# Tips and tricks

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# 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 <a href="wiki.pl?NaN">NaN</a> | `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()` |