TEST(AgradRevErrorHandlingMatrix, checkNonzeroSizeMatrix) {
using stan::agrad::var;
Eigen::Matrix<var,Eigen::Dynamic,Eigen::Dynamic> y;
using stan::math::check_nonzero_size;
var result;
y.resize(3,3);
EXPECT_TRUE(check_nonzero_size("checkNonzeroSize", "y", y));
y.resize(2, 3);
EXPECT_TRUE(check_nonzero_size("checkNonzeroSize", "y", y));
y.resize(0,0);
EXPECT_THROW_MSG(check_nonzero_size("checkNonzeroSize", "y", y),
std::invalid_argument, "y has size 0");
std::vector<var> a;
a.push_back(3.0);
a.push_back(3.0);
a.push_back(3.0);
a.push_back(3.0);
EXPECT_TRUE(stan::math::check_nonzero_size("checkNonzeroSize",
"a", a));
a.resize(2);
EXPECT_TRUE(stan::math::check_nonzero_size("checkNonzeroSize",
"a", a));
a.resize(0);
EXPECT_THROW_MSG(stan::math::check_nonzero_size("checkNonzeroSize", "a", a),
std::invalid_argument,
"a has size 0");
}
TEST(MathErrorHandlingMatrix, checkNonzeroSizeMatrix) {
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> y;
double result;
y.resize(3,3);
EXPECT_TRUE(stan::math::check_nonzero_size("checkNonzeroSize(%1%)",
y, "y", &result));
y.resize(2, 3);
EXPECT_TRUE(stan::math::check_nonzero_size("checkNonzeroSize(%1%)",
y, "y", &result));
y.resize(0,0);
EXPECT_THROW(stan::math::check_nonzero_size("checkNonzeroSize(%1%)",y, "y",
&result),
std::domain_error);
std::vector<double> a;
a.push_back(3.0);
a.push_back(3.0);
a.push_back(3.0);
a.push_back(3.0);
EXPECT_TRUE(stan::math::check_nonzero_size("checkNonzeroSize(%1%)",
a, "a", &result));
a.resize(2);
EXPECT_TRUE(stan::math::check_nonzero_size("checkNonzeroSize(%1%)",
a, "a", &result));
a.resize(0);
EXPECT_THROW(stan::math::check_nonzero_size("checkNonzeroSize(%1%)",a, "a",
&result),
std::domain_error);
}
void
jacobian(const F& f,
const Eigen::Matrix<double, Dynamic, 1>& x,
Eigen::Matrix<double, Dynamic, 1>& fx,
Eigen::Matrix<double, Dynamic, Dynamic>& J) {
using Eigen::Matrix;
using stan::agrad::var;
start_nested();
try {
Matrix<var, Dynamic, 1> x_var(x.size());
for (int k = 0; k < x.size(); ++k)
x_var(k) = x(k);
Matrix<var, Dynamic, 1> fx_var = f(x_var);
fx.resize(fx_var.size());
for (int i = 0; i < fx_var.size(); ++i)
fx(i) = fx_var(i).val();
J.resize(x.size(), fx_var.size());
for (int i = 0; i < fx_var.size(); ++i) {
if (i > 0)
set_zero_all_adjoints();
grad(fx_var(i).vi_);
for (int k = 0; k < x.size(); ++k)
J(k, i) = x_var(k).adj();
}
} catch (const std::exception& e) {
stan::agrad::recover_memory_nested();
throw;
}
stan::agrad::recover_memory_nested();
}
int main() {
gen.seed(static_cast<unsigned int>(std::time(0)));
const int beam_size = 3;
const int vocab_size = 10;
const int start_symbol = 0;
const int end_symbol = 1;
const int max_decoding_length = 30;
const int min_decoding_length = 10;
const int source_length = 10;
const precision penalty = 3;
decoder<precision> d(beam_size,vocab_size,start_symbol,end_symbol,
max_decoding_length,min_decoding_length,penalty,"");
Eigen::Matrix<precision,Eigen::Dynamic, Eigen::Dynamic> outputDist;
Eigen::Matrix<precision, 1, Eigen::Dynamic> normalization;
outputDist.resize(vocab_size,beam_size);
normalization.resize(1,beam_size);
init_output_dist(outputDist,normalization);
d.init_decoder();
for(int i=0; i<20; i++) {
init_output_dist(outputDist,normalization);
d.expand_hypothesis(outputDist);
d.print_current_hypotheses();
}
d.finish_current_hypotheses(outputDist);
d.print_current_hypotheses();
}
void
hessian(const F& f,
const Eigen::Matrix<double, Eigen::Dynamic, 1>& x,
