void extractInternalFaces(const Dune::CpGrid& grid,
Eigen::Array<int, Eigen::Dynamic, 1>& internal_faces,
Eigen::Array<int, Eigen::Dynamic, 2, Eigen::RowMajor>& nbi)
{
// Extracts the internal faces of the grid.
// These are stored in internal_faces.
int nf=numFaces(grid);
int num_internal=0;
for(int f=0; f<nf; ++f)
{
if(grid.faceCell(f, 0)<0 || grid.faceCell(f, 1)<0)
continue;
++num_internal;
}
// std::cout << num_internal << " internal faces." << std::endl;
nbi.resize(num_internal, 2);
internal_faces.resize(num_internal);
int fi = 0;
for (int f = 0; f < nf; ++f) {
if(grid.faceCell(f, 0)>=0 && grid.faceCell(f, 1)>=0) {
internal_faces[fi] = f;
nbi(fi,0) = grid.faceCell(f, 0);
nbi(fi,1) = grid.faceCell(f, 1);
++fi;
}
}
}
void
factor_U(const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>& U,
Eigen::Array<T, Eigen::Dynamic, 1>& CPCs) {
size_t K = U.rows();
size_t position = 0;
size_t pull = K - 1;
if (K == 2) {
CPCs(0) = atanh(U(0, 1));
return;
}
Eigen::Array<T, 1, Eigen::Dynamic> temp = U.row(0).tail(pull);
CPCs.head(pull) = temp;
Eigen::Array<T, Eigen::Dynamic, 1> acc(K);
acc(0) = -0.0;
acc.tail(pull) = 1.0 - temp.square();
for (size_t i = 1; i < (K - 1); i++) {
position += pull;
pull--;
temp = U.row(i).tail(pull);
temp /= sqrt(acc.tail(pull) / acc(i));
CPCs.segment(position, pull) = temp;
acc.tail(pull) *= 1.0 - temp.square();
}
CPCs = 0.5 * ( (1.0 + CPCs) / (1.0 - CPCs) ).log(); // now unbounded
}
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>
read_corr_L(const Eigen::Array<T, Eigen::Dynamic, 1>& CPCs, // on (-1, 1)
size_t K) {
Eigen::Array<T, Eigen::Dynamic, 1> temp;
Eigen::Array<T, Eigen::Dynamic, 1> acc(K-1);
acc.setOnes();
// Cholesky factor of correlation matrix
Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic> L(K, K);
L.setZero();
size_t position = 0;
size_t pull = K - 1;
L(0, 0) = 1.0;
L.col(0).tail(pull) = temp = CPCs.head(pull);
acc.tail(pull) = T(1.0) - temp.square();
for (size_t i = 1; i < (K - 1); i++) {
position += pull;
pull--;
temp = CPCs.segment(position, pull);
L(i, i) = sqrt(acc(i-1));
L.col(i).tail(pull) = temp * acc.tail(pull).sqrt();
acc.tail(pull) *= T(1.0) - temp.square();
}
L(K-1, K-1) = sqrt(acc(K-2));
return L.matrix();
}
void operator() (Image<value_type>& dec_img) {
w = 1.0;
if (w_img.valid()) {
assign_pos_of(dec_img, 0, 3).to(w_img);
w = w_img.value();
if (w <= 0.0) {
for (auto l = Loop (3) (dec_img); l; ++l)
dec_img.value() = 0.0;
return;
}
}
for (auto l = Loop (3) (dec_img); l; ++l)
dec[dec_img.index(3)] = dec_img.value();
dec = dec.cwiseMax(0.0);
br = std::pow((dec.pow(gamma) * coefs).sum() , 1.0 / gamma);
if (br == 0.0)
dec.fill(grey * w);
else
dec = dec * (w / br);
for (auto l = Loop (3) (dec_img); l; ++l)
dec_img.value() = dec[dec_img.index(3)];
}
void extractInternalFaces(const UnstructuredGrid& grid,
Eigen::Array<int, Eigen::Dynamic, 1>& internal_faces,
Eigen::Array<int, Eigen::Dynamic, 2, Eigen::RowMajor>& nbi)
{
typedef Eigen::Array<bool, Eigen::Dynamic, 1> OneColBool;
typedef Eigen::Array<int, Eigen::Dynamic, 2, Eigen::RowMajor> TwoColInt;
typedef Eigen::Array<bool, Eigen::Dynamic, 2, Eigen::RowMajor> TwoColBool;
TwoColInt nb = faceCells(grid);
// std::cout << "nb = \n" << nb << std::endl;
// Extracts the internal faces of the grid.
