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
