#!/usr/bin/env python
from nose.tools import *
from nose import SkipTest
import networkx

# Example from
# A. Langville and C. Meyer, "A survey of eigenvector methods of web
# information retrieval."  http://citeseer.ist.psu.edu/713792.html


class TestHITS:

    def setUp(self):
        
        G=networkx.DiGraph()

        edges=[(1,3),(1,5),\
           (2,1),\
           (3,5),\
           (5,4),(5,3),\
           (6,5)]
           
        G.add_edges_from(edges,weight=1)
        self.G=G
        self.G.a=dict(zip(G,[0.000000, 0.000000, 0.366025,
                             0.133975, 0.500000, 0.000000]))
        self.G.h=dict(zip(G,[ 0.366025, 0.000000, 0.211325, 
                              0.000000, 0.211325, 0.211325]))


    def test_hits(self):
        G=self.G
        h,a=networkx.hits(G,tol=1.e-08)
        for n in G:
            assert_almost_equal(h[n],G.h[n],places=4)
        for n in G:
            assert_almost_equal(a[n],G.a[n],places=4)

    def test_hits_numpy(self):
        try:
            import numpy as np
        except ImportError:
            raise SkipTest('NumPy not available.')


        G=self.G
        h,a=networkx.hits_numpy(G)
        for n in G:
            assert_almost_equal(h[n],G.h[n],places=4)
        for n in G:
            assert_almost_equal(a[n],G.a[n],places=4)


    def test_hits_scipy(self):
        try:
            import scipy as sp
        except ImportError:
            raise SkipTest('SciPy not available.')

        G=self.G
        h,a=networkx.hits_scipy(G,tol=1.e-08)
        for n in G:
            assert_almost_equal(h[n],G.h[n],places=4)
        for n in G:
            assert_almost_equal(a[n],G.a[n],places=4)