double& fx,
Eigen::Matrix<double, Eigen::Dynamic, 1>& grad,
Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic>& H) {
H.resize(x.size(), x.size());
grad.resize(x.size());
try {
for (int i = 0; i < x.size(); ++i) {
start_nested();
Eigen::Matrix<fvar<var>, Eigen::Dynamic, 1> x_fvar(x.size());
for (int j = 0; j < x.size(); ++j)
x_fvar(j) = fvar<var>(x(j), i == j);
fvar<var> fx_fvar = f(x_fvar);
grad(i) = fx_fvar.d_.val();
if (i == 0) fx = fx_fvar.val_.val();
stan::math::grad(fx_fvar.d_.vi_);
for (int j = 0; j < x.size(); ++j)
H(i, j) = x_fvar(j).val_.adj();
stan::math::recover_memory_nested();
}
} catch (const std::exception& e) {
stan::math::recover_memory_nested();
throw;
}
}
IGL_INLINE bool igl::png::texture_from_png(
const std::string png_file,
Eigen::Matrix<char,Eigen::Dynamic,Eigen::Dynamic>& R,
Eigen::Matrix<char,Eigen::Dynamic,Eigen::Dynamic>& G,
Eigen::Matrix<char,Eigen::Dynamic,Eigen::Dynamic>& B,
Eigen::Matrix<char,Eigen::Dynamic,Eigen::Dynamic>& A
)
{
int width,height,n;
unsigned char *data = stbi_load(png_file.c_str(), &width, &height, &n, 4);
if(data == NULL) {
return false;
}
R.resize(height,width);
G.resize(height,width);
B.resize(height,width);
A.resize(height,width);
for (unsigned j=0; j<height; ++j) {
for (unsigned i=0; i<width; ++i) {
// used to flip with libPNG, but I'm not sure if
// simply j*width + i wouldn't be better
// stb_image uses horizontal scanline an starts top-left corner
R(i,j) = data[4*( (width-1-i) + width * (height-1-j) )];
G(i,j) = data[4*( (width-1-i) + width * (height-1-j) ) + 1];
B(i,j) = data[4*( (width-1-i) + width * (height-1-j) ) + 2];
//A(i,j) = data[4*( (width-1-i) + width * (height-1-j) ) + 3];
}
}
stbi_image_free(data);
return true;
}
IGL_INLINE void igl::mat_min(
const Eigen::Matrix<T,Eigen::Dynamic,Eigen::Dynamic> & X,
const int dim,
Eigen::Matrix<T,Eigen::Dynamic,1> & Y,
Eigen::Matrix<int,Eigen::Dynamic,1> & I)
{
assert(dim==1||dim==2);
// output size
int n = (dim==1?X.cols():X.rows());
// resize output
Y.resize(n);
I.resize(n);
// loop over dimension opposite of dim
for(int j = 0;j<n;j++)
{
typename Eigen::Matrix<T,Eigen::Dynamic,Eigen::Dynamic>::Index PHONY;
typename Eigen::Matrix<T,Eigen::Dynamic,Eigen::Dynamic>::Index i;
T m;
if(dim==1)
{
m = X.col(j).minCoeff(&i,&PHONY);
}else
{
m = X.row(j).minCoeff(&PHONY,&i);
}
Y(j) = m;
I(j) = i;
}
}
Tvector<T> Tmatrix<T>::solve( const Tvector<T>& b, const std::string method ) const
{
assert( ROWS == b.size() ); // Check dimensions are compatible
// Convert Tvector to an Eigen matrix
Eigen::Matrix<T, -1, 1> B;
B.resize(b.size(),1);
for (std::size_t i=0; i<b.size(); ++i)
{
B(i,0) = b.CONTAINER[i];
}
// Solve the system
Eigen::Matrix<T, -1, 1> X;
X.resize(b.size(),1);
assert( method == "LU" || method == "QR" || method == "PartialLU" );
if ( method == "LU" )
{
X = MATRIX.fullPivLu().solve(B);
}
if ( method == "PartialLU" )
{
X = MATRIX.partialPivLu().solve(B);
}
if ( method == "QR" )
{
X = MATRIX.fullPivHouseholderQr().solve(B);
}
// Convert back to a Tvector
Tvector<T> x(COLS,0.0);
for (std::size_t i=0; i<COLS; ++i)
{
x.CONTAINER[i] = X(i,0);
}
return x;
}
int main(){
ifstream myfile;
myfile.open("../ex1data1.txt");
vector<double> vX{};
vector<double> vy{};
double d1, d2;
char c;
if(myfile.is_open()){
while(myfile>>d1>>c>>d2){
vX.push_back(d1);
vy.push_back(d2);
}
}
int m = vy.size();
cout<<"Running Gradient Descent ...\n";
Eigen::Matrix<double,Eigen::Dynamic,2> X;
