Skip to content

Commit

Permalink
more cleanup
Browse files Browse the repository at this point in the history
  • Loading branch information
@tvatter
tvatter committed Feb 27, 2024
1 parent cfb2608 commit c38cc89
Show file tree
Hide file tree
Showing 4 changed files with 82 additions and 75 deletions.
17 changes: 12 additions & 5 deletions .Rbuildignore
View file
Original file line number Diff line number Diff line change
@@ -1,12 +1,19 @@
^docs$
^CRAN-RELEASE$
^_pkgdown\.yml$
^.*\.Rproj$
^\.Rproj\.user$
^cran-comments\.md$
^.*\.Rprofile$
^README\.Rmd$
^.*\.html$
^\.travis\.yml$
^appveyor\.yml$
^codecov\.yml$
docs
^cran-comments\.md$
^revdep$
^.vscode$
\.o$
\.so$
\.dll$
^src/update_kde1d-cpp\.sh$
^docs$
.github/

^\.github$
136 changes: 68 additions & 68 deletions inst/include/kde1d/kde1d.hpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -152,7 +152,7 @@ inline Kde1d::Kde1d(size_t nlevels,
}
if (nlevels_ > 0 && (!std::isnan(xmin) || !std::isnan(xmax))) {
throw std::invalid_argument(
"xmin and xmax are not meaningful for discrete distributions");
"xmin and xmax are not meaningful for discrete distributions");
}
this->check_xmin_xmax(xmin, xmax);
}
Expand Down Expand Up @@ -221,7 +221,7 @@ Kde1d::fit(const Eigen::VectorXd& x, const Eigen::VectorXd& weights)

// calculate effective degrees of freedom
interp::InterpolationGrid infl_grid(
grid_points, fitted.col(1).cwiseMin(2.0).cwiseMax(0), 0);
grid_points, fitted.col(1).cwiseMin(2.0).cwiseMax(0), 0);
edf_ = infl_grid.interpolate(x).sum();
}

Expand Down Expand Up @@ -372,13 +372,13 @@ Kde1d::check_levels(const Eigen::VectorXd& x) const
return;
if ((xx.array() != xx.array().round()).any() || (xx.minCoeff() < 0)) {
throw std::runtime_error(
"when nlevels > 0, 'x' must only contain non-negatives integers.");
"when nlevels > 0, 'x' must only contain non-negatives integers.");
}
if (xx.maxCoeff() > static_cast<double>(nlevels_ - 1)) {
throw std::runtime_error(
"maximum value of 'x' is" + std::to_string(xx.maxCoeff()) +
", which is larger than " + std::to_string(nlevels_ - 1) +
" (number of factor levels minus 1).");
"maximum value of 'x' is" + std::to_string(xx.maxCoeff()) +
", which is larger than " + std::to_string(nlevels_ - 1) +
" (number of factor levels minus 1).");
// throw std::runtime_error("maximum value of 'x' is larger than the "
// "number of factor levels.");
}
Expand Down Expand Up @@ -413,7 +413,7 @@ Kde1d::fit_lp(const Eigen::VectorXd& x,
{
size_t m = grid_points.size();
fft::KdeFFT kde_fft(
x, bandwidth_, grid_points(0), grid_points(m - 1), weights);
x, bandwidth_, grid_points(0), grid_points(m - 1), weights);
Eigen::VectorXd f0 = kde_fft.kde_drv(0);

Eigen::VectorXd wbin = Eigen::VectorXd::Ones(m);
Expand Down Expand Up @@ -465,43 +465,43 @@ Kde1d::fit_lp(const Eigen::VectorXd& x,
//! calculate influence for data point for density estimate based on
//! quantities pre-computed in `fit_lp()`.
inline double
Kde1d::calculate_infl(const size_t& n,
const double& f0,
const double& b,
const double& bandwidth,
const double& s,
const double& weight)
{
double M_inverse00;
double bandwidth2 = std::pow(bandwidth, 2);
double b2 = std::pow(b, 2);
if (degree_ == 0) {
M_inverse00 = 1 / f0;
} else if (degree_ == 1) {
Eigen::Matrix2d M;
M(0, 0) = f0;
M(0, 1) = bandwidth2 * b * f0;
M(1, 0) = M(0, 1);
M(1, 1) = f0 * bandwidth2 + f0 * bandwidth2 * bandwidth2 * b2;
M_inverse00 = M.inverse()(0, 0);
} else {
Eigen::Matrix3d M;
M(0, 0) = f0;
M(0, 1) = f0 * b;
M(1, 0) = M(0, 1);
M(1, 1) = f0 * bandwidth2 + f0 * b2;
M(1, 2) = 0.5 * f0 * (3.0 / s * b + b * b2);
M(2, 1) = M(1, 2);
M(2, 2) = 0.25 * f0;
M(2, 2) *= 3.0 / std::pow(s, 2) + 6.0 / s * b2 + b2 * b2;
M(0, 2) = M(2, 2);
M(2, 0) = M(2, 2);
M_inverse00 = M.inverse()(0, 0);
}

