inline std::pair<T, T> sample_pair_from_urn (
rng_t & rng,
const std::vector<T> & urn)
{
DIST_ASSERT(urn.size() >= 2, "urn is too small to sample pair from");
size_t f1 = sample_int(rng, 0, urn.size() - 1);
size_t f2 = sample_int(rng, 0, urn.size() - 2);
if (f2 >= f1) {
f2 += 1;
}
DIST_ASSERT(0 <= f1 and f1 < urn.size(), "bad value: " << f1);
DIST_ASSERT(0 <= f2 and f2 < urn.size(), "bad value: " << f2);
DIST_ASSERT(f1 != f2, "bad pair: " << f1 << ", " << f2);
return std::make_pair(urn[f1], urn[f2]);
}
inline T sample_from_urn (
rng_t & rng,
const std::vector<T> & urn)
{
DIST_ASSERT(urn.size() >= 1, "urn is too small to sample from");
size_t f = sample_int(rng, 0, urn.size() - 1);
DIST_ASSERT(0 <= f and f < urn.size(), "bad value: " << f);
return urn[f];
}
// sample a random permutation
void sample_permutation(int n, vector<int> &out_p)
{
out_p.resize(n);
for (int i = 0; i < n; ++i)
{
out_p[i] = i;
}
// randomly swap with rest
for (int i = 0; i < n; ++i)
{
int j = sample_int(i, n-1);
if (j > i)
{
swap(out_p[i], out_p[j]);
}
}
}
void sim_geno(int n_ind, int n_pos, int n_gen, int n_draws,
int *geno, double *rf, double *rf2,
double error_prob, int *draws,
double initf(int, int *),
double emitf(int, int, double, int *),
double stepf(int, int, double, double, int *))
{
int i, k, j, v, v2;
double s, **beta, *probs;
int **Geno, ***Draws, curstate;
int cross_scheme[2];
/* cross scheme hidden in draws argument; used by hmm_bcsft */
cross_scheme[0] = draws[0];
cross_scheme[1] = draws[1];
draws[0] = 0;
draws[1] = 0;
/* allocate space for beta and
reorganize geno and draws */
/* Geno indexed as Geno[pos][ind] */
/* Draws indexed as Draws[rep][pos][ind] */
reorg_geno(n_ind, n_pos, geno, &Geno);
reorg_draws(n_ind, n_pos, n_draws, draws, &Draws);
allocate_alpha(n_pos, n_gen, &beta);
allocate_double(n_gen, &probs);
/* Read R's random seed */
GetRNGstate();
for(i=0; i<n_ind; i++) { /* i = individual */
R_CheckUserInterrupt(); /* check for ^C */
/* do backward equations */
/* initialize beta */
for(v=0; v<n_gen; v++) beta[v][n_pos-1] = 0.0;
/* backward equations */
for(j=n_pos-2; j>=0; j--) {
for(v=0; v<n_gen; v++) {
beta[v][j] = beta[0][j+1] + stepf(v+1,1,rf[j], rf2[j], cross_scheme) +
emitf(Geno[j+1][i],1,error_prob, cross_scheme);
for(v2=1; v2<n_gen; v2++)
beta[v][j] = addlog(beta[v][j], beta[v2][j+1] +
stepf(v+1,v2+1,rf[j],rf2[j], cross_scheme) +
emitf(Geno[j+1][i],v2+1,error_prob, cross_scheme));
}
}
for(k=0; k<n_draws; k++) { /* k = simulation replicate */
/* first draw */
/* calculate probs */
s = (probs[0] = initf(1, cross_scheme)+emitf(Geno[0][i],1,error_prob, cross_scheme)+beta[0][0]);
for(v=1; v<n_gen; v++) {
probs[v] = initf(v+1, cross_scheme) + emitf(Geno[0][i], v+1, error_prob, cross_scheme) +
beta[v][0];
s = addlog(s, probs[v]);
}
for(v=0; v<n_gen; v++) probs[v] = exp(probs[v] - s);
/* make draw: returns a value from {1, 2, ..., n_gen} */
curstate = Draws[k][0][i] = sample_int(n_gen, probs);
/* move along chromosome */
for(j=1; j<n_pos; j++) {
/* calculate probs */
for(v=0; v<n_gen; v++)
probs[v] = exp(stepf(curstate,v+1,rf[j-1],rf2[j-1], cross_scheme) +
emitf(Geno[j][i],v+1,error_prob, cross_scheme) +
beta[v][j] - beta[curstate-1][j-1]);
/* make draw */
curstate = Draws[k][j][i] = sample_int(n_gen, probs);
}
} /* loop over replicates */
} /* loop over individuals */
/* write R's random seed */
PutRNGstate();
}
