/// \brief Get the latest Monte Carlo integration record of a given
/// variable
///
/// \details
/// For a `dim` dimension Monitor, `id` shall be 0 to `dim` - 1
double record(std::size_t id) const
{
std::size_t iter = iter_size() ? iter_size() - 1 : iter_size();
VSMC_RUNTIME_ASSERT_CORE_MONITOR_ID(record);
VSMC_RUNTIME_ASSERT_CORE_MONITOR_ITER(record);
return record_[iter * dim_ + id];
}
void push_back (std::size_t iter, double grid, double integrand)
{
index_.push_back(iter);
grid_.push_back(grid);
integrand_.push_back(integrand);
if (iter_size() > 1) {
std::size_t i = iter_size() - 1;
log_zconst_ += 0.5 * (grid_[i] - grid_[i - 1]) *
(integrand_[i] + integrand_[i - 1]);
}
}
void read_record(std::size_t id, OutputIter first) const
{
const std::size_t N = iter_size();
const double *riter = record_.data() + id;
for (std::size_t i = 0; i != N; ++i, ++first, riter += dim_)
*first = *riter;
}
/** kmeans_reduce()
* Updates the sum calculation for the various points
*/
void kmeans_reduce(void *key_in, iterator_t *itr)
{
assert (key_in);
assert (itr);
int i;
int *sum;
int *mean;
void *val;
int vals_len = iter_size (itr);
sum = (int *)calloc(dim, sizeof(int));
mean = (int *)malloc(dim * sizeof(int));
i = 0;
while (iter_next (itr, &val))
{
add_to_sum (sum, val);
++i;
}
assert (i == vals_len);
for (i = 0; i < dim; i++)
{
mean[i] = sum[i] / vals_len;
}
free(sum);
emit(key_in, (void *)mean);
}
void read_record_matrix(OutputIter first) const
{
const std::size_t N = iter_size();
if (Layout == ColMajor) {
for (std::size_t d = 0; d != dim_; ++d) {
const double *riter = record_.data() + d;
for (std::size_t i = 0; i != N; ++i, ++first, riter += dim_)
*first = *riter;
}
}
if (Layout == RowMajor)
std::copy(record_.begin(), record_.end(), first);
}
