Hey, folks.

So, I'm doing some Kmeans classification using numpy arrays that are quite sparse-- lots and lots of zeroes. I figured that I'd use scipy's 'sparse' package to reduce the storage overhead, but I'm a little confused about how to create arrays, not matrices.

I've gone through this tutorial on how to create sparse matrices: http://www.scipy.org/SciPy_Tutorial#head-c60163f2fd2bab79edd94be43682414f18b90df7

To mimic an array, I just create a 1xN matrix, but as you may guess, Asp.dot(Bsp) doesn't quite work because you can't multiply two 1xN matrices. I'd have to transpose each array to Nx1, and that's pretty lame, since I'd be doing it for every dot-product calculation.

Next up, I tried to create an NxN matrix where column 1 == row 1 (such that you can multiply two matrices and just take the top-left corner as the dot product), but that turned out to be really inefficient.

I'd love to use scipy's sparse package as a magic replacement for numpy's array(), but as yet, I'm not really sure what to do.

Any advice?

Thank you very much!