#encoding:latin-1
from numpy import *
from nnma_utils import *

def lnmf(V, rdim):

    V /= V.max()
    #scale(V)
    vdim, samples = V.shape
 
    W=random.rand(vdim, rdim)*2
    #W=V[:,:rdim]
    H=random.rand(rdim, samples)*2
    balance_matrixpair(W,H)

    # zeilen auf 1 normieren
    W /= ones((vdim,1)) * sum(W, axis=0)

    #while True:
    for _ in range(1155):

        # update H
        XWH = V / (dot(W,H))
        H = sqrt( H * dot(W.T, XWH))

        # update W
        ZAEHLER = W * dot(XWH, H.T)
        NENNER1 = tile(sum(W,axis=1).reshape(-1,1),(1,rdim))
        NENNER2 = tile(sum(H.T,axis=0), (vdim,1))
        W = ZAEHLER / (NENNER1+ NENNER2)
        # scale cols of W to 1
        W /= ones((vdim,1)) * sum(W, axis=0)
        #print sum(W, axis=0)

        print linalg.norm(V-dot(W,H)), sum(dot(W.T,W)), sum(diag(dot(H,H.T)))
        #print V
        #print dot(W,H)
        #print W
        #print
    return W,H


ONES=ones((3,2))
ZEROS=zeros((3,2))

W =vstack( ( hstack((ONES,ZEROS)) , hstack((ZEROS, ONES)) ))  
print W
print W.shape
H = random.rand(2,6)
V = dot(W,H)
scale(V)

Wn, Hn = lnmf(V,4)
print dot(Wn,Hn)
print V