// These are stored in internal_faces.
TwoColBool nbib = nb >= 0;
OneColBool ifaces = nbib.rowwise().all();
const int num_internal = ifaces.cast<int>().sum();
// std::cout << num_internal << " internal faces." << std::endl;
nbi.resize(num_internal, 2);
internal_faces.resize(num_internal);
int fi = 0;
int nf = numFaces(grid);
for (int f = 0; f < nf; ++f) {
if (ifaces[f]) {
internal_faces[fi] = f;
nbi.row(fi) = nb.row(f);
++fi;
}
}
}
void saveAsBitmap(const Eigen::VectorXd& x, int n, const char* filename)
{
Eigen::Array<unsigned char,Eigen::Dynamic,Eigen::Dynamic> bits = (x*255).cast<unsigned char>();
QImage img(bits.data(), n,n,QImage::Format_Indexed8);
img.setColorCount(256);
for(int i=0;i<256;i++) img.setColor(i,qRgb(i,i,i));
img.save(filename);
}
typename boost::math::tools::promote_args<T_y,T_loc,T_scale,T_shape>::type
lkj_cov_log(const Eigen::Matrix<T_y,Eigen::Dynamic,Eigen::Dynamic>& y,
const Eigen::Matrix<T_loc,Eigen::Dynamic,1>& mu,
const Eigen::Matrix<T_scale,Eigen::Dynamic,1>& sigma,
const T_shape& eta,
const Policy&) {
static const char* function = "stan::prob::lkj_cov_log(%1%)";
using stan::math::check_size_match;
using stan::math::check_finite;
using stan::math::check_positive;
using boost::math::tools::promote_args;
typename promote_args<T_y,T_loc,T_scale,T_shape>::type lp(0.0);
if (!check_size_match(function,
mu.rows(), "Rows of location parameter",
sigma.rows(), "columns of scale parameter",
&lp, Policy()))
return lp;
if (!check_size_match(function,
y.rows(), "Rows of random variable",
y.cols(), "columns of random variable",
&lp, Policy()))
return lp;
if (!check_size_match(function,
y.rows(), "Rows of random variable",
mu.rows(), "rows of location parameter",
&lp, Policy()))
return lp;
if (!check_positive(function, eta, "Shape parameter", &lp, Policy()))
return lp;
if (!check_finite(function, mu, "Location parameter", &lp, Policy()))
return lp;
if (!check_finite(function, sigma, "Scale parameter", &lp, Policy()))
return lp;
// FIXME: build vectorized versions
for (int m = 0; m < y.rows(); ++m)
for (int n = 0; n < y.cols(); ++n)
if (!check_finite(function, y(m,n), "Covariance matrix", &lp, Policy()))
return lp;
const unsigned int K = y.rows();
const Eigen::Array<T_y,Eigen::Dynamic,1> sds
= y.diagonal().array().sqrt();
for (unsigned int k = 0; k < K; k++) {
lp += lognormal_log<propto>(sds(k), mu(k), sigma(k), Policy());
}
if (stan::is_constant<typename stan::scalar_type<T_shape> >::value
&& eta == 1.0) {
// no need to rescale y into a correlation matrix
lp += lkj_corr_log<propto,T_y,T_shape,Policy>(y, eta, Policy());
return lp;
}
Eigen::DiagonalMatrix<T_y,Eigen::Dynamic> D(K);
D.diagonal() = sds.inverse();
lp += lkj_corr_log<propto,T_y,T_shape,Policy>(D * y * D, eta, Policy());
return lp;
}
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>
read_cov_L(const Eigen::Array<T, Eigen::Dynamic, 1>& CPCs,
const Eigen::Array<T, Eigen::Dynamic, 1>& sds,
T& log_prob) {
size_t K = sds.rows();
// adjust due to transformation from correlations to covariances
log_prob += (sds.log().sum() + LOG_2) * K;
return sds.matrix().asDiagonal() * read_corr_L(CPCs, K, log_prob);
