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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