On 2009-07-29 18:27, Colin J. Williams wrote:
Robert Kern wrote:
On 2009-07-29 15:23, Nanime Puloski wrote:
What are some differences between arrays and matrices using the Numpy
library? When would I want to use arrays instead of matrices and vice
versa?
You will want to ask numpy questions on the numpy mailing list:
http://www.scipy.org/Mailing_Lists
An overview of how the matrix subclass differs from ndarray, see the
documentation:
http://docs.scipy.org/doc/numpy/reference/arrays.classes.html#matrix-objects
Basically, I suggest that you just use regular arrays always. There is
a syntactical convenience to matrix objects, but it does cause
incompatibilities with the majority of code that is written for
regular arrays. The convenience is usually not worth the cost.
Numpy's arrays can have any dimensionality, whereas
matrices[http://en.wikipedia.org/wiki/Matrix_%28mathematics%29],
typically have two. A single column can represent a vector or a single
row can represent a transposed vector.
Does the additional cost arise because the commonly used procedures are
accessed through numpy's array?
Most functions are written to expect that its inputs behave like ndarrays; e.g.
a*b is elementwise multiplication rather than matrix multiplication. When you
use the matrix subclass, you are basically confining yourself to a smallish
ghetto of functions that knows how to deal with matrix semantics. That's a huge
cost compared to the relatively small syntactic cost of having to write dot(a,b)
instead of (a*b).
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
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