}
void SLHA_io::convert_symmetric_fermion_mixings_to_slha(Eigen::Array<double, N, 1>& m,
Eigen::Matrix<std::complex<double>, N, N>& z)
{
for (int i = 0; i < N; i++) {
// check if i'th row contains non-zero imaginary parts
if (!is_zero(z.row(i).imag().cwiseAbs().maxCoeff())) {
z.row(i) *= std::complex<double>(0.0,1.0);
m(i) *= -1;
}
}
}
Eigen::MatrixXd eddy2scheme (const Eigen::MatrixXd& config, const Eigen::Array<int, Eigen::Dynamic, 1>& indices)
{
if (config.cols() != 4)
throw Exception ("Expected 4 columns in EDDY-format phase-encoding config file");
Eigen::MatrixXd result (indices.size(), 4);
for (ssize_t row = 0; row != indices.size(); ++row) {
if (indices[row] > config.rows())
throw Exception ("Malformed EDDY-style phase-encoding information: Index exceeds number of config entries");
result.row(row) = config.row(indices[row]-1);
}
return result;
}
IGL_INLINE void igl::ears(
const Eigen::MatrixBase<DerivedF> & F,
Eigen::PlainObjectBase<Derivedear> & ear,
Eigen::PlainObjectBase<Derivedear_opp> & ear_opp)
{
assert(F.cols() == 3 && "F should contain triangles");
Eigen::Array<bool,Eigen::Dynamic,3> B;
{
Eigen::Array<bool,Eigen::Dynamic,1> I;
on_boundary(F,I,B);
}
find(B.rowwise().count() == 2,ear);
Eigen::Array<bool,Eigen::Dynamic,3> Bear;
slice(B,ear,1,Bear);
Eigen::Array<bool,Eigen::Dynamic,1> M;
mat_min(Bear,2,M,ear_opp);
}
void SLHA_io::convert_symmetric_fermion_mixings_to_hk(Eigen::Array<double, N, 1>& m,
Eigen::Matrix<std::complex<double>, N, N>& z)
{
for (int i = 0; i < N; i++) {
if (m(i) < 0.) {
z.row(i) *= std::complex<double>(0.0,1.0);
m(i) *= -1;
}
}
}
void Streamer::calculateAverageUpdateTime()
{
static boost::chrono::steady_clock::time_point currentTime, lastRecordedTime;
static Eigen::Array<float, 5, 1> recordedTimes = Eigen::Array<float, 5, 1>::Zero();
currentTime = boost::chrono::steady_clock::now();
if (lastRecordedTime.time_since_epoch().count())
{
if (!(recordedTimes > 0).all())
{
boost::chrono::duration<float> elapsedTime = currentTime - lastRecordedTime;
recordedTimes[(recordedTimes > 0).count()] = elapsedTime.count();
}
else
{
averageUpdateTime = recordedTimes.mean() * 50.0f;
recordedTimes.setZero();
}
}
lastRecordedTime = currentTime;
}
int FeatureTransformationEstimator::consensus3D(Eigen::MatrixXd P, Eigen::MatrixXd Q, Eigen::Isometry3d T, double thresh, Eigen::Array<bool, 1, Eigen::Dynamic> &consensusSet)
{
int consensus = 0;
P = T * P.colwise().homogeneous();
Eigen::MatrixXd norms = (P - Q).colwise().norm();
consensusSet = norms.array() < thresh;
consensus = consensusSet.count();
return consensus;
}
void BinaryInputStream::readVector (Eigen::Array<FinalType,Rows,1> &values, size_t n)
{
values.resize(n);
SourceType value;
for (size_t i=0; i<n; i++)
{
stream->read((char *) &value, sizeof(SourceType));
if (swapEndian)
swap(&value);
values[i] = static_cast<FinalType>(value);
}
}
void LQCDA::readVector(AsciiReader& reader, Eigen::Array<double, Eigen::Dynamic, 1>& output)
{
try {
int i;
reader.read(i);
output.resize(i);
for(int _i=0; _i<i; ++_i)
reader.read(output(_i));
}
catch (const IOException& e) {
