Tips and tricks
Preferences
Sometimes, Octave defaults are not teh best for someone's specific use. To change the defaults, use the following on the .octaverc file.
Changing default figure size
The default size of a figure may be appropriate for simple figures but not so much when using subplot for example. This can be changed though.
| Code: change default figure size |
set (0, 'DefaultFigurePosition', [1 get(0, "screensize")(4:-1:3) get(0, "DefaultFigurePosition")(4)]); |
The value of DefaultFigurePosition must be a four element vector with the x and y coordinates for the figure, followed by its with and height. The code above sets the default image to be placed at the top of the monitor, with the width of the monitor and the same height previously set as default.
Tiny helper functions
This is a list of tiny helper functions (the equivalent of e.g., shell aliases), the kind one would have on its .octaverc file.
replace help with man
If you use octave too much, you'll find yourself trying to use help instead of man on bash. This function will fix that so you can use man in your octave instance (you can also do the opposite, create a help alias in bash but man has less characters).
| Code: alias to help |
function man (name) help (char (name)) endfunction |
C++
Real matrix operations
This is a table of matrix operations commonly performed in Octave and their equivalents in C++ when using the octave libraries.
| Operation | Octave | C++ |
| add | A+B | A+B |
| subtract | A-B | A-B |
| matrix multiplication | A*B | A*B |
| element multiplication | A.*B | product(A,B) |
| element division | A./B | quotient(A,B) |
| transpose* | A' | A.transpose() |
| select element m,n of A** | A(m,n) | A(m-1,n-1) |
| select row N of A** | A(N,:) | A.row(N-1) |
| select column N of A** | A(:,N) | A.column(N-1) |
| extract submatrix of A | A(a:b,c:d) | A.extract(a-1,c-1,b-1,d-1) |
| absolute value of A | abs(A) | A.abs() |
| comparison to scalar*** | A>2 | mx_el_gt(A,2) |
A<2 | mx_el_lt(A,2) | |
A==2 | mx_el_eq(A,2) | |
A~=2 | mx_el_ne(A,2) | |
A>=2 | mx_el_ge(A,2) | |
A<=2 | mx_el_le(A,2) | |
| matrix of zeros | A=zeros(m,n) | A.fill(0.0) |
| matrix of ones | A=ones(m,n) | A.fill(1.0) |
| identity matrix | eye(N) | identity_matrix(N,N) |
| inverse of A | inv(A) | A.inverse() |
| pseudoinverse of A | pinv(A) | A.pseudo_inverse() |
| diagonal elements of A | diag(A) | A.diag() |
| column vector | A(:) | ColumnVector(A.reshape (dim_vector(A.length()))) |
| row vector | A(:)' | RowVector(A.reshape (dim_vector(A.length()))) |
| check for Inf or NaN | any(~isfinite(A)) | A.any_element_is_inf_or_nan() |
| stack two matrices vertically | A=[B;C] | B.stack(C) |
| uniform random matrix | rand(a,b) | octave_rand::distribution("uniform"); octave_rand::matrix(a,b) |
| normal random matrix | randn(a,b) | octave_rand::distribution("normal"); octave_rand::matrix(a,b) |
| sum squares of columns | sumsq(A) | A.sumsq() |
| sum along columns | sum(A,1) | A.sum(0) |
| sum along rows | sum(A,2) | A.sum(1) |
| product along columns | prod(A,1) | A.prod(0) |
| product along rows | prod(A,2) | A.prod(1) |
| cumsum along columns | cumsum(A,1) | A.cumsum(0) |
| cumsum along rows | cumsum(A,2) | A.cumsum(1) |
| cumproduct along columns | cumprod(A,1) | A.cumprod(0) |
| cumproduct along rows | cumprod(A,2) | A.cumprod(1) |
| number of rows | size(A,1) | A.rows() |
| number of columns | size(A,2) | A.cols() |
Notes:
- Transpose, addition, and multiplication operations also apply to RowVector, ComplexRowVector, ColumnVector, and ComplexColumnVector data types when the dimensions are in agreement.
- The difference is due to the fact that arrays are zero-based in C++, but one-based in Octave.
- The names of Octave internal functions, such as mx_el_gt, are not documented and are subject to change. Functions such as mx_el_gt may eventually be available at both the scripting level and in C++ under more common names such as gt.
Complex Matrix Operations
| Operation | Octave | C++ |
| conjugate tranpose | A' | A.hermitian() |
General
Vectorizing Tricks
You can easily fill a vector with an index:
for i=1:n, x(i) = i; end
x = 1:n;
This works for expressions on the index by wrapping the index in an expression:
for i=1:n, x(i) = sin(2*pi*i*f/r); end
x = sin(2*pi*(1:n)*f/r);
You can also work with other vectors this way:
for i=1:n, x(i) = sin(2*pi*y(i)*f/r); end
x = sin(2*pi*y*f/r);
Conditionals in the for loop are a little bit tricky. We need to create an index vector for the true condition, and another for the false condition, then calculate the two independently.
for i=1:n, if y(i)<1, x(i)=y(i); else x(i) = 2*y(i); endif
idx = y < 1; x(idx) = y(idx); x(!idx) = 2*y(!idx);
FIXME: add the following
- examples from matrices
- tricks with sort and cumsum (e.g., hist, lookup)
- counter-examples such as a tridiagonal solver
- sparse matrix tricks
- tricks relying on fortran indexing
Other references
- MATLAB array manipulation tips and tricks by Peter Acklam: http://home.online.no/~pjacklam/matlab/doc/mtt/index.html
- The MathWorks: Code Vectorization Guide: http://www.mathworks.com/support/tech-notes/1100/1109.html