Softmax by hand

I’m working myself around the multinomial regression. At some point I have to change my model, more specifically my denominator dynamically (softmax). In order to accomplish that, I tried to work iteratively and change one thing at a time.

data {
int<lower=2> C; // # of alternatives choices
int<lower=1> N;	// # of observations
int<lower=1> K; // # of covariates
int<lower=1,upper=C> Y[N]; // observed choices
matrix[C,K] X[N]; // matrix of attributes for each N
 parameters {
    vector[K] beta;
model {
    matrix[K,N] xb;
    vector[N] s;
    // priors
beta ~ normal(0, 5); 

  for (i in 1:N){
    xb[,i] = exp(X[i]*beta);
    s[i] = sum(xb[,i]);

    Y[i] ~ categorical(xb[,i] / s[i]);

Obviously I have missed something because this fails miserably.

Unrecoverable error evaluating the log probability at the initial value.
Exception: matrix[multi,uni] assign sizes: lhs (4) and assigning variable xb (8) must match in size  (in   'modela153dd30a65_feit2' at line 22)

When specifying the model part as usual, things work fine:

model {
 beta ~normal(0,3);  
 for (i in 1:N)
	Y[i] ~ categorical_logit(X[i]*beta);

Thanks for any help

Could it be that you mixed up K and C in your declaration of xb. If so you need

matrix[C,N] xb;