Hacker News Reader

you can skip sklear and other packages and just use numpy (which is supported in pypy now), and just do an svd of the input matrix.

U,d,Vt=svd(X)

D=diag(d)

Xhat= U[:,:2].dot(D[:2, :2])

in addition if you want a way to project new vectors into a reduced svd space:

l=2 # desired vector space dimension

U,d,Vt = linalg.svd(X,full_matrices=False)

P=(U*(1/d))[:,:l]

xnew = dot(x,P)