sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
be7bfa6109326a0a8ab0bfef78be99ef962e67ec0bc1fa207eaf320af8414727 | C++ | 107 | 4 | #ifdef ENABLE_GPU
#error "The file nnpooling.cu should be compiled instead"
#endif
#include "nnpooling.cu"
|
2f6a2be26d77100c97fe8c7c128d1d69bde9489a355ca566014ac3843a802427 | C++ | 109 | 5 | Array3d v(1,2,3);
v(1) *= 0.0/0.0;
v(2) /= 0.0;
cout << v << endl << endl;
cout << isfinite(v) << endl;
|
5896ae8eae6de123aaae42dc123e641a6be4e8b553fec4bfcbbd203f1fc34845 | C++ | 109 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/normal.hpp>
int main()
{
int Error(0);
return Error;
}
|
945198fc90622f6ea389f746b21ccc5cd870b8a6e0913b281982ed31e4d62c93 | C++ | 109 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "datamex.cu"
|
e8959dc2cc58ed12ddb1a2c447c8d5958ca0c0f6d5d56bf8c45265d7fc7d92e2 | C++ | 109 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/extend.hpp>
int main()
{
int Error(0);
return Error;
}
|
a43a2625f2cec83178e974f5b09802ee95c0acd9ad996c9576d8b21af9015597 | C++ | 110 | 5 | Array3d v(1,2,3);
v(1) *= 0.0/0.0;
v(2) /= 0.0;
cout << v << endl << endl;
cout << !isfinite(v) << endl;
|
ed06938ec800b98bcce5107e82a0313cda8cbc457db1ce7beca607eb67106355 | C++ | 110 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_tmove.cu"
|
520c0d8af16148d9cf36445c5d373083611a62f783582596b8fe89799c6bdace | C++ | 111 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_nnpool.cu"
|
abf5af8e3d90c0c0ec59fa4cd15cc7ce6f67a0d7f220d44e305f622a79e8b4a1 | C++ | 111 | 4 | #ifdef ENABLE_GPU
#error "The file nnnormalize.cu should be compiled instead"
#endif
#include "nnnormalize.cu"
|
e3f2c75a14907d75ffb65987ba1a93cc7dfc6004b07ffdc0cc16d2a2ee712a9f | C++ | 111 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_nnconv.cu"
|
3ce8ea304b3ff2dc4b1eae180e92d41257db65cfbef72bc572fa85e87211653e | C++ | 112 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_nnbnorm.cu"
|
4ead7041a81c6a5299aa557e92b132a3f33fffbc9f51fab0f6cb3b3caf1ed695 | C++ | 112 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_nnconvt.cu"
|
71bd4f514315009ddca49536b88e431d762f6dd0316c03e7749d9d33db0388c4 | C++ | 112 | 5 | #ifdef ENABLE_GPU
#error "The file nnsubsample.cu should be compiled instead"
#endif
#include "nnsubsample.cu"
|
b2c651581a0ce94877ffb78354092c0c1c1206320fee1a0e01e7a1a84d3e9619 | C++ | 112 | 11 | #define GLM_FORCE_UNRESTRICTED_GENTYPE
#include <glm/glm.hpp>
int main()
{
int Error = 0;
return Error;
}
|
6e7de0d1afd4c3bbc917f417836832cbed76f25c8dcc6fa52f2eb81bd17ac9c7 | C++ | 113 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_cudatool.cu"
|
adde6923b27adafe5da466aa2c014817f568b96c977bf4d205e7af1965cf6c56 | C++ | 113 | 4 | #ifdef ENABLE_GPU
#error "The file nnroipooling.cu should be compiled instead"
#endif
#include "nnroipooling.cu"
|
be021178cb8ef3835af5dcb663895207d6b62c35f0499efd42b22c9374ea2fbe | C++ | 113 | 4 | #ifdef ENABLE_GPU
#error "The file nnbnorm.cu should be compiled instead"
#endif
#include "nnbilinearsampler.cu"
|
