1 files changed, 0 insertions, 122 deletions
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/tests/test_pagerank.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/tests/test_pagerank.py
deleted file mode 100644
index 7b409ff..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/tests/test_pagerank.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-from nose.plugins.attrib import attr
-import random
-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 TestPageRank:
-
-    def setUp(self):
-        G=networkx.DiGraph()
-        edges=[(1,2),(1,3),\
-           (3,1),(3,2),(3,5),\
-           (4,5),(4,6),\
-           (5,4),(5,6),\
-           (6,4)]
-        G.add_edges_from(edges)
-        self.G=G
-        self.G.pagerank=dict(zip(G,
-                                 [0.03721197,0.05395735,0.04150565,
-                                  0.37508082,0.20599833, 0.28624589]))
-
-    def test_pagerank(self):
-        G=self.G
-        p=networkx.pagerank(G,alpha=0.9,tol=1.e-08)
-        for n in G:
-            assert_almost_equal(p[n],G.pagerank[n],places=4)
-
-        nstart = dict((n,random.random()) for n in G)
-        p=networkx.pagerank(G,alpha=0.9,tol=1.e-08, nstart=nstart)
-        for n in G:
-            assert_almost_equal(p[n],G.pagerank[n],places=4)
-
-        assert_raises(networkx.NetworkXError,networkx.pagerank,G,
-                      max_iter=0)
-
-
-    @attr('numpy')
-    def test_numpy_pagerank(self):
-        try:
-            import numpy
-        except ImportError:
-            raise SkipTest('numpy not available.')
-        G=self.G
-        p=networkx.pagerank_numpy(G,alpha=0.9)
-        for n in G:
-            assert_almost_equal(p[n],G.pagerank[n],places=4)
-        personalize = dict((n,random.random()) for n in G)
-        p=networkx.pagerank_numpy(G,alpha=0.9, personalization=personalize)
-
-
-
-    @attr('numpy')
-    def test_google_matrix(self):
-        try:
-            import numpy.linalg
-        except ImportError:
-            raise SkipTest('numpy not available.')
-        G=self.G
-        M=networkx.google_matrix(G,alpha=0.9)
-        e,ev=numpy.linalg.eig(M.T)
-        p=numpy.array(ev[:,0]/ev[:,0].sum())[:,0]
-        for (a,b) in zip(p,self.G.pagerank.values()):
-            assert_almost_equal(a,b)
-
-        personalize = dict((n,random.random()) for n in G)
-        M=networkx.google_matrix(G,alpha=0.9, personalization=personalize)
-        _ = personalize.pop(1)
-        assert_raises(networkx.NetworkXError,networkx.google_matrix,G,
-                      personalization=personalize)
-
-    def test_scipy_pagerank(self):
-        G=self.G
-        try:
-            import scipy
-        except ImportError:
-            raise SkipTest('scipy not available.')
-        p=networkx.pagerank_scipy(G,alpha=0.9,tol=1.e-08)
-        for n in G:
-            assert_almost_equal(p[n],G.pagerank[n],places=4)
-        personalize = dict((n,random.random()) for n in G)
-        p=networkx.pagerank_scipy(G,alpha=0.9,tol=1.e-08,
-                                  personalization=personalize)
-
-        assert_raises(networkx.NetworkXError,networkx.pagerank_scipy,G,
-                      max_iter=0)
-
-    def test_personalization(self):
-        G=networkx.complete_graph(4)
-        personalize={0:1,1:1,2:4,3:4}
-        answer={0:0.1,1:0.1,2:0.4,3:0.4}
-        p=networkx.pagerank(G,alpha=0.0,personalization=personalize)
-        for n in G:
-            assert_almost_equal(p[n],answer[n],places=4)
-        _ = personalize.pop(0)
-        assert_raises(networkx.NetworkXError,networkx.pagerank,G,
-                      personalization=personalize)
-
-
-    @attr('numpy')
-    def test_empty(self):
-        try:
-            import numpy
-        except ImportError:
-            raise SkipTest('numpy not available.')
-        G=networkx.Graph()
-        assert_equal(networkx.pagerank(G),{})
-        assert_equal(networkx.pagerank_numpy(G),{})
-        assert_equal(networkx.google_matrix(G).shape,(0,0))
-
-    def test_empty_scipy(self):
-        try:
-            import scipy
-        except ImportError:
-            raise SkipTest('scipy not available.')
-        G=networkx.Graph()
-        assert_equal(networkx.pagerank_scipy(G),{})