// [[Rcpp::export]]
mat randWalkTrain(const mat& X, const mat& A, const colvec& Z,
const colvec& theta, int trainit,
const colvec& sigma, const colvec& etaRange,
const mat& V, bool verbose)
{
double logAccept;
int p = theta.size() - 1;
int n = X.n_rows;
colvec eigval;
mat eigvec;
eig_sym(eigval, eigvec, V);
mat R = eigvec * diagmat(sqrt(eigval));
colvec Y = rautologistic_(X, A, theta);
colvec Ynew(n);
colvec thetaold = theta;
colvec thetanew = theta;
colvec normvec(p + 1);
mat estimates(trainit + 1, p + 1);
estimates.row(0) = theta.t();
colvec beta = theta.subvec(0, p - 1);
double eta = theta[p];
colvec Xbeta = X * beta;
colvec mu = exp(Xbeta);
mu = mu / (1 + mu);
double Zsum = as_scalar(Z.t() * A * Z);
int onepct = 0.01 * trainit;
int pct = 1;
RNGScope scope;
Function rnorm("rnorm"),
runif("runif");
for (int i = 1; i < trainit + 1; i++)
{
do
{
normvec = as<colvec>(rnorm(p + 1));
thetanew = thetaold + R * normvec;
}
while(thetanew[p] < etaRange[0] || thetanew[p] > etaRange[1]);
Ynew = rautologistic_(X, A, thetanew);
colvec betaold = thetaold.subvec(0, p - 1);
colvec betanew = thetanew.subvec(0, p - 1);
double etaold = thetaold[p];
double etanew = thetanew[p];
colvec Xbetaold = X * betaold;
colvec Xbetanew = X * betanew;
colvec muold = exp(Xbetaold);
muold = muold / (1 + muold);
colvec munew = exp(Xbetanew );
munew = munew / (1 + munew);
double Ynewsum = as_scalar(Ynew.t() * A * Ynew);
double Ysum = as_scalar(Y.t() * A * Y);
logAccept = as_scalar(0.5 * (eta * (Ynewsum - Ysum) + etanew * (Zsum - Ynewsum) + etaold * (Ysum - Zsum))
+ trans(Ynew - Y) * Xbeta + trans(Z - Ynew) * Xbetanew + trans(Y - Z) * Xbetaold
+ etaold * trans(Z - Y) * A * muold + etanew * trans(Ynew - Z) * A * munew + eta * trans(Y - Ynew) * A * mu)
+ accu((square(betaold) - square(betanew)) / (2 * sigma));
if (as_scalar(log(as<double>(runif(1)))) < logAccept)
{
estimates.row(i) = thetanew.t();
thetaold = thetanew;
Y = Ynew;
}
else
estimates.row(i) = thetaold.t();
if (verbose && i % onepct == 0)
{
Rcout << "\rTraining progress: |";
for (int j = 1; j <= pct; j++)
Rcout << "+";
for (int j = 1; j < (100 - pct); j++)
Rcout << " ";
Rcout << "| " << pct << "%";
pct++;
R_FlushConsole();
}
}
if (verbose)
Rcout << std::endl << std::endl;
return estimates.rows(1, trainit);
}
#include <armadillo>
#include "catch.hpp"
using namespace arma;
TEST_CASE("fn_cumsum_1")
{
colvec a = linspace<colvec>(1,5,6);
rowvec b = linspace<rowvec>(1,5,6);
colvec c = { 1.0000, 2.8000, 5.4000, 8.8000, 13.0000, 18.0000 };
REQUIRE( accu(abs(cumsum(a) - c )) == Approx(0.0) );
REQUIRE( accu(abs(cumsum(b) - c.t())) == Approx(0.0) );
REQUIRE_THROWS( b = cumsum(a) );
}
TEST_CASE("fn_cumsum_2")
{
mat A =
{
{ -0.78838, 0.69298, 0.41084, 0.90142 },
{ 0.49345, -0.12020, 0.78987, 0.53124 },
{ 0.73573, 0.52104, -0.22263, 0.40163 }
};
// How is that for a descriptive function name???
colvec symmetricTridiagonalFrancisAlgorithmWithWilkinsonShift(colvec a, colvec b) {
float epsilon = 0.001f; // turned off currently
int passes = 3;
int pass = 0;
unsigned long long int count = 0;
int m = a.n_elem - 1;
while (0<m) {
++pass;
float s, c;
float d = (a(m-1) - a(m)) * 0.5f;
if (d == 0) {
s = a(m) - abs(b(m));
} else {
s = a(m) - pow(b(m),2) / (d + sgn(d)*sqrt(d*d + b(m)*b(m)));
}
float x = a(0) - s;
float y = b(1);
for (int k=0; k<=m-1; ++k) {
if (1 < m) {
// givens rotation
float r = hypot(y,x);
c = x/r;
s = y/r;
} else {
// Orthogonal diagonalization of a symmetric 2x2 matrix
// http://scipp.ucsc.edu/~haber/ph116A/diag2x2_11.pdf
float theta;
theta = 0.5f * atan2(2*b(1),a(0)-a(1));
s = sin(theta);
c = cos(theta);
}
float w = c*x - s*y;
d = a(k) - a(k+1);
float z = (2*c*b(k+1) + d*s)*s;
a(k) -= z;
a(k+1) += z;
b(k+1) = d*c*s + (c*c - s*s)*b(k);
x = b(k+1);
if (0 < k) {
b(k) = w;
}
if (k < m-1) {
y = -s*b(k+2);
b(k+2) *= c;
if (abs(c) > 1) {
cout << c << endl;
}
}
// Do not need to calculate Q. Do not need eigenvectors.
}
count += m;
// cout << abs(b(m)) << " " << epsilon*(abs(a(m-1)) + abs(a(m))) << endl;
float smallThing = epsilon*(abs(a(m-1)) + abs(a(m)));
if (isnan(smallThing)) {
cout << "Not a number!" << endl;
}
if (abs(b(m)) < smallThing) { // check for convergence
// --m;
// temporarily turned off, to make iterations until m is decreased constant
}
if (pass == passes) {
--m;
pass = 0;
}
}
// cout << "diagonal" << endl << a.t() << endl;
cout << "off diagonal" << endl << b.t() << endl;
cout << "inner loop iterations : " << count << endl; // total number of inner loop iterations performed
return a;
}
