Pystan: My model has parameters but the error says It doesn't!


In a project on linux, I am using pystan, stan_utility 0.1.2, gcc-9 and g++ -9.
when I call the function

fit = model.sampling(data=data_doublesigmoid_toy, seed=8453462, chains=4, iter=1000, n_jobs=1)

It produces this error:

RuntimeError: Must use algorithm="Fixed_param" for model that has no parameters.

But my model does have parameters. So I do not know what is wrong here. It would be great if someone has some hints.

And the stan file is this:

functions {
	real log_double_sigmoid(real value, real low, real high, real coef_div, real coef_si, real coef_se){
	real A;
	real B;
	real C;
	A = 10^(coef_se * (value / coef_div));
	B = (10^(coef_se * (value / coef_div)) + 10^(coef_se * (low / coef_div)));
	C = (10^(coef_si * (value / coef_div)) / (10^(coef_si * (value / coef_div)) + 10^(coef_si * (high / coef_div))));
	if((A / B) - C < 0)
		return 0;
		return log((A / B) - C);
	real aggregated_score_prod(vector weights,  vector x_raw, vector lows, vector deltas, vector coef_div, vector coef_si, vector coef_se, int k) {
		real score;
		score = 0;
		for (l in 1:k) {
			score = score + weights[l]*log_double_sigmoid(x_raw[l],lows[l], lows[l]+deltas[l], coef_div[l], coef_si[l], coef_se[l]);
		score = exp(score);
		return score;

data {
    int<lower=0> n;                     // number of data points
	int<lower=0> k;						// number of scoring components
	vector[k] x_raw[n];    				// raw scores
    int<lower=0,upper=1> y[n];          // binary response variable
	vector<lower=0>[k] weights;			// Non-negative weights of the user-model (fixed, sum 1)
	vector[k] coef_div;					// params of double sigmoid (fixed)
	vector[k] coef_si;					// params of double sigmoid (fixed)
	vector[k] coef_se;					// params of double sigmoid (fixed)
	vector[k] high0;					// initial guesses of the score transform variables
	vector[k] low0;
	int<lower=0> npred;
	vector[k] xpred[npred];    // all molecules (for active learning)

parameters {
	vector[k] lows;
	vector<lower=0>[k] deltas;
model {
	for (i in 1:k) {
		lows[i] ~ normal(low0[i], (high0[i]-low0[i])/8);
		deltas[i] ~ normal(high0[i]-low0[i], (high0[i]-low0[i])/8);
    // observation model
	for (j in 1:n) {
		y[j] ~ bernoulli(aggregated_score_prod(weights, x_raw[j], lows, deltas, coef_div, coef_si, coef_se, k));
generated quantities {
	vector[k] highs;
	vector[npred] score_pred;
	for (i in 1:k)
		highs[i] = lows[i]+deltas[i];
	for (j in 1:npred){
		score_pred[j] = aggregated_score_prod(weights, xpred[j], lows, deltas, coef_div, coef_si, coef_se, k);

Are you sure that model refers to the model you think it does in the code? And if it does, are you sure k is not 0, which would also lead to no parameters.

If you’re sure neither of those is the problem, you might want to try CmdStanPy, which is more up to date with Stan and more actively maintained these days than PyStan.