f8604cd37aaae8af7a69a7246242388a4782c6f8fd6c1aabd834687027abfd1a | C++ | 113 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/projection.hpp>
int main()
{
int Error(0);
return Error;
}
|
1bb61388b474228fa4171b3a5ed5177a8f29ba53ae02dcc2a5a59f5ea6819ca7 | C++ | 114 | 3 | MatrixXd m(3,4);
m.resize(NoChange, 5);
cout << "m: " << m.rows() << " rows, " << m.cols() << " cols" << endl;
|
1fc11a923e6a68866c742d77fbaccb4e38f7e567571dec28012d1e414954c522 | C++ | 114 | 3 | MatrixXd m(3,4);
m.resize(5, NoChange);
cout << "m: " << m.rows() << " rows, " << m.cols() << " cols" << endl;
|
30c77fd1678002edd329c4b7d31c77c5ef77c28f4afdbdde78655e275772f146 | C++ | 114 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_nnroipool.cu"
|
6bb59a1ebecdc64b46beca45a5b6a77d5e71485e0ae3d5c869d942500657b3e0 | C++ | 114 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/optimum_pow.hpp>
int main()
{
int Error(0);
return Error;
}
|
790d6867473d57b871c8927c64a555bc9a154dc981613d31f0172fbcc7dc7d46 | C++ | 114 | 5 | MatrixXf A(2,2), B(3,2);
B << 2, 0, 0, 3, 1, 1;
A << 2, 0, 0, -2;
A = (B * A).eval().cwiseAbs();
cout << A;
|
9c0c77f0122bea2d774bfb5f6c9eff6bb2e0451ab7484266df6f1ea56534b6e7 | C++ | 114 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_taccummex.cu"
|
bbd6d1dd3c1466f52d841a1fc29457c433d8bf2cfaa1753fa42e9bac476c5937 | C++ | 115 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_imreadjpeg.cu"
|
5aa4f79c66bb942558e7221ad0eba525adcc14090751b448da20cf1157aac58c | C++ | 116 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/closest_point.hpp>
int main()
{
int Error(0);
return Error;
}
|
c4d3a9d09917a0f0d2232c82ab1d3214df792c8e080994abeeaca04909d57077 | C++ | 116 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_nnnormalize.cu"
|
ccc8d7c72d75d6eebe13f7e7456e241934e8e3d8a6d6aaf228c6ba3471975e03 | C++ | 116 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/normalize_dot.hpp>
int main()
{
int Error(0);
return Error;
}
|
f7250bbecea01bea9a69cea9209dc7d5fa70e2fbf66b7b52550697346c6604f1 | C++ | 116 | 5 | #ifdef ENABLE_GPU
#error "The file nnnormalizelp.cu should be compiled instead"
#endif
#include "nnnormalizelp.cu"
|
f9305fcc44abf7cac120b6d6c097e1835d7cf8f3e4a274cf356b59e6859ef81d | C++ | 116 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/perpendicular.hpp>
int main()
{
int Error(0);
return Error;
}
|
98c5a25f94ddc6745fe87e60035033bcbe5a44fccd498a7108f61ff1e2f651ba | C++ | 117 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/orthonormalize.hpp>
int main()
{
int Error(0);
return Error;
}
|
c2bf624df19c54851ff13fc24bed1af183ff7b8a4a194e489bd439f3e97f0b15 | C++ | 117 | 11 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/vec_swizzle.hpp>
int main()
{
int Error = 0;
return Error;
}
|
19c9081a30e86831d017f159e78d1621d5733545f519a03ad2cb6f70ea9fa0f3 | C++ | 118 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_nnnormalizelp.cu"
|
c90768d2e47935649b75f90e46290b585f3038ff8f80bc31da9ff3b527b32a68 | C++ | 118 | 10 | #define GLM_FORCE_CXX98
#include <glm/glm.hpp>
