def testExpandIntArray(self):
v = numpy.array([1, 3, 2, 4], numpy.int)
w = Util.expandIntArray(v)
self.assertTrue((w == numpy.array([0,1,1,1,2,2,3,3,3,3], numpy.int)).all())
v = numpy.array([], numpy.int)
w = Util.expandIntArray(v)
self.assertTrue((w == numpy.array([], numpy.int)).all())
def generate(self, graph, requireEmpty=True):
'''
Create an Configuration Model graph. Note the the degree sequence(s) given
in the constructor cannot be guarenteed. The algorithm randomly selects
two free "spokes" and then tried to connect them. If two vertices are
already connected the corresponding spokes are not used again. In the case
that requireEmpty is False then a non-empty graph can be used and the given
degree sequence(s) is(are) the difference(s) in degrees between the output graph and
input one.
:param graph: a graph to populate with edges
:type graph: :class:`apgl.graph.AbstractMatrixGraph`
:param requireEmpty: if this is set to true then we require an empty graph.
:type requireEmpty: :class:`bool`
:returns: The modified input graph.
'''
Parameter.checkClass(graph, AbstractMatrixGraph)
if requireEmpty and graph.getNumEdges() != 0:
raise ValueError("Graph must have no edges")
if graph.getNumVertices() != self.outDegSequence.shape[0]:
raise ValueError(
"Graph must have same number of vertices as degree sequence")
if self.getInDegSequence() != None and graph.isUndirected():
raise ValueError(
"In-degree sequence must be used in conjunction with directed graphs"
)
if self.getInDegSequence() == None:
expandedInds = Util.expandIntArray(self.outDegSequence)
numpy.random.shuffle(expandedInds)
for i in range(0, len(expandedInds), 2):
if i != len(expandedInds) - 1:
graph.addEdge(expandedInds[i], expandedInds[i + 1])
else:
expandedOutInds = Util.expandIntArray(self.outDegSequence)
expandedInInds = Util.expandIntArray(self.inDegSequence)
numpy.random.shuffle(expandedOutInds)
numpy.random.shuffle(expandedInInds)
for i in range(
numpy.min(
numpy.array([
expandedOutInds.shape[0], expandedInInds.shape[0]
]))):
graph.addEdge(expandedOutInds[i], expandedInInds[i])
return graph
def generate(self, graph, requireEmpty=True):
"""
Create an Configuration Model graph. Note the the degree sequence(s) given
in the constructor cannot be guarenteed. The algorithm randomly selects
two free "spokes" and then tried to connect them. If two vertices are
already connected the corresponding spokes are not used again. In the case
that requireEmpty is False then a non-empty graph can be used and the given
degree sequence(s) is(are) the difference(s) in degrees between the output graph and
input one.
:param graph: a graph to populate with edges
:type graph: :class:`apgl.graph.AbstractMatrixGraph`
:param requireEmpty: if this is set to true then we require an empty graph.
:type requireEmpty: :class:`bool`
:returns: The modified input graph.
"""
Parameter.checkClass(graph, AbstractMatrixGraph)
if requireEmpty and graph.getNumEdges() != 0:
raise ValueError("Graph must have no edges")
if graph.getNumVertices() != self.outDegSequence.shape[0]:
raise ValueError("Graph must have same number of vertices as degree sequence")
if self.getInDegSequence() != None and graph.isUndirected():
raise ValueError("In-degree sequence must be used in conjunction with directed graphs")
if self.getInDegSequence() == None:
expandedInds = Util.expandIntArray(self.outDegSequence)
numpy.random.shuffle(expandedInds)
for i in range(0, len(expandedInds), 2):
if i != len(expandedInds) - 1:
graph.addEdge(expandedInds[i], expandedInds[i + 1])
else:
expandedOutInds = Util.expandIntArray(self.outDegSequence)
expandedInInds = Util.expandIntArray(self.inDegSequence)
numpy.random.shuffle(expandedOutInds)
numpy.random.shuffle(expandedInInds)
for i in range(numpy.min(numpy.array([expandedOutInds.shape[0], expandedInInds.shape[0]]))):
graph.addEdge(expandedOutInds[i], expandedInInds[i])
return graph
We simulate a network of sexual contacts and see if we can approximate the degree
using a subset of the edges.
"""
import numpy
from apgl.viroscopy.model.HIVGraph import HIVGraph
from apgl.util.Util import Util
numpy.set_printoptions(linewidth=200, threshold=10000)
numVertices = 500
graph = HIVGraph(numVertices)
T = 100
t = 0
expandedDegreeSeq = Util.expandIntArray(graph.outDegreeSequence())
expandedDegreeSeq = numpy.arange(numVertices)
contactRate = 0.01
tau = 1.0
infectedList = range(10)
#infectedList = range(numVertices)
while (t < T):
contactRatesMatrix = numpy.zeros((len(infectedList), numVertices))
#Select a random contact based on the degree
edsInds = numpy.random.randint(0, expandedDegreeSeq.shape[0], len(infectedList))
#Choose between a contact sampled from those with edges and one from random
