Error Running Sampler for Gaussian mixture model - Help Needed

Modeling
1

I am working on implementing Gaussian mixture model on iris dataset. The code is as shown below.

require(rstan)
library(datasets)
data(iris)
y=array(iris[3:4])
mixture_data=list(N=150, D=2, K=3, y=y)

mixture_model<-'
data {
 int D; //number of dimensions
 int K; //number of gaussians
 int N; //number of data
 vector[D] y[N]; //data
}

parameters {
 simplex[K] theta; //mixing proportions
 vector[D] mu[K]; //mixture component means
 cholesky_factor_corr[D] L[K]; //cholesky factor of correlation
 vector<lower=0>[K] sigma[D]; // standard deviations

}
transformed parameters{
  cholesky_factor_cov[D, D] cov[K];   
  for (k in 1:K) {
    cov[k] = diag_pre_multiply(sigma[k], L[k]);
  }
}
model {
 real ps[K];
 
 for(k in 1:K){
 mu[k] ~ normal(0, 10);
 L[k] ~ lkj_corr_cholesky(2);
 sigma[k] ~ exponential(1);

 }
 

 for (n in 1:N){
 for (k in 1:K){
 ps[k] = log(theta[k])+multi_normal_cholesky_lpdf(y[n] | mu[k], cov[k]); //increment log probability of the gaussian
 }
 target += log_sum_exp(ps);
 }

}
'
fit=stan(model_code=mixture_model, data=mixture_data, iter=1000, chains=4, init_r=0.5)

The model works perfectly for K=2 and does recover parameters correctly. The sampler throws an error for K = 3.


SAMPLING FOR MODEL 'd45eb236ae863de793b59e995d2e8072' NOW (CHAIN 1).
Chain 1: Unrecoverable error evaluating the log probability at the initial value.
Chain 1: Exception: diag_pre_multiply: m1.size() (3) and m2.rows() (2) must match in size  (in 'model1d9654deb7fc_d45eb236ae863de793b59e995d2e8072' at line 19)

[1] "Error in sampler$call_sampler(args_list[[i]]) : "                                                                                                        
[2] "  Exception: diag_pre_multiply: m1.size() (3) and m2.rows() (2) must match in size  (in 'model1d9654deb7fc_d45eb236ae863de793b59e995d2e8072' at line 19)"
error occurred during calling the sampler; sampling not done
2

Looks like you have transposed the dimensions for sigma. It should be

 vector<lower=0>[D] sigma[K]; // standard deviations
3

Hey, Thanks

I some how missed the dimensions.