Eigen::Matrix<double,Eigen::Dynamic,1> y;
X.resize(m,2);
y.resize(m,1);
for(int i = 0; i < m; ++i){
X(i,0) = 1;
X(i,1) = vX[i];
y(i) = vy[i];
}
Eigen::Matrix<double, Eigen::Dynamic, 1> theta = Eigen::MatrixXd::Zero(2,1);
// Some gradient descent settings
int iterations = 1500;
double alpha = 0.01;
// gradientDescent(X, y, theta, alpha, iterations);
gradientDescent(X, y, theta, alpha, iterations);
cout<<"Theta found by gradient descent: "<<endl;
cout<<theta<<endl<<endl;
Eigen::Matrix<double, 1, Eigen::Dynamic> predVec;
predVec.resize(1,theta.rows());
predVec<<1, 3.5;
double predict1 = predVec*theta;
predVec(1) = 7;
double predict2 = predVec*theta;
cout<<"For population = 35,000, we predict a profit of "<<predict1*10000<<endl;
cout<<"For population = 70,000, we predict a profit of "<<predict2*10000<<endl<<endl;
return 0;
}
/**
* Convert from a depth image to 3D camera space coordinates
* @param depth_image A width x height array of uint16_t depth values
* @param vertices A width x height array of Vector3f representing the vertices in the depth image in camera space
* @param normals A width x height array of Vector3f representing the vertices in the depth image in camera space
*/
void Camera::depth_image_to_vertices_and_normals(const uint16_t * depth_image, const uint32_t width, const uint32_t height, Eigen::Matrix<float, 3, Eigen::Dynamic>& vertices, Eigen::Matrix<float, 3, Eigen::Dynamic>& normals ) const {
using namespace Eigen;
// Allocate storage for vertx and norms
vertices.resize( 3, width * height );
normals.resize( 3, width * height );
// Run from bottom right corner so we can create normal sin the same pass
int32_t src_idx = (width * height) - 1;
for( int16_t y=height-1; y>=0; y-- ) {
for( int16_t x=width-1; x >= 0; x-- ) {
// Vertex and normal for this pixel
Vector3f vertex;
Vector3f normal { 0, 0, 0 };
// If this is a valid depth
uint16_t depth = depth_image[src_idx];
if( depth != 0 ) {
// Back project the point into camera 3D space using D(x,y) * Kinv * (x,y,1)T
Vector2f cam_point = pixel_to_image_plane( x, y );
vertex = Vector3f { cam_point.x(), cam_point.y(), 1.0f } * depth;
// Compute normal as v(y,x+1)-v(y,x) cross v(y+1, x)-v(y, x )
if( (y < static_cast<int16_t>(height - 1 )) && ( x < static_cast<int16_t>(width - 1 ) ) ) {
// We have adjacent vertex to right and below that we can extract
// Vector[0] is the element to the right of this one
// Vector[width] is the element below
Vector3f right_neighbour { vertices(0, src_idx + 1), vertices(1, src_idx + 1), vertices(2, src_idx + 1) };
Vector3f below_neighbour { vertices(0, src_idx + width), vertices(1, src_idx + width), vertices(2, src_idx + width) };
// If they are both not BAD
if( ( right_neighbour != BAD_VERTEX) && ( below_neighbour != BAD_VERTEX ) ) {
right_neighbour -= vertex;
below_neighbour -= vertex;
// Compute cross product for normal
normal = right_neighbour.cross( below_neighbour ).normalized();
}
}
} else {
vertex = BAD_VERTEX;
}
// Store
for( int i=0; i<3; i++ ) {
vertices(i, src_idx) = vertex[i];
normals(i, src_idx) = normal[i];
}
src_idx--;
}
}
}
TEST(ErrorHandlingMatrix, checkSquareMatrix) {
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> y;
y.resize(3,3);
EXPECT_TRUE(stan::math::check_square("checkSquareMatrix",
"y", y));
y.resize(3, 2);
EXPECT_THROW(stan::math::check_square("checkSquareMatrix", "y", y),
std::invalid_argument);
}
Tvector<T> Tsparse_matrix<T>::solve( const Tvector<T>& b ) const
{