#include <glm/ext.hpp>
int main()
{
int Error = 0;
return Error;
}
|
f240575b2331757bed13fe39cd7488a37c2e808e12dbecc1604625c9a7c15e4e | C++ | 118 | 5 | Matrix3f m;
m.row(0) << 1, 2, 3;
m.block(1,0,2,2) << 4, 5, 7, 8;
m.col(2).tail(2) << 6, 9;
std::cout << m;
|
476e16ae1760819c32ab8c5794aa7f620f5b430a94bd49100faaeae413ffb3c9 | C++ | 119 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/number_precision.hpp>
int main()
{
int Error(0);
return Error;
}
|
630ee606be144a158833da9fe9bf1862f1c6f04284daa7c39d8b80f888ba6f5d | C++ | 119 | 2 | cout << VectorXi::LinSpaced(4,7,10).transpose() << endl;
cout << VectorXd::LinSpaced(5,0.0,1.0).transpose() << endl;
|
6beda241f7f84a25565689fe74c3375a6aae0188617712d62392f05bdf8f54f2 | C++ | 119 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/extended_min_max.hpp>
int main()
{
int Error(0);
return Error;
}
|
e1fc3fa2d61b2df177b0d29efd6043bbf325e55d4519912c2b36110fa8111932 | C++ | 119 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/fast_exponential.hpp>
int main()
{
int Error(0);
return Error;
}
|
e5f06b062e9439d8dc250390c416319fa860b99b10449eafea124eee705b9263 | C++ | 119 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/matrix_operation.hpp>
int main()
{
int Error(0);
return Error;
}
|
bb8bfd818180a37f7b8bdc5c76ec2ce33ce4e3d38239a47f013b519a19c7ae20 | C++ | 120 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/color_space_YCoCg.hpp>
int main()
{
int Error(0);
return Error;
}
|
ec183b61ddbed98995162032d249e3451ad523cea84779387b5d4ba8e1d4ad36 | C++ | 120 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/polar_coordinates.hpp>
int main()
{
int Error(0);
return Error;
}
|
6d815240d66585c0c7198b1394a9f07e2807a1241110403131ceb635db834883 | C++ | 121 | 4 | #ifdef ENABLE_GPU
#error "The file nnfullyconnected.cu should be compiled instead"
#endif
#include "nnfullyconnected.cu"
|
91802a3af5a0512865e258d27fb8b00740da5f4b728a488ea03ea4b01d04cc1c | C++ | 121 | 4 | VectorXi v = VectorXi::Random(4);
cout << "Here is the vector v:\n";
for(auto x : v) cout << x << " ";
cout << "\n";
|
d745daa59514de75c05171af613fdf7f93d6782d210dc4ad5bcfa0f9d88740f5 | C++ | 121 | 6 | MatrixXi m(3, 3);
m << 1, 2, 3,
4, 5, 6,
7, 8, 9;
m = (m.array() >= 5).select(-m, m);
cout << m << endl;
|
35aef54f764a9088c33e55dc873acba2e6d2c013b9247f6a69396c9bae9e0ac1 | C++ | 122 | 3 | Matrix2d md = Matrix2d::Identity() * 0.45;
Matrix2f mf = Matrix2f::Identity();
cout << md + mf.cast<double>() << endl;
|
5d5ab406ebbc074a9344089f06686bc9956e216da8c6d5fac62651bb84bd1c92 | C++ | 122 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/matrix_transform_2d.hpp>
int main()
{
int Error(0);
return Error;
}
|
adecbb91dfc4fab53ed0153fdd666c9920bdcb46ebe7a27e5a298ad8777c7774 | C++ | 122 | 4 | #if ENABLE_GPU
#error This file should not be compiled with GPU support enabled
#endif
#include "vl_nnbilinearsampler.cu"
|
9ae85085ec47cf397ab550f1a95ce5a3ee622e646bd36ffe818ae95194a485f3 | C++ | 123 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/matrix_cross_product.hpp>
