BOOST_FORCEINLINE static void conf_bounds(const A0& a0, A1& a1,
const value_type& alpha )
{
nt2::container::table<value_type> pcov = boost::proto::child_c<3>(a0);
const In0& x = boost::proto::child_c<0>(a0);
const In1& mu = boost::proto::child_c<1>(a0);
const In2& sigma = boost::proto::child_c<2>(a0);
BOOST_AUTO_TPL(z, (x-mu)/sigma);
BOOST_AUTO_TPL(zvar, pcov(1,1) + (Two<value_type>()*pcov(1,2) + pcov(2,2)*z)*z);
BOOST_ASSERT_MSG(nt2::globalall(nt2::is_gez(zvar)), "Covariance matrix must be positive");
value_type normz = -nt2::norminv(alpha*nt2::Half<value_type>());
BOOST_AUTO_TPL(halfwidth, normz*nt2::sqrt(zvar)/sigma);
boost::proto::child_c<0>(a1) = Half<value_type>()*nt2::erfc(-Sqrt_2o_2<value_type>()*z);
boost::proto::child_c<1>(a1) = Half<value_type>()*nt2::erfc(-Sqrt_2o_2<value_type>()*(z-halfwidth));
boost::proto::child_c<2>(a1) = Half<value_type>()*nt2::erfc(-Sqrt_2o_2<value_type>()*(z+halfwidth));
}
BOOST_FORCEINLINE static void conf_bounds(const A0& a0, A1& a1,
const value_type& alpha )
{
typedef nt2::memory::container<tag::table_, value_type, nt2::_2D> semantic;
NT2_AS_TERMINAL_IN(semantic, pcov, boost::proto::child_c<3>(a0));
const In0& p = boost::proto::child_c<0>(a0);
const In1& mu = boost::proto::child_c<1>(a0);
const In2& sigma = boost::proto::child_c<2>(a0);
auto logx0 = -Sqrt_2<A0>()*erfcinv( nt2::Two<A0>()*p);
auto xvar = fma(fma(pcov(2,2), logx0, Two<value_type>()*pcov(1,2)), logx0, pcov(1,1));
BOOST_ASSERT_MSG(nt2::globalall(nt2::is_nltz(xvar)), "Covariance matrix must be positive");
value_type normz = -nt2::norminv(alpha*nt2::Half<value_type>());
auto halfwidth = normz*nt2::sqrt(xvar);
boost::proto::child_c<0>(a1) = exp(fma(sigma, logx0, mu));
auto coef = exp(-halfwidth);
boost::proto::child_c<1>(a1) = boost::proto::child_c<0>(a1)*coef;
boost::proto::child_c<2>(a1) = boost::proto::child_c<0>(a1)/coef;
}
BOOST_FORCEINLINE static void conf_bounds(const A0& a0, A1& a1,
const value_type& alpha )
{
typedef nt2::memory::container<tag::table_, value_type, nt2::_2D> semantic;
NT2_AS_TERMINAL_IN(semantic, pcov, boost::proto::child_c<3>(a0));
const In0& x = boost::proto::child_c<0>(a0);
const In1& mu = boost::proto::child_c<1>(a0);
const In2& sigma = boost::proto::child_c<2>(a0);
auto z = (log(if_zero_else(is_lez(x), x))-mu)/sigma;
// this is [1, x0]*pcov*[1; x0]
auto zvar = fma(fma(pcov(2,2), z, Two<value_type>()*pcov(1,2)), z, pcov(1,1));
BOOST_ASSERT_MSG(nt2::globalall(nt2::is_gez(zvar)), "Covariance matrix must be positive");
value_type normz = -nt2::norminv(alpha*nt2::Half<value_type>());
auto halfwidth = normz*nt2::sqrt(zvar)/sigma;
boost::proto::child_c<0>(a1) = Half<value_type>()*nt2::erfc(-Sqrt_2o_2<value_type>()*z);
boost::proto::child_c<1>(a1) = Half<value_type>()*nt2::erfc(-Sqrt_2o_2<value_type>()*(z-halfwidth));
boost::proto::child_c<2>(a1) = Half<value_type>()*nt2::erfc(-Sqrt_2o_2<value_type>()*(z+halfwidth));
}
void lvm_kriging(
const cont_property_array_t & input,
const mean_t * mean_data,
const sugarbox_grid_t & grid,
const ok_params_t & params,
cont_property_array_t & output)
{
print_algo_name("LVM Kriging");
print_params(params);
progress_reporter_t reporter(grid.size());
kriging_stats_t stats;
typedef precalculated_covariances_t covariances_t;
covariances_t pcov(cov_model_t(params), params.m_radiuses);
hpgl::cont_kriging(input, grid, params, mean_data, pcov,
sk_weight_calculator_t(),
output, reporter, stats, mean_on_failure);
write(boost::format("%1%") % stats);
}