assert( ROWS == b.size() ); // Check dimensions are compatible
// Convert Tvector to an Eigen matrix
Eigen::Matrix<T, -1, 1> B;
B.resize(b.size(),1);
for (std::size_t i=0; i<b.size(); ++i)
{
B(i,0) = b.CONTAINER[i];
}
// Convert Tsparse_matrix to an Eigen::SparseMatrix
Eigen::SparseMatrix<T> A(ROWS,COLS); // Declare Eigen sparse matrix
std::vector<Eigen::Triplet<T>> triplet_list; // Declare list of triplets
for ( std::size_t i=0; i<ROWS; ++i )
{
if ( !S_MATRIX[i].isempty() ) // Check that the row is not empty
{
std::vector<std::size_t> index;
std::vector<T> element;
index = S_MATRIX[i].index_list();
element = S_MATRIX[i].element_list();
std::size_t J = index.size();
for ( std::size_t j=0; j<J; ++j )
{
triplet_list.push_back( Eigen::Triplet<T>( i, index[j], element[j] ));
}
}
}
A.setFromTriplets(triplet_list.begin(), triplet_list.end());
// Setup and solve the system
Eigen::SparseLU< Eigen::SparseMatrix<T> > solverA;
//Eigen::BiCGSTAB<Eigen::SparseMatrix<T>> solverA;
//solverA.analyzePattern(A);
//solverA.factorize(A);
solverA.compute(A);
Eigen::Matrix<T, -1, 1> X;
X.resize(b.size(),1);
X = solverA.solve(B);
// Convert back to a Tvector
Tvector<T> x(COLS,0.0);
for (std::size_t i=0; i<COLS; ++i)
{
x.CONTAINER[i] = X(i,0);
}
return x;
}
TEST(ErrorHandlingMatrix, checkSquareMatrix_nan) {
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> y;
double nan = std::numeric_limits<double>::quiet_NaN();
y.resize(3,3);
y << nan, nan, nan,nan, nan, nan,nan, nan, nan;
EXPECT_TRUE(stan::math::check_square("checkSquareMatrix",
"y", y));
y.resize(3, 2);
y << nan, nan, nan,nan, nan, nan;
EXPECT_THROW(stan::math::check_square("checkSquareMatrix", "y", y),
std::invalid_argument);
}
TEST(ErrorHandlingMatrix, checkVectorMatrix) {
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> x;
x.resize(3,3);
EXPECT_THROW(stan::math::check_vector("checkVector", "x", x),
std::invalid_argument);
x.resize(0,0);
EXPECT_THROW(stan::math::check_vector("checkVector", "x", x),
std::invalid_argument);
x.resize(1,5);
EXPECT_TRUE(stan::math::check_vector("checkVector", "x", x));
x.resize(5,1);
EXPECT_TRUE(stan::math::check_vector("checkVector", "x", x));
}
static inline void _fast_vector_copy(Eigen::Matrix<T, Eigen::Dynamic, 1>& v_to, const Eigen::Matrix<T, Eigen::Dynamic, 1>& v_from) {
int sz = v_from.size();
v_to.resize(sz);
if (sz > 0) {
std::memcpy(&v_to(0), &v_from(0), v_from.size() * sizeof(double));
}
}
SEXP
vectorToMatVCL(SEXP A, const int nr, const int nc, int device_flag)
{
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> Am;
Am = Rcpp::as<Eigen::Matrix<T, Eigen::Dynamic, 1> >(A);
// Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> temp = as<Eigen::Matrix<T, Eigen::Dynamic, 1> >(A);
Am.resize(nr, nc);
// Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> > Amm(&Am(0), nr, nc);
dynVCLMat<T> *mat = new dynVCLMat<T>(Am, nr, nc, device_flag);
Rcpp::XPtr<dynVCLMat<T> > pMat(mat);
return pMat;
// Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> > Amm(&Am(0), nr, nc);
//
// //use only GPUs:
// if(device_flag == 0){
// long id = 0;
// viennacl::ocl::set_context_device_type(id, viennacl::ocl::gpu_tag());
// }
//
// viennacl::matrix<T> *vcl_A = new viennacl::matrix<T>(nr, nc);
//
// viennacl::copy(Amm, *vcl_A);
//
// Rcpp::XPtr<viennacl::matrix<T> > pMat(vcl_A);
// return pMat;
}
void
hessian_times_vector(const F& f,
const Eigen::Matrix<double, Dynamic, 1>& x,
const Eigen::Matrix<double, Dynamic, 1>& v,
