1 #include <Eigen/Array> 2 3 int main(int argc, char *argv[]) 4 { 5 std::cout.precision(2); 6 7 // demo static functions 8 Eigen::Matrix3f m3 = Eigen::Matrix3f::Random(); 9 Eigen::Matrix4f m4 = Eigen::Matrix4f::Identity(); 10 11 std::cout << "*** Step 1 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl; 12 13 // demo non-static set... functions 14 m4.setZero(); 15 m3.diagonal().setOnes(); 16 17 std::cout << "*** Step 2 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl; 18 19 // demo fixed-size block() expression as lvalue and as rvalue 20 m4.block<3,3>(0,1) = m3; 21 m3.row(2) = m4.block<1,3>(2,0); 22 23 std::cout << "*** Step 3 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl; 24 25 // demo dynamic-size block() 26 { 27 int rows = 3, cols = 3; 28 m4.block(0,1,3,3).setIdentity(); 29 std::cout << "*** Step 4 ***\nm4:\n" << m4 << std::endl; 30 } 31 32 // demo vector blocks 33 m4.diagonal().block(1,2).setOnes(); 34 std::cout << "*** Step 5 ***\nm4.diagonal():\n" << m4.diagonal() << std::endl; 35 std::cout << "m4.diagonal().start(3)\n" << m4.diagonal().start(3) << std::endl; 36 37 // demo coeff-wise operations 38 m4 = m4.cwise()*m4; 39 m3 = m3.cwise().cos(); 40 std::cout << "*** Step 6 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl; 41 42 // sums of coefficients 43 std::cout << "*** Step 7 ***\n m4.sum(): " << m4.sum() << std::endl; 44 std::cout << "m4.col(2).sum(): " << m4.col(2).sum() << std::endl; 45 std::cout << "m4.colwise().sum():\n" << m4.colwise().sum() << std::endl; 46 std::cout << "m4.rowwise().sum():\n" << m4.rowwise().sum() << std::endl; 47 48 // demo intelligent auto-evaluation 49 m4 = m4 * m4; // auto-evaluates so no aliasing problem (performance penalty is low) 50 Eigen::Matrix4f other = (m4 * m4).lazy(); // forces lazy evaluation 51 m4 = m4 + m4; // here Eigen goes for lazy evaluation, as with most expressions 52 m4 = -m4 + m4 + 5 * m4; // same here, Eigen chooses lazy evaluation for all that. 53 m4 = m4 * (m4 + m4); // here Eigen chooses to first evaluate m4 + m4 into a temporary. 54 // indeed, here it is an optimization to cache this intermediate result. 55 m3 = m3 * m4.block<3,3>(1,1); // here Eigen chooses NOT to evaluate block() into a temporary 56 // because accessing coefficients of that block expression is not more costly than accessing 57 // coefficients of a plain matrix. 58 m4 = m4 * m4.transpose(); // same here, lazy evaluation of the transpose. 59 m4 = m4 * m4.transpose().eval(); // forces immediate evaluation of the transpose 60 61 std::cout << "*** Step 8 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl; 62 } 63