int main()
{
int Error(0);
return Error;
}
|
b96345636722538f01d9dcc61da59059a31b3c936c4eaf2e50fe6712f0885cc0 | C++ | 123 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/matrix_major_storage.hpp>
int main()
{
int Error(0);
return Error;
}
|
62ba4882af1862d48c1fb67f59322c569702b68da952f1ca3e5fb9786072dc2a | C++ | 125 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/rotate_normalized_axis.hpp>
int main()
{
int Error(0);
return Error;
}
|
162a7e9bbf1ce69166830a518ec0eb1c8d29df04f5ae904e1ef2caf6563f7caa | C++ | 126 | 9 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtx/handed_coordinate_space.hpp>
int main()
{
int Error(0);
return Error;
}
|
80e41e3427da529d9b344d16cb7739b88610c576771814e1ff640508cf6d5861 | C++ | 132 | 3 | Matrix2d a, b, c; a << 1,2,3,4; b << 5,6,7,8;
c.noalias() = a * b; // this computes the product directly to c
cout << c << endl;
|
9ea467b8de40ede5745fe87ffbea160e3c58396638b7e17ae51c0340fec937a4 | C++ | 135 | 3 | MatrixXd ones = MatrixXd::Ones(3,3);
cout << "The operator norm of the 3x3 matrix of ones is "
<< ones.operatorNorm() << endl;
|
ced9e8a78ab4ac7842425a8e1d67f729240d5715ce85c55ba01edfd4fc459fa9 | C++ | 137 | 5 | MatrixXf matA(2, 2);
matA << 1, 2, 3, 4;
MatrixXf matB(4, 4);
matB << matA, matA/10, matA/10, matA;
std::cout << matB << std::endl;
|
ff00f809cb5cf2301c59df570b0f65af40e63070a57332d6952fb4a437910959 | C++ | 139 | 9 | #include <nlohmann/json.hpp>
#include <iostream>
using json = nlohmann::json;
int main()
{
std::cout << json::meta() << std::endl;
}
|
2d58827783df1ceea19e246ee087d98eab0ccbbf3836334aa7b557738e3b4576 | C++ | 141 | 2 | cout << VectorXi::LinSpaced(Sequential,4,7,10).transpose() << endl;
cout << VectorXd::LinSpaced(Sequential,5,0.0,1.0).transpose() << endl;
|
a837e289b50f645602e6c62f05137b050434a9ce84e4a7b2cee12786b1c1fe4d | C++ | 141 | 3 | int array[12];
for(int i = 0; i < 12; ++i) array[i] = i;
cout << Map<MatrixXi, 0, OuterStride<> >(array, 3, 3, OuterStride<>(4)) << endl;
|
2a6708a5db2efc7710b3eb8f996f9ad2dcc6d207e4103f2fba1af54d04beeb41 | C++ | 142 | 13 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/vec4.hpp>
#include <glm/gtx/type_trait.hpp>
int main()
{
int Error = 0;
return Error;
}
|
b1693030e296bdc2e2af6fff5b38dd0b6f70d34b4f935a1ce2853c192b793dc5 | C++ | 143 | 6 | // Override backend class in `test_spikes.cpp`
#include <backends/gpu/fvm.hpp>
#define USE_BACKEND gpu::backend
#include "./test_spikes.cpp"
|
f4f332401fbc9ba5ad10fd6d6172a48be48a9e55351a4725da4a7a3557a77adf | C++ | 145 | 4 | Array33i a = Array33i::Random(), b = Array33i::Random();
Array33i c = a * b;
cout << "a:\n" << a << "\nb:\n" << b << "\nc:\n" << c << endl;
|
84fe7a3c4970cfe8e6831a3489ea53aab827349547b5496ab67f0543d3efd66a | C++ | 148 | 3 | Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Its inverse is:" << endl << m.inverse() << endl;
|
aa359eeff271f3c558ff0b5bd75f104be0cb12ecb03f2e13a7e47258899f1ee5 | C++ | 152 | 6 | Matrix3f A;
Vector3f b;
A << 1,2,3, 4,5,6, 7,8,10;
b << 3, 3, 4;
