PageRank python implementation, algorithm

Question

I'm trying to write this matlab implementation in Python, but I don't understand a few moments:

last_v = ones(N, 1) * inf;

Do we obtain here a vector, containing all infinities? In this case the condition in while is instantly false and we don't obtain any iterations:

while(norm(v - last_v, 2) > v_quadratic_error)

What do I understand false?

This is the way, I tried it to do:

from numpy import *

def pagerank(M,d,v_quadratic_error):
    count = 0
    N=M.shape[1]
    v=random.rand(N,1)
    v=v/linalg.norm(v)
    ainf=array([[inf]])
    last_v = dot(ones((N,1)),ainf)
    R = d*M + ((1-d)/N * ones((N,N)))

    while linalg.norm(v-last_v,2) > v_quadratic_error:
        last_v = v
        v = dot(R,v)
        count+=1
        print 'iteration #', count
    return v

Answer 1

In Matlab/Octave:

octave:4> last_v = ones(N, 1) * inf;
octave:10> norm(v - last_v, 2)
ans = Inf
octave:13> norm(v - last_v, 2) > v_quadratic_error
ans =  1

In Python:

In [139]: last_v = np.dot(np.ones((N,1)),ainf)
In [140]: np.linalg.norm(v - last_v, 2)
Out[140]: nan
In [141]: np.linalg.norm(v - last_v, 2) <= v_quadratic_error
Out[141]: False

So the condition is True in Matlab/Octave but the similar expression in Python is False. In Python, instead of using

while linalg.norm(v-last_v,2) > v_quadratic_error:

you could use

while True:
    last_v = v
    ...
    if np.linalg.norm(v - last_v, 2) <= v_quadratic_error: break

This guarantees that the flow of execution enters the while-loop at least once, and then breaks when the condition is True. By that point last_v will have a finite value, so the NaN versus Inf issue is avoided.

---

import numpy as np

def pagerank(M, d, v_quadratic_error):
    count = 0
    N = M.shape[1]
    while True:
        v = np.random.rand(N, 1)
        if (v != 0).all(): break
    v = v / np.linalg.norm(v)
    R = d * M + ((1 - d) / N * np.ones((N, N)))
    while True:
        last_v = v
        v = np.dot(R, v)
        count += 1
        print('iteration # {}: {}'.format(count, np.isfinite(v)))
        if np.linalg.norm(v - last_v, 2) <= v_quadratic_error: break
    return v

M = np.array(np.mat('0 0 0 0 1 ; 0.5 0 0 0 0 ; 0.5 0 0 0 0 ; 0 1 0.5 0 0 ; 0 0 0.5 1 0'))
print(pagerank(M, 0.80, 0.001))

yields (something like)

[[ 0.46322263]
 [ 0.25968575]
 [ 0.25968575]
 [ 0.38623472]
 [ 0.48692059]]