/**
* Adds the diagonal of the Hessia of the expected log-likelihoood of
* the specified data point to the Hessian diagonal h.
*/
void add_hessian_diag(const table<T>& phead, const uint_vector& tail, T w,
table<T>& h) const {
assert(phead.arity() + tail.size() == h.arity());
std::size_t index = h.offset().linear(tail, phead.arity());
for (std::size_t i = 0; i < phead.size(); ++i) {
h[index + i] -= phead[i] * w / (f[index + i] * f[index + i]);
}
}
/**
* Adds the gradient of the expected log-likelihood of the specified
* data point to the gradient table g.
*
* \param phead the distribution over a leading set of indices of f
* \param tail a fixed assignment to the remaining indices of f
* \param w the weight of the data point
*/
void add_gradient(const table<T>& phead, const uint_vector& tail, T w,
table<T>& g) const {
assert(phead.arity() + tail.size() == g.arity());
std::size_t index = g.offset().linear(tail, phead.arity());
for (std::size_t i = 0; i < phead.size(); ++i) {
g[index + i] += phead[i] * w;
}
}
/**
* Restricts the given table to an assignment to all the dimensions
* >= n, where n is the arity of this table. The parameter x_start is
* the linear index of the first element in the input table that should
* be copied. This table must be preallocated, and its first n
* dimensions must match the first n dimensions of the input table.
*/
void restrict(const table& x, std::size_t x_start) {
assert(arity() <= x.arity());
assert(std::equal(shape_.begin(), shape_.end(), x.shape_.begin()));
assert(x_start + size() <= x.size());
std::copy(x.data() + x_start, x.data() + x_start + size(), data());
}
