def test_generalized_laplacian(self): "Generalized Graph Laplacian" GL = numpy.array([[1.00, -0.408, -0.408, -0.577, 0.00], [-0.408, 1.00, -0.50, 0.00, 0.00], [-0.408, -0.50, 1.00, 0.00, 0.00], [-0.577, 0.00, 0.00, 1.00, 0.00], [0.00, 0.00, 0.00, 0.00, 0.00]]) assert_almost_equal(nx.generalized_laplacian(self.G), GL, decimal=3)
def test_generalized_laplacian(self): "Generalized Graph Laplacian" GL=numpy.array([[ 1.00, -0.41, -0.41, -0.58, 0.00], [-0.41, 1.00, -0.50, 0.00 , 0.00], [-0.41, -0.50, 1.00, 0.00, 0.00], [-0.58, 0.00, 0.00, 1.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00]]) assert_almost_equal(nx.generalized_laplacian(self.G),GL,decimal=2)
try: from pylab import * except: pass from graphml import read_graphml import sys, os if len(sys.argv) == 2: fn = sys.argv[1] print "Reading in %s" % fn g = read_graphml(fn) print "Generating a generalized laplacian" l = nx.generalized_laplacian(g) print "Calculating eigenvalues" e = eigenvalues(l) print ("Largest eigenvalue:", max(e)) print ("Smallest eigenvalue:", min(e)) # plot with matplotlib if we have it # shows "semicircle" distribution of eigenvalues try: print "Trying to plot semicircle of eigenvalues" hist(e, bins=100) # histogram with 100 bins xlim(0, 2) # eigenvalues between 0 and 2 show() # plt.savefig("%s.eigen.png" % fn) except: pass
session = Session() query = session.query(Title) for x in query.filter(Title.title.like('%air%')): print(x) if __name__ == '__main__': for file in util.dir2files('/home/luoxing/data/KB'): IdName.addColums(file) import networkx import numpy.linalg import pylab n = 100 m = 500 g = networkx.gnm_random_graph(n, m) l = networkx.generalized_laplacian(g) e = numpy.linalg.eigvals(l) max(e), min(e) pylab.hist(e, bins=10) pylab.xlim(0, 2) pylab.show() import networkx as nx import db s = db.getSession() def makeGraph(session): g = nx.DiGraph() for src, dest in session.query(db.Anchor.source, db.Anchor.destination): g.add_edge(src, dest) return g
try: from pylab import * except: pass from graphml import read_graphml import sys, os if len(sys.argv) == 2: fn = sys.argv[1] print "Reading in %s" % fn g = read_graphml(fn) print "Generating a generalized laplacian" l = nx.generalized_laplacian(g) print "Calculating eigenvalues" e = eigenvalues(l) print("Largest eigenvalue:", max(e)) print("Smallest eigenvalue:", min(e)) # plot with matplotlib if we have it # shows "semicircle" distribution of eigenvalues try: print "Trying to plot semicircle of eigenvalues" hist(e, bins=100) # histogram with 100 bins xlim(0, 2) # eigenvalues between 0 and 2 show() #plt.savefig("%s.eigen.png" % fn) except: pass