return K0_ * weight / (static_cast<double>(n) * bandwidth) * M_inverse00;
Kde1d::calculate_infl(const size_t& n,
const double& f0,
const double& b,
const double& bandwidth,
const double& s,
const double& weight)
{
double M_inverse00;
double bandwidth2 = std::pow(bandwidth, 2);
double b2 = std::pow(b, 2);
if (degree_ == 0) {
M_inverse00 = 1 / f0;
} else if (degree_ == 1) {
Eigen::Matrix2d M;
M(0, 0) = f0;
M(0, 1) = bandwidth2 * b * f0;
M(1, 0) = M(0, 1);
M(1, 1) = f0 * bandwidth2 + f0 * bandwidth2 * bandwidth2 * b2;
M_inverse00 = M.inverse()(0, 0);
} else {
Eigen::Matrix3d M;
M(0, 0) = f0;
M(0, 1) = f0 * b;
M(1, 0) = M(0, 1);
M(1, 1) = f0 * bandwidth2 + f0 * b2;
M(1, 2) = 0.5 * f0 * (3.0 / s * b + b * b2);
M(2, 1) = M(1, 2);
M(2, 2) = 0.25 * f0;
M(2, 2) *= 3.0 / std::pow(s, 2) + 6.0 / s * b2 + b2 * b2;
M(0, 2) = M(2, 2);
M(2, 0) = M(2, 2);
M_inverse00 = M.inverse()(0, 0);
}

return K0_ * weight / (static_cast<double>(n) * bandwidth) * M_inverse00;
}

//! transformations for density estimates with bounded support.
//! @param x evaluation points.
//! @param inverse whether the inverse transformation should be applied.
Expand Down Expand Up @@ -611,33 +611,33 @@ Kde1d::finalize_grid(Eigen::VectorXd& grid_points)

// Bandwidth for Kernel Density Estimation
//' @param x vector of observations
//' @param bandwidth bandwidth parameter, NA for automatic selection.
//' @param multiplier bandwidth multiplieriplier.
//' @param discrete whether a jittered estimate is computed.
//' @param weights vector of weights for each observation (can be empty).
//' @param degree polynomial degree.
//' @return the selected bandwidth
//' @noRd
inline double
Kde1d::select_bandwidth(const Eigen::VectorXd& x,
double bandwidth,
double multiplier,
size_t degree,
const Eigen::VectorXd& weights) const
{
if (std::isnan(bandwidth)) {
bandwidth::PluginBandwidthSelector selector(x, weights);
bandwidth = selector.select_bandwidth(degree);
}

bandwidth *= multiplier;
if (nlevels_ > 0) {
bandwidth = std::max(bandwidth, 0.5 / 5);
}
//' @param bandwidth bandwidth parameter, NA for automatic selection.
//' @param multiplier bandwidth multiplieriplier.
//' @param discrete whether a jittered estimate is computed.
//' @param weights vector of weights for each observation (can be empty).
//' @param degree polynomial degree.
//' @return the selected bandwidth
//' @noRd
inline double
Kde1d::select_bandwidth(const Eigen::VectorXd& x,
double bandwidth,
double multiplier,
size_t degree,
const Eigen::VectorXd& weights) const
{
if (std::isnan(bandwidth)) {
bandwidth::PluginBandwidthSelector selector(x, weights);
bandwidth = selector.select_bandwidth(degree);
}

return bandwidth;
bandwidth *= multiplier;
if (nlevels_ > 0) {
bandwidth = std::max(bandwidth, 0.5 / 5);
}

return bandwidth;
}

inline void
Kde1d::check_xmin_xmax(const double& xmin, const double& xmax) const
{
Expand Down Expand Up @@ -665,11 +665,11 @@ Kde1d::check_inputs(const Eigen::VectorXd& x,

if (!std::isnan(xmin_) && (x.minCoeff() < xmin_))
throw std::invalid_argument(
"all values in x must be larger than or equal to xmin");
"all values in x must be larger than or equal to xmin");

if (!std::isnan(xmax_) && (x.maxCoeff() > xmax_))
throw std::invalid_argument(
"all values in x must be smaller than or equal to xmax");
"all values in x must be smaller than or equal to xmax");
}

void
Expand Down
2 changes: 1 addition & 1 deletion inst/include/kde1d/stats.hpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,7 @@ quantile(const Eigen::VectorXd& x,
const Eigen::VectorXd& q,
const Eigen::VectorXd& w)
{
if (w.size() == 0)
if (w.size() == 0 || w.minCoeff() == w.maxCoeff())
return quantile(x, q);
if (w.size() != x.size())
throw std::invalid_argument("x and w must have the same size");
Expand Down
2 changes: 1 addition & 1 deletion src/update_kde1d.sh → src/update_kde1d-cpp.sh
View file
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
#!/bin/bash

git clone --depth 1 [email protected]:vinecopulib/kde1d-cpp.git -b main --single-branch
git clone --depth 1 [email protected]:vinecopulib/kde1d-cpp.git -b more-rkde1d --single-branch

rm -rf ../inst/include/kde1d/*
mv ./kde1d-cpp/include/* ../inst/include
Expand Down

0 comments on commit c38cc89

Please sign in to comment.