double& fx,
Eigen::Matrix<double, Dynamic, 1>& Hv) {
using stan::math::fvar;
using stan::math::var;
using Eigen::Matrix;
start_nested();
try {
Matrix<var, Dynamic, 1> x_var(x.size());
for (int i = 0; i < x_var.size(); ++i)
x_var(i) = x(i);
var fx_var;
var grad_fx_var_dot_v;
gradient_dot_vector(f, x_var, v, fx_var, grad_fx_var_dot_v);
fx = fx_var.val();
stan::math::grad(grad_fx_var_dot_v.vi_);
Hv.resize(x.size());
for (int i = 0; i < x.size(); ++i)
Hv(i) = x_var(i).adj();
} catch (const std::exception& e) {
stan::math::recover_memory_nested();
throw;
}
stan::math::recover_memory_nested();
}
template <typename PointT, typename Scalar> void
pcl::demeanPointCloud (const pcl::PointCloud<PointT> &cloud_in,
const std::vector<int> &indices,
const Eigen::Matrix<Scalar, 4, 1> ¢roid,
pcl::PointCloud<PointT> &cloud_out)
{
cloud_out.header = cloud_in.header;
cloud_out.is_dense = cloud_in.is_dense;
if (indices.size () == cloud_in.points.size ())
{
cloud_out.width = cloud_in.width;
cloud_out.height = cloud_in.height;
}
else
{
cloud_out.width = static_cast<uint32_t> (indices.size ());
cloud_out.height = 1;
}
cloud_out.resize (indices.size ());
// Subtract the centroid from cloud_in
for (size_t i = 0; i < indices.size (); ++i)
{
cloud_out[i].x = cloud_in[indices[i]].x - centroid[0];
cloud_out[i].y = cloud_in[indices[i]].y - centroid[1];
cloud_out[i].z = cloud_in[indices[i]].z - centroid[2];
}
}
TEST(ErrorHandlingMatrix, checkSquareMatrix_0x0) {
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> y;
y.resize(0,0);
EXPECT_TRUE(stan::math::check_square("checkSquareMatrix",
"y", y));
}
static bool
read_header(std::istream& in,
Eigen::Matrix<std::string, Eigen::Dynamic, 1>& header) {
std::string line;
if (in.peek() != 'l')
return false;
std::getline(in, line);
std::stringstream ss(line);
header.resize(std::count(line.begin(), line.end(), ',') + 1);
int idx = 0;
while (ss.good()) {
std::string token;
std::getline(ss, token, ',');
boost::trim(token);
int pos = token.find('.');
if (pos > 0) {
token.replace(pos, 1, "[");
std::replace(token.begin(), token.end(), '.', ',');
token += "]";
}
header(idx++) = token;
}
return true;
}
template <typename PointT, typename Scalar> void
pcl::demeanPointCloud (ConstCloudIterator<PointT> &cloud_iterator,
const Eigen::Matrix<Scalar, 4, 1> ¢roid,
pcl::PointCloud<PointT> &cloud_out,
int npts)
{
// Calculate the number of points if not given
if (npts == 0)
{
while (cloud_iterator.isValid ())
{
++npts;
++cloud_iterator;
}
cloud_iterator.reset ();
}
int i = 0;
cloud_out.resize (npts);
// Subtract the centroid from cloud_in
while (cloud_iterator.isValid ())
{
cloud_out[i].x = cloud_iterator->x - centroid[0];
cloud_out[i].y = cloud_iterator->y - centroid[1];
cloud_out[i].z = cloud_iterator->z - centroid[2];
++i;
++cloud_iterator;
}
cloud_out.width = cloud_out.size ();
cloud_out.height = 1;
}
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
{
dst.resize(src.rows, src.cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else if( src.cols == src.rows )
{
src.convertTo(_dst, _dst.type());
transpose(_dst, _dst);
}
else
Mat(src.t()).convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
else
{
Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
}
template<typename _Tp> static inline
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
{
dst.resize(src.rows, src.cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
const Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else if( src.cols == src.rows )
