_rate_stocked;
real production_rate_stocked_change_rate = desired_minus_shadow_pr / time_step;
real production_rate_stocked_dydt = production_rate + production_rate_stocked_change_rate;
real time_to_average_order_rate = 8;
real change_in_exp_orders = (dataFunc__customer_order_rate(time, time_step) - expected_order_rate) / time_to_average_order_rate;
real maximum_shipment_rate = inventory / minimum_order_processing_time;
real order_fulfillment_ratio = lookupFunc__table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate);
real correlation_time_over_time_step = 100;
real white_noise = 4.89 * correlation_time_over_time_step ^ 0.5 * dataFunc__process_noise_uniform_driving(time, time_step) * process_noise_scale;
real shipment_rate = desired_shipment_rate * order_fulfillment_ratio;
real order_fulfillment_rate = shipment_rate;
real order_rate = dataFunc__customer_order_rate(time, time_step);
real backlog_dydt = order_rate - order_fulfillment_rate;
real white_minus_process = white_noise - process_noise;
real correlation_time = time_step * correlation_time_over_time_step;
real process_noise_change_rate = white_minus_process / correlation_time;
real expected_order_rate_dydt = change_in_exp_orders;
real production_start_rate_stocked_dydt = production_start_rate + production_start_rate_stocked_change_rate;
real process_noise_dydt = process_noise_change_rate;
real work_in_process_inventory_dydt = production_start_rate - production_rate;
real inventory_dydt = production_rate - shipment_rate;
dydt[1] = expected_order_rate_dydt;
dydt[2] = process_noise_dydt;
dydt[3] = work_in_process_inventory_dydt;
dydt[4] = backlog_dydt;
dydt[5] = production_rate_stocked_dydt;
dydt[6] = inventory_dydt;
dydt[7] = production_start_rate_stocked_dydt;
return dydt;
}