}
}
typename boost::math::tools::promote_args<T_y,T_loc,T_scale,T_shape>::type
lkj_cov_log(const Eigen::Matrix<T_y,Eigen::Dynamic,Eigen::Dynamic>& y,
const T_loc& mu,
const T_scale& sigma,
const T_shape& eta,
const Policy&) {
static const char* function = "stan::prob::lkj_cov_log(%1%)";
using stan::math::check_finite;
using stan::math::check_positive;
using boost::math::tools::promote_args;
typename promote_args<T_y,T_loc,T_scale,T_shape>::type lp(0.0);
if (!check_positive(function, eta, "Shape parameter", &lp, Policy()))
return lp;
if (!check_finite(function, mu, "Location parameter", &lp, Policy()))
return lp;
if (!check_finite(function, sigma, "Scale parameter",
&lp, Policy()))
return lp;
const unsigned int K = y.rows();
const Eigen::Array<T_y,Eigen::Dynamic,1> sds
= y.diagonal().array().sqrt();
for (unsigned int k = 0; k < K; k++) {
lp += lognormal_log<propto>(sds(k), mu, sigma, Policy());
}
if (stan::is_constant<typename stan::scalar_type<T_shape> >::value
&& eta == 1.0) {
// no need to rescale y into a correlation matrix
lp += lkj_corr_log<propto>(y,eta,Policy());
return lp;
}
Eigen::DiagonalMatrix<T_y,Eigen::Dynamic> D(K);
D.diagonal() = sds.inverse();
lp += lkj_corr_log<propto,T_y,T_shape,Policy>(D * y * D, eta, Policy());
return lp;
}
// project points outside of domain back to boundary
void distmesh::utils::projectPointsToBoundary(
Functional const& distanceFunction, double const initialPointDistance,
Eigen::Ref<Eigen::ArrayXXd> points) {
Eigen::ArrayXd distance = distanceFunction(points);
// check for points outside of boundary
Eigen::Array<bool, Eigen::Dynamic, 1> outside = distance > 0.0;
if (outside.any()) {
// calculate gradient
Eigen::ArrayXXd gradient(points.rows(), points.cols());
Eigen::ArrayXXd deltaX = Eigen::ArrayXXd::Zero(points.rows(), points.cols());
for (int dim = 0; dim < points.cols(); ++dim) {
deltaX.col(dim).fill(constants::deltaX * initialPointDistance);
gradient.col(dim) = (distanceFunction(points + deltaX) - distance) /
(constants::deltaX * initialPointDistance);
deltaX.col(dim).fill(0.0);
}
// project points back to boundary
points -= outside.replicate(1, points.cols()).select(
gradient.colwise() * distance / gradient.square().rowwise().sum(), 0.0);
}
}
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>
read_corr_L(const Eigen::Array<T, Eigen::Dynamic, 1>& CPCs,
size_t K,
T& log_prob) {
Eigen::Matrix<T, Eigen::Dynamic, 1> values(CPCs.rows() - 1);
size_t pos = 0;
// no need to abs() because this Jacobian determinant
// is strictly positive (and triangular)
// see inverse of Jacobian in equation 11 of LKJ paper
for (size_t k = 1; k <= (K - 2); k++)
for (size_t i = k + 1; i <= K; i++) {
values(pos) = (K - k - 1) * log1m(square(CPCs(pos)));
pos++;
}
log_prob += 0.5 * sum(values);
return read_corr_L(CPCs, K);
}
//-----------------------------------------------------------
// Utility functions
//-----------------------------------------------------------
void LQCDA::readArray(AsciiReader& reader, Eigen::Array<double, Eigen::Dynamic, Eigen::Dynamic>& output)
{
try {
std::string line;
reader.readLine(line);
