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#include <Eigen/Array>
int main(int argc, char *argv[])
{
std::cout.precision(2);
// demo static functions
Eigen::Matrix3f m3 = Eigen::Matrix3f::Random();
Eigen::Matrix4f m4 = Eigen::Matrix4f::Identity();
std::cout << "*** Step 1 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;
// demo non-static set... functions
m4.setZero();
m3.diagonal().setOnes();
std::cout << "*** Step 2 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;
// demo fixed-size block() expression as lvalue and as rvalue
m4.block<3,3>(0,1) = m3;
m3.row(2) = m4.block<1,3>(2,0);
std::cout << "*** Step 3 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;
// demo dynamic-size block()
{
int rows = 3, cols = 3;
m4.block(0,1,3,3).setIdentity();
std::cout << "*** Step 4 ***\nm4:\n" << m4 << std::endl;
}
// demo vector blocks
m4.diagonal().block(1,2).setOnes();
std::cout << "*** Step 5 ***\nm4.diagonal():\n" << m4.diagonal() << std::endl;
std::cout << "m4.diagonal().start(3)\n" << m4.diagonal().start(3) << std::endl;
// demo coeff-wise operations
m4 = m4.cwise()*m4;
m3 = m3.cwise().cos();
std::cout << "*** Step 6 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;
// sums of coefficients
std::cout << "*** Step 7 ***\n m4.sum(): " << m4.sum() << std::endl;
std::cout << "m4.col(2).sum(): " << m4.col(2).sum() << std::endl;
std::cout << "m4.colwise().sum():\n" << m4.colwise().sum() << std::endl;
std::cout << "m4.rowwise().sum():\n" << m4.rowwise().sum() << std::endl;
// demo intelligent auto-evaluation
m4 = m4 * m4; // auto-evaluates so no aliasing problem (performance penalty is low)
Eigen::Matrix4f other = (m4 * m4).lazy(); // forces lazy evaluation
m4 = m4 + m4; // here Eigen goes for lazy evaluation, as with most expressions
m4 = -m4 + m4 + 5 * m4; // same here, Eigen chooses lazy evaluation for all that.
m4 = m4 * (m4 + m4); // here Eigen chooses to first evaluate m4 + m4 into a temporary.
// indeed, here it is an optimization to cache this intermediate result.
m3 = m3 * m4.block<3,3>(1,1); // here Eigen chooses NOT to evaluate block() into a temporary
// because accessing coefficients of that block expression is not more costly than accessing
// coefficients of a plain matrix.
m4 = m4 * m4.transpose(); // same here, lazy evaluation of the transpose.
m4 = m4 * m4.transpose().eval(); // forces immediate evaluation of the transpose
std::cout << "*** Step 8 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;
}
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