I guess I'll become accustomed to it over time. I have some interesting things to do for which I will need the facilities of numpy.
I realized where I got into trouble with some of this. I was not differentiating between the dimensionality of space and that of a matrix or array. I haven't had to crank out math and computer work for quite awhile. Further, I've been doing a lot of reading on the Big Bang, and the dimensionality of space. I'm presently strongly biased towards thinking about space. For example, when I say 2D, I'm thinking of plane geometry space, and 3D as the world we live in. Thanks to all on this thread. Christopher Barker wrote: > 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 >>> >>> >>> > > > -- Wayne Watson (Watson Adventures, Prop., Nevada City, CA) (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time) Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet "... humans'innate skills with numbers isn't much better than that of rats and dolphins." -- Stanislas Dehaene, neurosurgeon Web Page: <www.speckledwithstars.net/> _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