{
src.convertTo(_dst, _dst.type());
transpose(_dst, _dst);
}
else
Mat(src.t()).convertTo(_dst, _dst.type());
}
else
{
const Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
}
}
IGL_INLINE void igl::slice_into(
const Eigen::PlainObjectBase<DerivedX> & X,
const Eigen::Matrix<int,Eigen::Dynamic,1> & R,
const Eigen::Matrix<int,Eigen::Dynamic,1> & C,
Eigen::PlainObjectBase<DerivedX> & Y)
{
int xm = X.rows();
int xn = X.cols();
assert(R.size() == xm);
assert(C.size() == xn);
#ifndef NDEBUG
int ym = Y.size();
int yn = Y.size();
assert(R.minCoeff() >= 0);
assert(R.maxCoeff() < ym);
assert(C.minCoeff() >= 0);
assert(C.maxCoeff() < yn);
#endif
// Build reindexing maps for columns and rows, -1 means not in map
Eigen::Matrix<int,Eigen::Dynamic,1> RI;
RI.resize(xm);
for(int i = 0;i<xm;i++)
{
for(int j = 0;j<xn;j++)
{
Y(R(i),C(j)) = X(i,j);
}
}
}
TEST(MathErrorHandlingMatrix, checkVectorMatrix) {
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> x;
double result;
x.resize(3,3);
EXPECT_THROW(stan::math::check_vector("checkVector(%1%)",x,"x", &result),
std::domain_error);
x.resize(0,0);
EXPECT_THROW(stan::math::check_vector("checkVector(%1%)",x,"x", &result),
std::domain_error);
x.resize(1,5);
EXPECT_TRUE(stan::math::check_vector("checkVector(%1%)",x,"x", &result));
x.resize(5,1);
EXPECT_TRUE(stan::math::check_vector("checkVector(%1%)",x,"x", &result));
}
IGL_INLINE void igl::colon(
const L low,
const S step,
const H hi,
Eigen::Matrix<T,Eigen::Dynamic,1> & I)
{
if(low < hi)
{
if(step < 0)
{
I.resize(0);
//fprintf(stderr,"WARNING: colon() low(%g)<hi(%g) but step(%g)<0\n",
// (double)low,
// (double)hi,
// (double)step);
//assert(false && "low<hi but step<0");
return;
}
}
if(low > hi)
{
if(step > 0)
{
I.resize(0);
//fprintf(stderr,"Error: colon() low(%g)>hi(%g) but step(%g)>0\n",
// (double)low,
// (double)hi,
// (double)step);
//assert(false && "low>hi but step<0");
return;
}
}
// resize output
int n = floor(double((hi-low)/step))+1;
I.resize(n);
int i = 0;
T v = (T)low;
while((low<hi && (H)v<=hi) || (low>hi && (H)v>=hi))
{
I(i) = v;
v = v + (T)step;
i++;
}
assert(i==n);
}
static inline void _fast_matrix_copy(Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>& v_to,
const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>& v_from) {
int nr = v_from.rows();
int nc = v_from.cols();
v_to.resize(nr, nc);
if (nr > 0 && nc > 0) {
std::memcpy(&v_to(0), &v_from(0), v_from.size() * sizeof(double));
}
}
TEST(ErrorHandlingMatrix, checkSymmetric_nan) {
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> y;
double nan = std::numeric_limits<double>::quiet_NaN();
y.resize(2,2);
y << 1, nan, 3, 1;
EXPECT_THROW(stan::math::check_symmetric("checkSymmetric",
"y", y),
std::domain_error);
y << nan, 3, 3, 1;
EXPECT_TRUE(stan::math::check_symmetric("checkSymmetric",
"y", y));
y.resize(1,1);
y << nan;
EXPECT_TRUE(stan::math::check_symmetric("checkSymmetric",
"y", y));
}
inline void assign(Eigen::Matrix<T, R, C>& x,
const nil_index_list& /* idxs */,
const Eigen::Matrix<U, R, C>& y,
const char* name = "ANON",
int depth = 0) {
x.resize(y.rows(), y.cols());
for (size_t i = 0; i < y.size(); ++i)
assign(x(i), nil_index_list(), y(i), name, depth + 1);
}
TEST(ErrorHandlingMatrix, checkSymmetric_non_square) {
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> y;
y.resize(2,3);
EXPECT_THROW(stan::math::check_symmetric("checkSymmetric",
"y", y),
std::invalid_argument);
}