Vector3f x = A.inverse() * b;
cout << "The solution is:" << endl << x << endl;
|
29cd9164b08eb437c4b54b2349d78ef178827b84d0c7e27b1426a2610dc94d0a | C++ | 153 | 5 | Matrix2i A = Matrix2i::Random();
cout << "Here are the coeffs of the 2x2 matrix A:\n";
for(auto x : A.reshaped())
cout << x << " ";
cout << "\n";
|
391e9d9c6c7fc39aa8545bb518054a6df7e4896c42abe4996e978e5322bc2eae | C++ | 157 | 4 | Matrix3i a = Matrix3i::Random(), b = Matrix3i::Random();
Matrix3i c = a.cwiseProduct(b);
cout << "a:\n" << a << "\nb:\n" << b << "\nc:\n" << c << endl;
|
3f6684ab1b21c524f581ab2ba6443d1aed7cb51daedca421f993e0484a4245f4 | C++ | 159 | 18 | #include <gli/gli.hpp>
namespace test
{
int test()
{
int Error(0);
return Error;
}
}//namespace test
int main()
{
int Error(0);
return Error;
}
|
4b6b90b97cd1b8449ba6997d449073061f1f8deed37eed07fc6b5eb6b9662238 | C++ | 159 | 10 | #define GLM_ENABLE_EXPERIMENTAL
#include <glm/gtc/type_precision.hpp>
#include <glm/gtx/associated_min_max.hpp>
int main()
{
int Error(0);
return Error;
}
|
4a023269d316456ca22e0ba26b0cf42548e9c9f4fdad018567e7488731ec441b | C++ | 160 | 3 | MatrixXd ones = MatrixXd::Ones(3,3);
cout << "The operator norm of the 3x3 matrix of ones is "
<< ones.selfadjointView<Lower>().operatorNorm() << endl;
|
587d64ddeb289814dd9af1dadba4215e2f4c91ec3fe5235e975a43d82f25cdc8 | C++ | 161 | 5 | #ifdef EIGEN_SHOULD_FAIL_TO_BUILD
This is just some text that won't compile as a C++ file, as a basic sanity check for failtest.
#else
int main() {}
#endif
|
089c18d124e08a1c2f62805bbd482549b1a50a83f67078bccf6afc5df2f3f2a0 | C++ | 163 | 3 | MatrixXd ones = MatrixXd::Ones(3,3);
VectorXcd eivals = ones.eigenvalues();
cout << "The eigenvalues of the 3x3 matrix of ones are:" << endl << eivals << endl;
|
287c8e40b4ecf38a776c3fe1000390e4ac8d0518f97c1b117165c3b3fd4c9fbe | C++ | 163 | 3 | Matrix4i m = Matrix4i::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is m.reshaped(2, 8):" << endl << m.reshaped(2, 8) << endl;
|
1d83f7bcceff22a5c715fa6db10cfa068ae6215829ddb01742e56506f7643ce0 | C++ | 164 | 7 | Matrix4i A;
A << 1, 2, 3, 4,
5, 6, 7, 8,
9, 10, 11, 12,
13, 14, 15, 16;
std::cout << Matrix2i::Map(&A(1,1),Stride<8,2>()) << std::endl;
|
8cbc0c2aee3cb64932369513d3cd7c95278ebc72da9cc2493f52dd5bc5d6422c | C++ | 165 | 6 | Matrix3f A;
Vector3f b;
A << 1,2,3, 0,5,6, 0,0,10;
b << 3, 3, 4;
A.triangularView<Upper>().solveInPlace(b);
cout << "The solution is:" << endl << b << endl;
|
4ce572b6f0a3253497b3606ceec965d58d87de6fae101463274398dcf52832b0 | C++ | 167 | 3 | Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the sum of each row:" << endl << m.rowwise().sum() << endl;
|
cf966fc54575831dbfbe0703442f82f9129a30ef8f8356e6b482f6177872452d | C++ | 167 | 4 | Vector3i v = Vector3i::Random();
cout << "Here is the vector v:" << endl << v << endl;
cout << "v.replicate(2,5) = ..." << endl;
cout << v.replicate(2,5) << endl;
|
5ff8819319ea6a59592e89ac0aa83e1c0e0c2d3184132f5831adce5c3ef84ea2 | C++ | 168 | 4 | std::vector<int> ind{4,2,5,5,3};
