void MarkovExpectation::compute(FitContext *fc, const char *what, const char *how)
{
if (fc) {
for (auto c1 : components) {
c1->compute(fc, what, how);
}
}
omxRecompute(initial, fc);
if (initialV != omxGetMatrixVersion(initial)) {
omxCopyMatrix(scaledInitial, initial);
EigenVectorAdaptor Ei(scaledInitial);
if (scale == SCALE_SOFTMAX) Ei.derived() = Ei.array().exp();
if (scale != SCALE_NONE) {
Ei /= Ei.sum();
}
if (verbose >= 2) mxPrintMat("initial", Ei);
initialV = omxGetMatrixVersion(initial);
}
if (transition) {
omxRecompute(transition, fc);
if (transitionV != omxGetMatrixVersion(transition)) {
omxCopyMatrix(scaledTransition, transition);
EigenArrayAdaptor Et(scaledTransition);
if (scale == SCALE_SOFTMAX) Et.derived() = Et.array().exp();
if (scale != SCALE_NONE) {
Eigen::ArrayXd v = Et.colwise().sum();
Et.rowwise() /= v.transpose();
}
if (verbose >= 2) mxPrintMat("transition", Et);
transitionV = omxGetMatrixVersion(transition);
}
}
}
void channelns() {
double nx = 41;
double ny = 41;
double nt = 10;
double nit = 50;
double c = 1;
double dx = 2/(nx-1);
double dy = 2/(ny-1);
double rho = 1;
double nu =.1;
double F = 1;
double dt = .01;
std::string dir = "step11/";
Eigen::ArrayXd x; // Array containing our x values
x.setLinSpaced(nx,0,2); // Array is linearly spaced values from 0 to 2
Eigen::ArrayXd y; // Array containing our x values
y.setLinSpaced(ny,0,2);
Eigen::ArrayXXd X;
Eigen::ArrayXXd Y;
X = x.transpose().replicate(nx,1); // Need to transpose this because the type we declared, ArrayXd, is row-major (and we need column-major for a proper meshgrid)
Y = y.replicate(1,ny);
Eigen::ArrayXXd u;
u.setZero(ny,nx);
Eigen::ArrayXXd un;
un.setZero(ny,nx);
Eigen::ArrayXXd v;
v.setZero(ny,nx);
Eigen::ArrayXXd vn;
vn.setZero(ny,nx);
Eigen::ArrayXXd b;
b.setZero(ny,nx);
Eigen::ArrayXXd p;
p.setZero(ny,nx);
Eigen::ArrayXXd pn;
pn.setZero(ny,nx);
}
