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Update matlab-eigen quick ascii reff

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Gael Guennebaud 2013-03-11 21:20:12 +01:00
parent 6c68f1d787
commit 5d1a74da0a
1 changed files with 22 additions and 13 deletions
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35
doc/AsciiQuickReference.txt
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@ -1,8 +1,7 @@
// A simple quickref for Eigen. Add anything that's missing.
// Main author: Keir Mierle
#include <Eigen/Core>
#include <Eigen/Array>
#include <Eigen/Dense>
Matrix<double, 3, 3> A; // Fixed rows and cols. Same as Matrix3d.
Matrix<double, 3, Dynamic> B; // Fixed rows, dynamic cols.
@ -11,6 +10,7 @@ Matrix<double, 3, 3, RowMajor> E; // Row major; default is column-major.
Matrix3f P, Q, R; // 3x3 float matrix.
Vector3f x, y, z; // 3x1 float matrix.
RowVector3f a, b, c; // 1x3 float matrix.
VectorXd v; // Dynamic column vector of doubles
double s;
// Basic usage
@ -31,9 +31,19 @@ A << 1, 2, 3, // Initialize A. The elements can also be
7, 8, 9; // and then the rows are stacked.
B << A, A, A; // B is three horizontally stacked A's.
A.fill(10); // Fill A with all 10's.
A.setRandom(); // Fill A with uniform random numbers in (-1, 1).
// Requires #include <Eigen/Array>.
A.setIdentity(); // Fill A with the identity.
// Eigen // Matlab
MatrixXd::Identity(rows,cols) // eye(rows,cols)
C.setIdentity(rows,cols) // C = eye(rows,cols)
MatrixXd::Zero(rows,cols) // zeros(rows,cols)
C.setZero(rows,cols) // C = ones(rows,cols)
MatrixXd::Ones(rows,cols) // ones(rows,cols)
C.setOnes(rows,cols) // C = ones(rows,cols)
MatrixXd::Random(rows,cols) // rand(rows,cols)*2-1 // MatrixXd::Random returns uniform random numbers in (-1, 1).
C.setRandom(rows,cols) // C = rand(rows,cols)*2-1
VectorXd::LinSpace(size,low,high) // linspace(low,high,size)'
v.setLinSpace(size,low,high) // v = linspace(low,high,size)'
// Matrix slicing and blocks. All expressions listed here are read/write.
// Templated size versions are faster. Note that Matlab is 1-based (a size N
@ -77,8 +87,7 @@ a *= M; R = P + Q; R = P/s;
R += Q; R *= s;
R -= Q; R /= s;
// Vectorized operations on each element independently
// (most require #include <Eigen/Array>)
// Vectorized operations on each element independently
// Eigen // Matlab
R = P.cwiseProduct(Q); // R = P .* Q
R = P.array() * s.array();// R = P .* s
@ -150,12 +159,11 @@ MatrixXi mat2x2 = Map<Matrix2i>(data);
MatrixXi mat2x2 = Map<MatrixXi>(data, 2, 2);
// Solve Ax = b. Result stored in x. Matlab: x = A \ b.
bool solved;
solved = A.ldlt().solve(b, &x)); // A sym. p.s.d. #include <Eigen/Cholesky>
solved = A.llt() .solve(b, &x)); // A sym. p.d. #include <Eigen/Cholesky>
solved = A.lu() .solve(b, &x)); // Stable and fast. #include <Eigen/LU>
solved = A.qr() .solve(b, &x)); // No pivoting. #include <Eigen/QR>
solved = A.svd() .solve(b, &x)); // Stable, slowest. #include <Eigen/SVD>
x = A.ldlt().solve(b)); // A sym. p.s.d. #include <Eigen/Cholesky>
x = A.llt() .solve(b)); // A sym. p.d. #include <Eigen/Cholesky>
x = A.lu() .solve(b)); // Stable and fast. #include <Eigen/LU>
x = A.qr() .solve(b)); // No pivoting. #include <Eigen/QR>
x = A.svd() .solve(b)); // Stable, slowest. #include <Eigen/SVD>
// .ldlt() -> .matrixL() and .matrixD()
// .llt() -> .matrixL()
// .lu() -> .matrixL() and .matrixU()
@ -168,3 +176,4 @@ A.eigenvalues(); // eig(A);
EigenSolver<Matrix3d> eig(A); // [vec val] = eig(A)
eig.eigenvalues(); // diag(val)
eig.eigenvectors(); // vec
// For self-adjoint matrices use SelfAdjointEigenSolver<>