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      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