Vector from Dimension of Matrix

single_dimension(matrix, scalar=1)

Extracts a column vector as a row vector from a matrix according to an integer corresponding to the column’s position

Parameters

  • matrix (list of lists of int or float) – List containing other lists, where each inner list is a row and elements within those inner lists correspond to columns

  • scalar (int, default=1) – Number corresponding to the column’s position

Raises

  • TypeError – First argument must be a 2-dimensional list

  • TypeError – Elements nested within the first argument’s lists must be integers or floats

  • ValueError – Last argument must be a positive integer

Returns

vector – List containing only integers or floats

Return type

list of int or float

See also

column_conversion(), sorted_dimension(), half_dimension()

Notes

  • Matrix: \(\begin{bmatrix} a_{1,1} & a_{1,2} & \cdots & a_{1,n} \\ a_{2,1} & a_{2,2} & \cdots & a_{2,n} \\ \cdots & \cdots & \cdots & \cdots \\ a_{m,1} & a_{m,2} & \cdots & a_{m,n} \end{bmatrix}\)

  • Row vector corresponding to the \(n\)th column of the matrix: \(\langle a_{1,n}, a_{2,n}, \cdots, a_{m,n} \rangle\)

Examples

Import single_dimension function from regressions library
>>> from regressions.vectors.dimension import single_dimension
Extract the second column from the matrix [[3, 5, 9], [1, -4, 2]]
>>> vector_2c = single_dimension([[3, 5, 9], [1, -4, 2]], 2)
>>> print(vector_2c)
[5, -4]
Extract the first column from the matrix [[3, 5, 9], [1, -4, 2]]
>>> vector_1c = single_dimension([[3, 5, 9], [1, -4, 2]], 1)
>>> print(vector_1c)
[3, 1]