python implementation of pagerank

pagerank.py

import numpy as np
from scipy.sparse import csc_matrix

def pageRank(G, s = .85, maxerr = .001):
    """
    Computes the pagerank for each of the n states.

    Used in webpage ranking and text summarization using unweighted
    or weighted transitions respectively.


    Args
    ----------
    G: matrix representing state transitions
       Gij can be a boolean or non negative real number representing the
       transition weight from state i to j.

    Kwargs
    ----------
    s: probability of following a transition. 1-s probability of teleporting
       to another state. Defaults to 0.85

    maxerr: if the sum of pageranks between iterations is bellow this we will
            have converged. Defaults to 0.001
    """
    n = G.shape[0]

    # transform G into markov matrix M
    M = csc_matrix(G,dtype=np.float)
    rsums = np.array(M.sum(1))[:,0]
    ri, ci = M.nonzero()
    M.data /= rsums[ri]

    # bool array of sink states
    sink = rsums==0

    # Compute pagerank r until we converge
    ro, r = np.zeros(n), np.ones(n)
    while np.sum(np.abs(r-ro)) > maxerr:
        ro = r.copy()
        # calculate each pagerank at a time
        for i in xrange(0,n):
            # inlinks of state i
            Ii = np.array(M[:,i].todense())[:,0]
            # account for sink states
            Si = sink / float(n)
            # account for teleportation to state i
            Ti = np.ones(n) / float(n)

            r[i] = ro.dot( Ii*s + Si*s + Ti*(1-s) )

    # return normalized pagerank
    return r/sum(r)




if __name__=='__main__':
    # Example extracted from 'Introduction to Information Retrieval'
    G = np.array([[0,0,1,0,0,0,0],
                  [0,1,1,0,0,0,0],
                  [1,0,1,1,0,0,0],
                  [0,0,0,1,1,0,0],
                  [0,0,0,0,0,0,1],
                  [0,0,0,0,0,1,1],
                  [0,0,0,1,1,0,1]])

    print pageRank(G,s=.86)

@godvinpoulose xrange() is for python 2. if you're using python 3, which apparently you are, change xrange() to rang(), which is also faster than xrange().

can someone explain in detail hoe the code is implemented?
especially this line Ai = np.array(A[:,i].todense())[:,0]

line 33 xrange is not defined

@hi101000 > line 33 xrange is not defined

Change xrange() (a python 2 function) to range () (a python 3 function)