[SciPy-user] array vs matrix, converting code from matlab
David Cournapeau
david at ar.media.kyoto-u.ac.jp
Thu Apr 20 22:27:25 CDT 2006
Gennan Chen wrote:
> Hi! All,
>
> I am also in the same process. And I would like to add one more
> question:
>
> In Matlab, for a 3D array or matrix, the indexing is a(i,j,k). In
> numpy, it became a[k-1,i-1,j-1]. Is there any way to make it become
> a[i-1,j-1,k-1]? Or I am doing something wrong here??
>
>
To be more specific, I am trying to convert a function which compute
multivariate Gaussian densities. It should be able to handle scalar
case, the case where the mean is a vector, and the case where va is a
vector (diagonal covariance matrix) or square matrix (full covariance
matrix).
So, in matlab, I simply do:
function [n, d, K, varmode] = gaussd_args(data, mu, var)
[n, d] = size(data);
[dm0, dm1] = size(mu);
[dv0, dv1]= size(var);
And I check that the dimensions are what I expect afterwards. Using
arrays, I don't see a simple way to do that while passing scalar
arguments to the functions. So either I should be using matrix type (and
using asmatrix on the arguments), or I should never pass scalar to the
function, and always pass arrays. But maybe I've used matlab too much,
and there is a much simpler way to do that in scipy.
To sum it up, what is the convention in scipy when a function
handles both scalar and arrays ? Is there an idiom to treat scalar and
arrays of size 1 the same way, whatever the number of dimensions arrays
may have ?
David
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