SVD algorithm in Numpy

http://www.scipy.org/doc/numpy_api_docs/numpy.linalg.linalg.html#svd

Numpy routine implementing svd operation, usefull for triangulating points.svd(a, full_matrices = 1, compute_uv = 1)

Singular Value Decomposition.

u,s,vh = svd(a)

If a is an M x N array, then the svd produces a factoring of the array
into two unitary (orthogonal) 2-d arrays u (MxM) and vh (NxN) and a
min(M,N)-length array of singular values such that
 
                a == dot(u,dot(S,vh))

where S is an MxN array of zeros whose main diagonal is s.

if compute_uv == 0, then return only the singular values
if full_matrices == 0, then only part of either u or vh is
                      returned so that it is MxN