MatrixXi A = MatrixXi::Random(4,6);
cout << "Initial matrix A:\n" << A << "\n\n";
cout << "A(all,ind):\n" << A(all,ind) << "\n\n";
|
2403008f237be5fedd6f49fe11c02896d4cd397c2961d6eb15e5ccee7cba1263 | C++ | 169 | 5 | int array[24];
for(int i = 0; i < 24; ++i) array[i] = i;
cout << Map<MatrixXi, 0, Stride<Dynamic,2> >
(array, 3, 3, Stride<Dynamic,2>(8, 2))
<< endl;
|
57ecdada1d17d698c2ac981776a3a91a4d8efb4cd253f7d8113bae8a1ef1b6ea | C++ | 171 | 4 | ArrayXi ind(5); ind<<4,2,5,5,3;
MatrixXi A = MatrixXi::Random(4,6);
cout << "Initial matrix A:\n" << A << "\n\n";
cout << "A(all,ind-1):\n" << A(all,ind-1) << "\n\n";
|
3dce187e8a947c9f9b52ba677c19a40e95dd7cbb1e57d0637a4207541385693d | C++ | 172 | 4 | MatrixXf mat = MatrixXf::Random(2, 3);
std::cout << mat << std::endl << std::endl;
mat = (MatrixXf(2,2) << 0, 1, 1, 0).finished() * mat;
std::cout << mat << std::endl;
|
46f58e0b511fc65cfcc8e82e36d9aaf1f625e8ecabf064ee4791bc1c2dc0e89e | C++ | 172 | 3 | Matrix2cf m = Matrix2cf::Random();
cout << "Here is the 2x2 complex matrix m:" << endl << m << endl;
cout << "Here is the adjoint of m:" << endl << m.adjoint() << endl;
|
65156fab31e4f5fd4e046726118ce515bbf81a49f0ada3db640e3e4c4cbb6343 | C++ | 172 | 3 | Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the product of each row:" << endl << m.rowwise().prod() << endl;
|
ea9e5881708d42533b8861cab4803991245414d92a61aad056866a27bcfcf845 | C++ | 172 | 3 | Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the norm of each column:" << endl << m.colwise().norm() << endl;
|
15bac7cfdf7594ef585943634792ca6ebbb04efe512dce4604ca135acd00e7f5 | C++ | 174 | 4 | MatrixXi m = MatrixXi::Random(2,3);
cout << "Here is the matrix m:" << endl << m << endl;
cout << "m.replicate<3,2>() = ..." << endl;
cout << m.replicate<3,2>() << endl;
|
779f9a8ef318523a98ca78e01539b357e3bcb7d484220d5706814cd2628a245f | C++ | 174 | 10 | #include <nlohmann/json.hpp>
#include <iostream>
#include <iomanip>
using json = nlohmann::json;
int main()
{
std::cout << std::setw(4) << json::meta() << std::endl;
}
|
9e544a58686ed89dce461f897907779a89856e06052b425aa251b31ed67d56f4 | C++ | 174 | 3 | Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the product of all the coefficients:" << endl << m.prod() << endl;
|
d5dfebeea1a3617ff09244aa8a0ccdbcaf0a2d5636cf288155272768a93ed492 | C++ | 177 | 6 | MatrixXf M1(2,6); // Column-major storage
M1 << 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12;
Map<MatrixXf> M2(M1.data(), 6,2);
cout << "M2:" << endl << M2 << endl;
|
480e30996ea3fb94612fb722025c5ddc2651faa98e376a928ca39619eaf37b5f | C++ | 179 | 3 | Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the maximum of each column:" << endl << m.colwise().maxCoeff() << endl;
|
8f19224dd1420bdf5e1cce09dfc32d77787aa455159d0b86733e321798a65d60 | C++ | 179 | 3 | Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the minimum of each column:" << endl << m.colwise().minCoeff() << endl;
|
0ae07f1a3ea31dbd01d77b91c6885235367c359c67043a9c9b1eeb2cfdc5e8da | C++ | 180 | 6 | MatrixXf a(2,3); a << 1, 2, 3, 4, 5, 6;
