[Numpy-discussion] mixing arrays and matrices: squeeze yes, flattened no?
Travis Oliphant
oliphant.travis at ieee.org
Tue Feb 21 11:13:02 CST 2006
Sven Schreiber wrote:
>Hi, sometimes I'm still struggling with peculiarities of numpy-arrays
>vs. numpy-matrices; my latest story goes like this:
>
>I first slice out a column of a 2d-numpy-array (a = somearray[:,1]). I
>can just manage to understand the resulting shape ( == (112,) ).
>
>Then I slice a column from a numpy-matrix b = somematrix[:,1] and get
>the expected (112,1) shape.
>
>Then I do what I thought was the easiest thing in the world, I subtract
>the two vectors: c = a - b
>I was very surprised by the bug that showed up due to the fact that
>c.shape == (112,112) !!
>
>
As you know this isn't a bug, but very expected behavior. I don't see
this changing any time soon. Arrays are different than matrices.
Matrices are always 2-d arrays while arrays can have any number of
dimensions.
The default relationship between arrays and matrices is that 1-d arrays
get converted to row-matrices (1,N). Regardless of which convention is
chosen somebody will be bitten by that conversion if they think in terms
of the other default. I don't see a way around that except to be
careful when you mix arrays and matrices.
>Next, I try to workaround by b.squeeze(). That seems to work, but why is
>b.squeeze().shape == (1, 112) instead of (112,)?
>
>
Again the same reason as before. A matrix is returned from b.squeeze()
and there are no 1-d matrices. Thus, you get a row-vector. Use .T if
you want a column vector.
>Then I thought maybe b.flattened() does the job, but then I get an error
>(matrix has no attr flattened). Again, I'm baffled.
>
>
The correct spelling is b.flatten()
And again you are going to get a (1,N) matrix out because of how 1d
arrays are interpreted as matrices.
In short, there is no way to get a 1-d matrix because that doesn't make
sense. You can get a 1-d array using
b.A.squeeze()
>Second (preliminary) conclusion: I will paranoically use even more
>asmatrix()-conversions in my code to avoid dealing with those
>array-beasts ;-) and get column vectors I can trust...
>
>
>
>Is there a better general advice than to say: "numpy-matrices and
>numpy-arrays are best kept in separated worlds" ?
>
>
You can mix arrays and matrices just fine if you remember that 1d arrays
are equivalent to row-vectors.
-Travis
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