The elements in B preserve their columnwise ordering from A. The data type and number of elements in B are the same as the data type and number of elements in A. direction can be ascend (default) for ascending order or descend for descending order.direction can also be a cell array whose elements are ascend and descend, where each element corresponds to a column that sortrows operates on. Reshaped array, returned as a vector, matrix, multidimensional array, or cell array. So it’s not like Julia is the odd one out here. B sortrows(,direction) sorts the rows of A in the order specified by direction for any of the previous syntaxes. You can also do this - vector(:,ones(1,n)) But, if I have to choose, repmat would be the go-to approach for me, as it is made exactly for this purpose. transposing it produces a special transpose (or in the case of ' an adjoint) vector, which is ‘row-like’.sz must contain at least 2 elements, and prod (sz) must be the same as numel (A). For example, reshape (A, 2,3) reshapes A into a 2-by-3 matrix. => X(1,:)*X(1,:)' returns a 1x1 matrix (‘scalar’-like) Description example B reshape (A,sz) reshapes A using the size vector, sz, to define size (B).multiplication with * is a linear algebra operation A common task in linear algebra is to work with the transpose of a matrix, which turns the rows into columns and the columns into rows.‘row slice’ produces a 1xN matrix, not a vector. multiplication with * is element-by-element and broadcasting, not a linear algebra operation I need to convert the rows into new vectors like this: Theme.transposing a flat vector does nothing, the vector is unchanged.First of all, numpy does not return a row vector, but a flat vector like Julia: In : X.shapeĪlso, you should note that Matlab and numpy disagree with each other (as well as with Julia) in other ways: python and matlab however return a row vector (or a 1xN matrix) What size should the result be and why? in python and matlab (and julia), X will select a row.
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