std::istringstream isstr (line);
int i, j;
isstr >> i >> j;
output.resize(i, j);
for(int _i=0; _i<i; ++_i)
for(int _j=0; _j<j; ++_j)
{
reader.read(output(_i,_j));
}
}
catch (const IOException& e) {
}
}
int FeatureTransformationEstimator::consensusReprojection(Eigen::MatrixXd P, Eigen::MatrixXd Q, Eigen::Isometry3d T, double thresh, Eigen::Array<bool, 1, Eigen::Dynamic> &consensusSet)
{
Eigen::MatrixXd Pproj(2, P.cols());
Eigen::MatrixXd Qproj(2, P.cols());
for (int i = 0; i < P.cols(); i++) {
Eigen::Vector3d PT = T * P.col(i).homogeneous();
Pproj(0,i) = pnp.uc + pnp.fu * PT(0) / PT(2);
Pproj(1,i) = pnp.vc + pnp.fv * PT(1) / PT(2);
Qproj(0,i) = pnp.uc + pnp.fu * Q(0,i) / Q(2,i);
Qproj(1,i) = pnp.vc + pnp.fv * Q(1,i) / Q(2,i);
}
int consensus = 0;
Eigen::MatrixXd norms = (Pproj - Qproj).colwise().norm();
consensusSet = norms.array() < thresh;
consensus = consensusSet.count();
return consensus;
}
Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> operator() (
const Eigen::Array<Scalar, Rows, Cols>& a,
const Eigen::Array<Scalar, Rows, Cols>& b) {
return a.matrix() * b.matrix().transpose();
}
bool SavePng(std::string filename, const DepthImage& img)
{
// Open up a file for writing
FILE* output = fopen(filename.c_str(), "wb");
if (output == NULL)
{
printf("Failed to open PNG file for writing (errno = %i)\n", errno);
return false;
}
// Create a png pointer with the callbacks above
png_structp png_ptr = png_create_write_struct(
PNG_LIBPNG_VER_STRING, NULL, on_png_error, on_png_warn);
if (png_ptr == NULL)
{
fprintf(stderr, "Failed to allocate png write_struct\n");
fclose(output);
return false;
}
// Create an info pointer
png_infop info_ptr = png_create_info_struct(png_ptr);
if (info_ptr == NULL)
{
fprintf(stderr, "Failed to create png info_struct");
png_destroy_write_struct(&png_ptr, &info_ptr);
fclose(output);
return false;
}
// Set physical vars
png_set_IHDR(png_ptr, info_ptr, img.cols(), img.rows(), 16,
PNG_COLOR_TYPE_GRAY, PNG_INTERLACE_NONE,
PNG_COMPRESSION_TYPE_BASE, PNG_FILTER_TYPE_BASE);
png_init_io(png_ptr, output);
const float zmax = img.maxCoeff();
const float zmin = (img == -std::numeric_limits<float>::infinity())
.select(DepthImage::Constant(img.rows(), img.cols(), zmax),
img)
.minCoeff();
auto scaled = (zmax == zmin)
? DepthImage((img - zmin) + 65535)
: DepthImage((img - zmin) * 65534 / (zmax - zmin) + 1);
Eigen::Array<uint16_t, Eigen::Dynamic, Eigen::Dynamic>
pixels = scaled.cast<uint16_t>().transpose();
std::vector<uint16_t*> rows;
for (int i=pixels.cols() - 1; i >= 0; --i)
{
rows.push_back(pixels.data() + i * pixels.rows());
}
png_set_rows(png_ptr, info_ptr, reinterpret_cast<png_bytepp>(&rows[0]));
png_write_png(png_ptr, info_ptr, PNG_TRANSFORM_SWAP_ENDIAN, NULL);
fclose(output);
png_destroy_write_struct(&png_ptr, &info_ptr);
return true;
}
mglData mgl(const Eigen::Array<Scalar, Eigen::Dynamic, 1>& d)
{
return mglData(d.size(), d.data());
}
DecWeighter (Eigen::Matrix<value_type, 3, 1> coefs, value_type gamma, Image<value_type>& w_img) :
coefs (coefs),
gamma (gamma),
w_img (w_img),
grey (1.0 / std::pow(coefs.sum(), 1.0 / gamma)) {}