void rtnorm(double *x, double *left, double* right, double *mu, double *sig, int *num)
{
// TODO: NEED TO DEAL WITH Inf/NA/NaN
RNG r;
#ifdef USE_R
GetRNGstate();
#endif
for(int i=0; i < *num; ++i){
#ifdef USE_R
if (i%SAMPCHECK==0) R_CheckUserInterrupt();
if (ISNAN(left[i]) || ISNAN(right[i]) || ISNAN(mu[i]) || ISNAN(sig[i]))
x[i] = R_NaN;
if (ISNA(left[i]) || ISNA(right[i]) || ISNA(mu[i]) || ISNA(sig[i]))
x[i] = NA_REAL;
#endif
#ifdef USE_R
if (left[i] != R_NegInf && right[i] != R_PosInf) {
x[i] = r.tnorm(left[i], right[i], mu[i], sig[i]);
} else if (left[i] != R_NegInf && right[i] == R_PosInf) {
x[i] = r.tnorm(left[i], mu[i], sig[i]);
} else if (left[i] == R_NegInf && right[i] != R_PosInf) {
x[i] = -1.0 * r.tnorm(-1.0 * right[i], -1.0 * mu[i], sig[i]);
} else if (left[i] == R_NegInf && right[i] == R_PosInf) {
x[i] = r.norm(mu[i], sig[i]);
} else {
x[i] = R_NaN;
}
#else
// TODO: Need to adjust so that we deal with +/- inf.
if (MYFINITE(left[i]) && MYFINITE(right[i]))
x[i] = r.tnorm(left[i], right[i], mu[i], sig[i]);
else if (MYFINITE(left[i]))
x[i] = r.tnorm(left[i], mu[i], sig[i]);
else if (MYFINITE(right[i]))
x[i] = -1.0 * r.tnorm(-1.0 * right[i], -1.0 * mu[i], sig[i]);
else
x[i] = r.norm(mu[i], sig[i]);
#endif
}
#ifdef USE_R
PutRNGstate();
#endif
}
inline void Logit::draw_beta(MF beta, MF w, RNG& r)
{
// tXRtOm = tX sqrt(Om)
Matrix tXRtOm(P, N);
for(unsigned int j=0; j<tX.cols(); j++)
for(unsigned int i=0; i<tX.rows(); i++)
tXRtOm(i,j) = tX(i,j) * sqrt(w(j));
// PP = X' Om X + P0.
PP = P0; syrk(PP, tXRtOm, 'N', 1.0, 1.0);
// PP = U'U.
// U'U mP = bP --> U' y = bP; U mP = y;
// ep = U z
// dr = mP + z.
Matrix U; chol(U, PP, 'U');
r.norm(beta, 1.0);
Matrix mP(bP);
trsm(U, mP, 'U', 'L', 'T'); // U' y = bP
trsm(U, mP, 'U', 'L', 'N'); // U mP = y
trsm(U, beta, 'U', 'L', 'N'); // U z = ep.
for(uint i=0; i<P; i++)
beta(i) += mP(i);
}
double PolyaGamma::rtigauss(double Z, RNG& r)
{
Z = fabs(Z);
double t = __TRUNC;
double X = t + 1.0;
if (__TRUNC_RECIP > Z) { // mu > t
double alpha = 0.0;
while (r.unif() > alpha) {
// X = t + 1.0;
// while (X > t)
// X = 1.0 / r.gamma_rate(0.5, 0.5);
// Slightly faster to use truncated normal.
double E1 = r.expon_rate(1.0); double E2 = r.expon_rate(1.0);
while ( E1*E1 > 2 * E2 / t) {
E1 = r.expon_rate(1.0); E2 = r.expon_rate(1.0);
}
X = 1 + E1 * t;
X = t / (X * X);
alpha = exp(-0.5 * Z*Z * X);
}
}
else {
double mu = 1.0 / Z;
while (X > t) {
double Y = r.norm(1.0); Y *= Y;
double half_mu = 0.5 * mu;
double mu_Y = mu * Y;
X = mu + half_mu * mu_Y - half_mu * sqrt(4 * mu_Y + mu_Y * mu_Y);
if (r.unif() > mu / (mu + X))
X = mu*mu / X;
}
}
return X;
}
int main(int argc, char** argv)
{
RNG r;
// MatrixXd A(4,1);
// Matrix<double, 1, 1> mu; mu(0) = 0.0;
// Matrix<double, 1, 1> sig; sig(0) = 1.0;
MatrixXd A(4,1);
VectorXd v(4);
r.norm(A, 1.0);
Map<MatrixXd> M(&A(0), 4, 1);
fun2(M);
fun3(v);
cout << "A:\n" << A << endl;
return 0;
}