cout << "Here is the initial matrix a:\n" << a << endl;
a.transposeInPlace();
cout << "and after being transposed:\n" << a << endl;
|
365963c4fcd87542c4047ad477c4b4d2942fd333b5fb4f272f13797cfe0db325 | C++ | 180 | 4 | MatrixXd ones = MatrixXd::Ones(3,3);
EigenSolver<MatrixXd> es(ones, false);
cout << "The eigenvalues of the 3x3 matrix of ones are:"
<< endl << es.eigenvalues() << endl;
|
5920a1feacf15bc885943353f4251c884301342db49b9c00b18f9191d79c48d6 | C++ | 181 | 3 | Matrix4i m = Matrix4i::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is m.reshaped(fix<2>,fix<8>):" << endl << m.reshaped(fix<2>,fix<8>) << endl;
|
5c6ccc4fa4a4068648dbeaf790cdcacf74f736ab72e1cf8fecba75d4e4ae2b24 | C++ | 181 | 4 | MatrixXf A = MatrixXf::Random(3, 2);
VectorXf b = VectorXf::Random(3);
cout << "The solution using the QR decomposition is:\n"
<< A.colPivHouseholderQr().solve(b) << endl;
|
357bc09c33be128779184073cd5671824a13facd83b54039b148b494ef49b82a | C++ | 183 | 3 | Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the square norm of each row:" << endl << m.rowwise().squaredNorm() << endl;
|
6231ec48e869918fa353cf6bcdf5a6ad6cff85a9411d1bf6261f4865f481e8a8 | C++ | 183 | 8 | #include <atomic>
#include <thalamus/tracing.hpp>
namespace thalamus {
static std::atomic_ullong next_id = 0;
uint64_t get_unique_id() { return ++next_id; }
} // namespace thalamus
|
d0a4d018caf3ccb9b8c224533b4798e01244c9fdccd4953a3eda6bc90e264f62 | C++ | 183 | 4 | Vector3i v = Vector3i::Random();
cout << "Here is the vector v:" << endl << v << endl;
cout << "v.rowwise().replicate(5) = ..." << endl;
cout << v.rowwise().replicate(5) << endl;
|
30e32010ec5efd9b33c1e3c4a4222354c92fa26b1ced09ec708e4a9710b12974 | C++ | 184 | 4 | RowVectorXf v = RowVectorXf::LinSpaced(20,0,19);
cout << "Input:" << endl << v << endl;
Map<RowVectorXf,0,InnerStride<2> > v2(v.data(), v.size()/2);
cout << "Even:" << v2 << endl;
|
f79f3b4dcab0ffb3e850028ddc87b3e88acd650766e52ba9322ee758138ccbbb | C++ | 184 | 4 | MatrixXd ones = MatrixXd::Ones(3,3);
SelfAdjointEigenSolver<MatrixXd> es(ones);
cout << "The eigenvalues of the 3x3 matrix of ones are:"
<< endl << es.eigenvalues() << endl;
|
e07684e9c6e643705bfcd3eaaeb45f26ed424f5bc3031dd862c26242c89c1662 | C++ | 185 | 4 | MatrixXd ones = MatrixXd::Ones(3,3);
EigenSolver<MatrixXd> es(ones);
cout << "The first eigenvector of the 3x3 matrix of ones is:"
<< endl << es.eigenvectors().col(0) << endl;
|
27354842aa20e2ddccf5ec6df0a37f62a6e82f696114e66c0380f38b98e1ddec | C++ | 187 | 8 | // Cytosim was created by Francois Nedelec. Copyright 2007-2017 EMBL.
#include "exceptions.h"
/// This is used to align text in the error messages
const char PREF[] = " | ";
|
2f56f654a93ee6a31fa9e011bb2228c1bc6f2bf4b6d2da62f531bbd174b1225a | C++ | 187 | 13 |
// This anonymous enum is essential to trigger the linking issue
enum {
Foo
};
#include "bug1213.h"
bool bug1213_1(const Eigen::Vector3f& x)
{
return bug1213_2(x);
}
|
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