void Bs_integrator::extrapol( Cluster &cl_exp, vector<mpreal> &h, vector<Cluster> &cl )
{
int M = h.size();
int N = cl[0].get_N();
vector< Star* > st(M);
for(int i=0; i<M; i++) st[i] = cl[i].get_pointer_to_star();
Star* st_exp = cl_exp.get_pointer_to_star();
for(int i=0; i<N; i++)
{
vector<mpreal> x_sample(M), y_sample(M), z_sample(M), vx_sample(M), vy_sample(M), vz_sample(M);
for(int j=0; j<M; j++)
{
x_sample[j] = st[j]->x;
y_sample[j] = st[j]->y;
z_sample[j] = st[j]->z;
vx_sample[j] = st[j]->vx;
vy_sample[j] = st[j]->vy;
vz_sample[j] = st[j]->vz;
}
st_exp->x = extrapolate(h, x_sample, "0.0");
st_exp->y = extrapolate(h, y_sample, "0.0");
st_exp->z = extrapolate(h, z_sample, "0.0");
st_exp->vx = extrapolate(h, vx_sample, "0.0");
st_exp->vy = extrapolate(h, vy_sample, "0.0");
st_exp->vz = extrapolate(h, vz_sample, "0.0");
for(int j=0; j<M; j++) st[j]++;
st_exp++;
}
}
sample transition(sample& init_sample)
{
// Initialize the algorithm
this->sample_stepsize();
nuts_util util;
this->seed(init_sample.cont_params());
this->_hamiltonian.sample_p(this->_z, this->_rand_int);
this->_hamiltonian.init(this->_z);
ps_point z_plus(this->_z);
ps_point z_minus(z_plus);
ps_point z_sample(z_plus);
ps_point z_propose(z_plus);
int n_cont = init_sample.cont_params().size();
Eigen::VectorXd rho_init = this->_z.p;
Eigen::VectorXd rho_plus(n_cont); rho_plus.setZero();
Eigen::VectorXd rho_minus(n_cont); rho_minus.setZero();
util.H0 = this->_hamiltonian.H(this->_z);
// Sample the slice variable
util.log_u = std::log(this->_rand_uniform());
// Build a balanced binary tree until the NUTS criterion fails
util.criterion = true;
int n_valid = 0;
this->_depth = 0;
this->_n_divergent = 0;
util.n_tree = 0;
util.sum_prob = 0;
while (util.criterion && (this->_depth <= this->_max_depth) ) {
// Randomly sample a direction in time
ps_point* z = 0;
Eigen::VectorXd* rho = 0;
if (this->_rand_uniform() > 0.5)
{
z = &z_plus;
rho = &rho_plus;
util.sign = 1;
}
else
{
z = &z_minus;
rho = &rho_minus;
util.sign = -1;
}
// And build a new subtree in that direction
this->_z.ps_point::operator=(*z);
int n_valid_subtree = build_tree(_depth, *rho, 0, z_propose, util);
*z = this->_z;
// Metropolis-Hastings sample the fresh subtree
if (!util.criterion)
break;
double subtree_prob = 0;
if (n_valid) {
subtree_prob = static_cast<double>(n_valid_subtree) /
static_cast<double>(n_valid);
} else {
subtree_prob = n_valid_subtree ? 1 : 0;
}
if (this->_rand_uniform() < subtree_prob)
z_sample = z_propose;
n_valid += n_valid_subtree;
// Check validity of completed tree
this->_z.ps_point::operator=(z_plus);
Eigen::VectorXd delta_rho = rho_minus + rho_init + rho_plus;
util.criterion = compute_criterion(z_minus, this->_z, delta_rho);
++(this->_depth);
}
--(this->_depth); // Correct for increment at end of loop
double accept_prob = util.sum_prob / static_cast<double>(util.n_tree);
this->_z.ps_point::operator=(z_sample);
return sample(this->_z.q, - this->_z.V, accept_prob);
}
sample
transition(sample& init_sample,
interface_callbacks::writer::base_writer& info_writer,
interface_callbacks::writer::base_writer& error_writer) {
// Initialize the algorithm
this->sample_stepsize();
nuts_util util;
this->seed(init_sample.cont_params());
this->hamiltonian_.sample_p(this->z_, this->rand_int_);
this->hamiltonian_.init(this->z_, info_writer, error_writer);
ps_point z_plus(this->z_);
ps_point z_minus(z_plus);
ps_point z_sample(z_plus);
ps_point z_propose(z_plus);
int n_cont = init_sample.cont_params().size();
Eigen::VectorXd rho_init = this->z_.p;
Eigen::VectorXd rho_plus(n_cont); rho_plus.setZero();
Eigen::VectorXd rho_minus(n_cont); rho_minus.setZero();
util.H0 = this->hamiltonian_.H(this->z_);
// Sample the slice variable
util.log_u = std::log(this->rand_uniform_());
// Build a balanced binary tree until the NUTS criterion fails
util.criterion = true;
int n_valid = 0;
this->depth_ = 0;
this->divergent_ = 0;
util.n_tree = 0;
util.sum_prob = 0;
while (util.criterion && (this->depth_ <= this->max_depth_)) {
// Randomly sample a direction in time
ps_point* z = 0;
Eigen::VectorXd* rho = 0;
if (this->rand_uniform_() > 0.5) {
z = &z_plus;
rho = &rho_plus;
util.sign = 1;
} else {
z = &z_minus;
rho = &rho_minus;
util.sign = -1;
}
// And build a new subtree in that direction
this->z_.ps_point::operator=(*z);
int n_valid_subtree = build_tree(depth_, *rho, 0, z_propose, util,
info_writer, error_writer);
++(this->depth_);
*z = this->z_;
// Metropolis-Hastings sample the fresh subtree
if (!util.criterion)
break;
double subtree_prob = 0;
if (n_valid) {
subtree_prob = static_cast<double>(n_valid_subtree) /
static_cast<double>(n_valid);
} else {
subtree_prob = n_valid_subtree ? 1 : 0;
}
if (this->rand_uniform_() < subtree_prob)
z_sample = z_propose;
n_valid += n_valid_subtree;
// Check validity of completed tree
this->z_.ps_point::operator=(z_plus);
Eigen::VectorXd delta_rho = rho_minus + rho_init + rho_plus;
util.criterion = compute_criterion(z_minus, this->z_, delta_rho);
}
this->n_leapfrog_ = util.n_tree;
double accept_prob = util.sum_prob / static_cast<double>(util.n_tree);
this->z_.ps_point::operator=(z_sample);
this->energy_ = this->hamiltonian_.H(this->z_);
return sample(this->z_.q, - this->z_.V, accept_prob);
}
