[Numpy-discussion] Matlab -> NumPy translation and indexing

[Christopher Barker]

David Koch wrote:
> - Is it "pythonic" to initialize vectors to 2 dimensions so that 
> vec.shape == (len(vec), 1) instead of vec.shape == (len(vec),)?

It depends what it means -- and this is not a question of Pythonic -- 
maybe Numpythonic?

Numpy is an n-d array package -- NOT a matrix package (if you really 
want matrices, see the numpy matrix package).

thus, a Vector can be represented as a (n,) shaped array
       a Matrix can be represented by a (n,m) shaped array
etc...

If what you want is a matrix that happens to have one column, you want a 
(n,1) array, if you want one row, you want a (1,n) array. In linear 
algebra terms, these are row and column vectors. In other math, a vector 
is (n,) in shape.

Other than linear algebra, a reason to use 2-d "vectors" is for array 
broadcasting:

 >>> import numpy as N
 >>> x = N.arange(10).reshape(1,-1) # a "row" vector
 >>> y = N.arange(5).reshape(-1,1) # a "column" vector
 >>> x.shape
(1, 10)
 >>> y.shape
(5, 1)
 >>> z = x * y
 >>> z
array([[ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0],
        [ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
        [ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18],
        [ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27],
        [ 0,  4,  8, 12, 16, 20, 24, 28, 32, 36]])
 >>>

## note that you didn't' really need to reshape x, as a (n,) array is 
interpreted as a row vector for broadcasting purposes, but I like to do 
it for clarities sake.

In MATLAB, there is no such thing as a vector, so you only have the last 
two options -- not the first.

-Chris




-- 
Christopher Barker, Ph.D.
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