This is cmdstanr version 0.0.0.9000
- Online documentation and vignettes at mc-stan.org/cmdstanr
- CmdStan path set to: /home/saumyap/.cmdstanr/cmdstan
- Use set_cmdstan_path() to change the path

Model executable is up to date!
functions {
  real parallel_loc(int[] slice_location,int start, int end, matrix theta_loc_mu) {
    return categorical_lpmf(slice_location | softmax(to_vector(theta_loc_mu[start:end])));
  }
  
  real parallel_rat(int[] slice_rating,int start, int end, matrix theta_rat_mu) {
    return categorical_lpmf(slice_rating | softmax(to_vector(theta_rat_mu[start:end])));
  }
  
  real parallel_dom(int[] slice_domain,int start, int end, matrix theta_dom_mu) {
    return categorical_lpmf(slice_domain | softmax(to_vector(theta_dom_mu[start:end])));
  }
  
}
data{
  int<lower = 0> N; // number of instances in the data
  int<lower = 0> K; // number of covariates 
  int            R; // No. of possible ratings 
  int            L; // No. of Locations
  int            D; // No. of Unique domains
  matrix[N, K]    s ;// matrix of sensitive attributes
  int            location[N]; // location
  int            domain[N];   //domain
  int            rating[N]; //  rating 
  
}

transformed data{

	 vector[K] zero_K;
	 vector[K] one_K;
	 
	 vector[R] zero_R;
	 vector[R] one_R;
	 
	 vector[L] zero_L;
	 vector[L] one_L;
	 
	 vector[D] zero_D;
	 vector[D] one_D;
	 
	 
	 zero_K = rep_vector(0,K);
	 one_K = rep_vector(1,K);
	 
	 zero_R = rep_vector(0,R);
	 one_R = rep_vector(1,R);
	 
	 zero_L = rep_vector(0,L);
	 one_L = rep_vector(1,L);
	 
	 zero_D = rep_vector(0,D);
	 one_D = rep_vector(1,D);

}

parameters{
  
  vector[N] attitude; //latent Variables
  
  matrix [K,R] eta_s_rating;
  row_vector[R] eta_attitude_rating;
   
  matrix [K,L] eta_s_location;
  row_vector[L] eta_attitude_location;
  
  matrix [K,D] eta_s_domain;
  row_vector[D] eta_attitude_domain;
  
  real<lower=0> sigma_g_Sq;


}

transformed parameters{

 real<lower=0> sigma_g;
 matrix[N,R] theta_rat_mu; 
 matrix[N,L] theta_loc_mu;  
 matrix[N,D] theta_dom_mu; 
 
 sigma_g = sqrt(sigma_g_Sq); // SD 
 
 theta_rat_mu = attitude* eta_attitude_rating + s *eta_s_rating; 
 theta_loc_mu = attitude* eta_attitude_location + s * eta_s_location; 
 theta_dom_mu = attitude* eta_attitude_domain + s * eta_s_domain; 

}

model{
  
  int grainsize =1;
  attitude ~ normal(0,1);
  
   for (i in 1:K)
      eta_s_rating[i] ~ normal(zero_R, one_R); 
  
  for (i in 1:1)
    eta_attitude_rating[i] ~ normal(zero_R, one_R); 
    
   for (i in 1:K)
      eta_s_location[i] ~ normal(zero_L, one_L); 
  
  for (i in 1:1)
    eta_attitude_location[i] ~ normal(zero_L, one_L); 
  
  
  for (i in 1:K)
      eta_s_domain[i] ~ normal(zero_D, one_D); 
  
  for (i in 1:1)
    eta_attitude_domain[i] ~ normal(zero_D, one_D); 
  
  
  sigma_g_Sq ~ inv_gamma(1, 1);


 // Driven by normal prior and categorical distribution
 for (i in 1:N){
 
    target += reduce_sum(parallel_loc, location, grainsize, theta_loc_mu);
   
  }

 for (i in 1:N){
   
    target += reduce_sum(parallel_rat, rating, grainsize, theta_rat_mu);
  }

 for (i in 1:N){

 	 target += reduce_sum(parallel_dom, domain, grainsize, theta_dom_mu);

 }
    
}
Warning message:
In readLines(self$stan_file()) :
  incomplete final line found on '~/.cmdstanr/cmdstan-2.23-rc2/examples/parallel_test/tptrain_parallelV1.stan'
Running MCMC with 2 chain(s) on 1 core(s)...

Running ./tptrain_parallelV1 'id=1' random 'seed=367548213' data \
  'file=/tmp/Rtmpz7SZva/standata-263e657be2a9.json' output \
  'file=/tmp/Rtmpz7SZva/tptrain_parallelV1-202004202221-1-54372d.csv' \
  'refresh=100' 'method=sample' 'num_samples=100' 'num_warmup=100' \
  'save_warmup=0' 'algorithm=hmc' 'engine=nuts' adapt 'engaged=1'