Wayne Watson wrote: > Yes, flat sounds useful here. However, numpy isn't bending over > backwards to tie in conventional mathematical language into it.
exactly -- it isn't bending over at all! (well a little -- see below). numpy was designed for general purpose computational needs, not any one branch of math. nd-arrays are very useful for lots of things. In contrast, Matlab, for instance, was originally designed to be an easy front-end to linear algebra package. Personally, when I used Matlab, I found that very awkward -- I was usually writing 100s of lines of code that had nothing to do with linear algebra, for every few lines that actually did matrix math. So I much prefer numpy's way -- the linear algebra lines of code are longer an more awkward, but the rest is much better. The Matrix class is the exception to this: is was written to provide a natural way to express linear algebra. However, things get a bit tricky when you mix matrices and arrays, and even when sticking with matrices there are confusions and limitations -- how do you express a row vs a column vector? what do you get when you iterate over a matrix? etc. There has been a bunch of discussion about these issues, a lot of good ideas, a little bit of consensus about how to improve it, but no one with the skill to do it has enough motivation to do it. As for your problem, I think a 3-d euclidean vector is well expressed as a (3,) shape array, and then you don't need flat, etc. In [6]: v1 = np.array((1,2,3), dtype=np.float) In [7]: v2 = np.array((3,1,2), dtype=np.float) In [8]: np.dot(v1,v2) Out[8]: 11.0 -Chris > I don't recall flat in any calculus books. :-) Maybe I've been away so > long from it, that it is a common math concept? Although I doubt that. > > > Alan G Isaac wrote: >> On 12/19/2009 11:45 AM, Wayne Watson wrote: >> >>> A 4x1, 1x7, and 1x5 would be examples of a 1D array or matrix, right? >>> >>> Are you saying that instead of using a rotational matrix ... >>> that I should use a 2-D array for rotCW? So why does numpy have a matrix >>> class? Is the class only used when working with matplotlib? >>> >>> To get the scalar value (sum of squares) I had to use a transpose, T, on >>> one argument. >>> >> >> At this point, you have raised some long standing issues. >> There are a couple standard replies people give to some of them. >> E.g., >> >> 1. don't use matrices, OR >> 2. don't mix the use of matrices and arrays >> >> Matrices are *always* 2d (e.g., a "row vector" or a "column vector" is 2d). >> So in fact you should find it quite natural that that transpose was needed. >> Matrices change * to matrix multiplication and ** to matrix exponentiation. >> I find this very convenient, especially in a teaching setting, so I use >> NumPy matrices all the time. Many on this list avoid them completely. >> >> Again, if you want a *scalar* as the product of vectors for which you >> created matrix objects (e.g., a and b), you can just use flat: >> np.dot(a.flat,b.flat) >> >> hth, >> Alan Isaac >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
