double gen_real() { return real_dist(gen); }
void generate (SNPMatrix& A,
std::vector<double>& y,
const snp_int_t& num_rhs,
SNPSet& true_snps) {
typedef typename SNPSet::value_type snp_type;
/* psuedo-random generator engine for various phases in this function */
boost::random::mt19937 prng;
prng.seed (int_params[RAND_SEED_INDEX]);
/* parameters that we will refer to over and over again */
snp_int_t M = int_params[M_INDEX]; /* not making it const for BLAS */
snp_int_t N = int_params[N_INDEX]; /* not making it const for BLAS */
const double cutoff_for_0 = dbl_params[CUTOFF_FOR_0_INDEX];
const double cutoff_for_1 = dbl_params[CUTOFF_FOR_1_INDEX];
double signal_strength = dbl_params[SIGNAL_STRENGTH_INDEX];
/*************************************************************************/
/* generate epistatic and marginal SNPs */
const snp_int_t num_mar_snps = floor (dbl_params [PERCENT_MAR_INDEX] *
static_cast<double>(int_params [NUM_TRUE_SNPS_INDEX]));
const snp_int_t num_epi_snps =
int_params [NUM_TRUE_SNPS_INDEX]-num_mar_snps;
/* initialize a uniform random number generator in the range [0,N) */
boost::uniform_int<snp_int_t> int_dist(0, (N-1));
for (snp_int_t i=0;i<num_mar_snps;++i)
true_snps.insert(snp_type(int_dist(prng)));
/* generate some epistatic snp pairs */
generate_epistatic_t<snp_type,
boost::uniform_int<snp_int_t>,
boost::random::mt19937> gen_epistatic (int_dist,prng);
for (snp_int_t i=0; i<num_epi_snps; ++i) {
snp_type epi_snp = gen_epistatic();
while (true_snps.end()!=true_snps.find(epi_snp)) epi_snp=gen_epistatic();
true_snps.insert(epi_snp);
}
/*************************************************************************/
/* generate A */
boost::uniform_real<double> real_dist (0.0 /*lb*/, 1.0 /*ub*/);
for (snp_int_t col=0; col<N; ++col) for (snp_int_t row=0; row<M; ++row) {
const double next_rnd = real_dist(prng);
const unsigned char next_snp = ((cutoff_for_0>next_rnd)? 0:
((cutoff_for_0<=next_rnd && cutoff_for_1>next_rnd)) ? 1:2);
A.set (row,col,next_snp);
}
/*************************************************************************/
/* generate y */
boost::normal_distribution<double> nrml_dist (0.0/*mean*/, 1.0/*sigma*/);
std::vector<double> multiplier (M);
int truncated_M=static_cast<int> (M);
/* Initialize y to zero for good measure */
y.resize (M*num_rhs, 0.0);
/* Marginal: Set the right positions of x to be signal strength */
typename SNPSet::iterator first = true_snps.begin ();
while (first != true_snps.end()) {
/* Generate the column based on a linear model */
if (first->marginal()) A.interact (first->first(), multiplier.begin());
else A.interact (first->first(), first->second(), multiplier.begin());
daxpy_ (&truncated_M,
&signal_strength,
&(multiplier[0]),
&ONE_STEP,
&(y[0]),
&ONE_STEP);
++first;
}
/* Copy this "y" into all the other "y"'s */
double* y_src = &(y[0]);
for (snp_int_t i=1; i<num_rhs; ++i) {
double* y_dst = y_src + (i*truncated_M);
dcopy_ (&truncated_M, y_src, &ONE_STEP, y_dst, &ONE_STEP);
}
/* Now, for each "y", add a different gaussian noise */
for (size_t i=0; i<y.size(); ++i) y[i]+=nrml_dist(prng);
}
