def generateGraph2(self, alpha, dim):
numVertices = self.graph.getNumVertices()
self.X = numpy.random.rand(numVertices, dim)
D = KernelUtils.computeDistanceMatrix(numpy.dot(self.X, self.X.T))
#P = beta**(-alpha * D)
"""
Normalise the distance matrix so that the max distance (corner to corner)
is 1. The value of k is the cumulative probability of an edge for any node
"""
P = (D / math.sqrt(dim)) ** -alpha
diagIndices = numpy.array(list(range(0, numVertices)))
P[(diagIndices, diagIndices)] = numpy.zeros(numVertices)
P = P / numpy.array([numpy.sum(P, 1)]).T
B = numpy.random.rand(numVertices, numVertices) <= P
#Note that B is symmetric - could just go through e.g. upper triangle
for i in range(numpy.nonzero(B)[0].shape[0]):
v1 = numpy.nonzero(B)[0][i]
v2 = numpy.nonzero(B)[1][i]
self.graph.addEdge(v1, v2)
return self.graph
def generateGraph(self, alpha, p, dim):
Parameter.checkFloat(alpha, 0.0, float('inf'))
Parameter.checkFloat(p, 0.0, 1.0)
Parameter.checkInt(dim, 0, float('inf'))
numVertices = self.graph.getNumVertices()
self.X = numpy.random.rand(numVertices, dim)
D = KernelUtils.computeDistanceMatrix(numpy.dot(self.X, self.X.T))
P = numpy.exp(-alpha * D)
diagIndices = numpy.array(list(range(0, numVertices)))
P[(diagIndices, diagIndices)] = numpy.zeros(numVertices)
B = numpy.random.rand(numVertices, numVertices) <= P
#Note that B is symmetric - could just go through e.g. upper triangle
for i in range(numpy.nonzero(B)[0].shape[0]):
v1 = numpy.nonzero(B)[0][i]
v2 = numpy.nonzero(B)[1][i]
self.graph.addEdge(v1, v2)
erdosRenyiGenerator = ErdosRenyiGenerator(p)
self.graph = erdosRenyiGenerator.generate(self.graph, False)
return self.graph
def generateGraph2(self, alpha, dim):
numVertices = self.graph.getNumVertices()
self.X = numpy.random.rand(numVertices, dim)
D = KernelUtils.computeDistanceMatrix(numpy.dot(self.X, self.X.T))
#P = beta**(-alpha * D)
"""
Normalise the distance matrix so that the max distance (corner to corner)
is 1. The value of k is the cumulative probability of an edge for any node
"""
P = (D / math.sqrt(dim))**-alpha
diagIndices = numpy.array(list(range(0, numVertices)))
P[(diagIndices, diagIndices)] = numpy.zeros(numVertices)
P = P / numpy.array([numpy.sum(P, 1)]).T
B = numpy.random.rand(numVertices, numVertices) <= P
#Note that B is symmetric - could just go through e.g. upper triangle
for i in range(numpy.nonzero(B)[0].shape[0]):
v1 = numpy.nonzero(B)[0][i]
v2 = numpy.nonzero(B)[1][i]
self.graph.addEdge(v1, v2)
return self.graph
def generateGraph(self, alpha, p, dim):
Parameter.checkFloat(alpha, 0.0, float('inf'))
Parameter.checkFloat(p, 0.0, 1.0)
Parameter.checkInt(dim, 0, float('inf'))
numVertices = self.graph.getNumVertices()
self.X = numpy.random.rand(numVertices, dim)
D = KernelUtils.computeDistanceMatrix(numpy.dot(self.X, self.X.T))
P = numpy.exp(-alpha * D)
diagIndices = numpy.array(list(range(0, numVertices)))
P[(diagIndices, diagIndices)] = numpy.zeros(numVertices)
B = numpy.random.rand(numVertices, numVertices) <= P
#Note that B is symmetric - could just go through e.g. upper triangle
for i in range(numpy.nonzero(B)[0].shape[0]):
v1 = numpy.nonzero(B)[0][i]
v2 = numpy.nonzero(B)[1][i]
self.graph.addEdge(v1, v2)
erdosRenyiGenerator = ErdosRenyiGenerator(p)
self.graph = erdosRenyiGenerator.generate(self.graph, False)
return self.graph
def testComputeDistanceMatrix(self):
numExamples = 100
numFeatures = 10
numRepetitions = 10
for i in range(numRepetitions):
X = numpy.random.randn(numExamples, numFeatures)
K = numpy.dot(X, X.T)
D1 = KernelUtils.computeDistanceMatrix(K)
D2 = numpy.zeros((numExamples, numExamples))
for i in range(0, numExamples):
for j in range(0, numExamples):
D2[i, j] = numpy.linalg.norm(X[i, :] - X[j, :])
self.assertTrue(numpy.linalg.norm(D1 - D2) < 10**-6)
def testComputeDistanceMatrix(self):
numExamples = 100
numFeatures = 10
numRepetitions = 10
for i in range(numRepetitions):
X = numpy.random.randn(numExamples, numFeatures)
K = numpy.dot(X, X.T)
D1 = KernelUtils.computeDistanceMatrix(K)
D2 = numpy.zeros((numExamples, numExamples))
for i in range(0, numExamples):
for j in range(0, numExamples):
D2[i, j] = numpy.linalg.norm(X[i, :] - X[j, :])
self.assertTrue(numpy.linalg.norm(D1 - D2) < 10**-6)
