def testEvaluate2(self):
tau = 1.0
linearKernel = LinearKernel()
graphKernel = PermutationGraphKernel(tau, linearKernel)
(evaluation, f, P, SW1, SW2, SK1, SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph3, True)
W1 = self.sGraph1.getWeightMatrix()
W2 = self.sGraph3.getWeightMatrix()
self.assertTrue(numpy.linalg.norm(Util.mdot(P, W1, P.T)-W2) <= self.tol)
self.assertAlmostEquals(f, 0, 7)
def testEvaluate2(self):
tau = 1.0
linearKernel = LinearKernel()
graphKernel = PermutationGraphKernel(tau, linearKernel)
(evaluation, f, P, SW1, SW2, SK1,
SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph3, True)
W1 = self.sGraph1.getWeightMatrix()
W2 = self.sGraph3.getWeightMatrix()
self.assertTrue(
numpy.linalg.norm(Util.mdot(P, W1, P.T) - W2) <= self.tol)
self.assertAlmostEquals(f, 0, 7)
def testEvaluate(self):
tau = 1.0
linearKernel = LinearKernel()
graphKernel = PermutationGraphKernel(tau, linearKernel)
"""
First tests - if the graphs have identical edges then permutation is identity matrix
provided that tau = 1.
"""
(evaluation, f, P, SW1, SW2, SK1,
SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph1, True)
self.assertTrue(
numpy.linalg.norm(P - numpy.eye(self.numVertices)) <= self.tol)
S1, U = numpy.linalg.eigh(self.sGraph1.getWeightMatrix())
S2, U = numpy.linalg.eigh(self.sGraph2.getWeightMatrix())
evaluation2 = numpy.dot(S1, S1)
self.assertTrue(numpy.linalg.norm(SW1 - S1) <= self.tol)
self.assertTrue(numpy.linalg.norm(SW2 - S1) <= self.tol)
self.assertTrue(abs(evaluation - evaluation2) <= self.tol)
(evaluation, f, P, SW1, SW2, SK1,
SK2) = graphKernel.evaluate(self.sGraph2, self.sGraph2, True)
self.assertTrue(
numpy.linalg.norm(P - numpy.eye(self.numVertices)) <= self.tol)
evaluation2 = numpy.dot(S2, S2)
self.assertTrue(numpy.linalg.norm(SW1 - S2) <= self.tol)
self.assertTrue(numpy.linalg.norm(SW2 - S2) <= self.tol)
self.assertTrue(abs(evaluation - evaluation2) <= self.tol)
#Test symmetry
self.assertEquals(graphKernel.evaluate(self.sGraph1, self.sGraph2),
graphKernel.evaluate(self.sGraph2, self.sGraph1))
#Now we choose tau != 1
tau = 0.5
graphKernel = PermutationGraphKernel(tau, linearKernel)
(evaluation, f, P, SW1, SW2, SK1,
SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph1, True)
self.assertTrue(
numpy.linalg.norm(P - numpy.eye(self.numVertices)) <= self.tol)
self.assertTrue(graphKernel.evaluate(self.sGraph1, self.sGraph1) >= 0)
self.assertTrue(graphKernel.evaluate(self.sGraph2, self.sGraph2) >= 0)
self.assertTrue(
(graphKernel.evaluate(self.sGraph1, self.sGraph2) -
graphKernel.evaluate(self.sGraph2, self.sGraph1)) <= self.tol)
(evaluation, f, P, SW1, SW2, SK1,
SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph2, True)
self.assertTrue(
numpy.linalg.norm(numpy.dot(P.T, P) -
numpy.eye(self.numVertices)) <= self.tol)
#Choose tau=0
tau = 0.0
graphKernel = PermutationGraphKernel(tau, linearKernel)
(evaluation, f, P, SW1, SW2, SK1,
SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph1, True)
self.assertTrue(
numpy.linalg.norm(P - numpy.eye(self.numVertices)) <= self.tol)
self.assertTrue(
numpy.linalg.norm(numpy.dot(P.T, P) -
numpy.eye(self.numVertices)) <= self.tol)
X1 = self.sGraph1.getVertexList().getVertices(
list(range(0, (self.sGraph1.getNumVertices()))))
X2 = self.sGraph2.getVertexList().getVertices(
list(range(0, (self.sGraph2.getNumVertices()))))
S1, U = numpy.linalg.eigh(numpy.dot(X1, X1.T))
S2, V = numpy.linalg.eigh(numpy.dot(X2, X2.T))
evaluation2 = numpy.dot(S1, S1)
self.assertTrue(numpy.linalg.norm(SK1 - S1) <= self.tol)
self.assertTrue(numpy.linalg.norm(SK2 - S1) <= self.tol)
self.assertTrue(abs(evaluation - evaluation2) <= self.tol)
self.assertTrue(
(graphKernel.evaluate(self.sGraph1, self.sGraph2) -
graphKernel.evaluate(self.sGraph2, self.sGraph1)) <= self.tol)
def testEvaluate(self):
tau = 1.0
linearKernel = LinearKernel()
graphKernel = PermutationGraphKernel(tau, linearKernel)
"""
First tests - if the graphs have identical edges then permutation is identity matrix
provided that tau = 1.
"""
(evaluation, f, P, SW1, SW2, SK1, SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph1, True)
self.assertTrue(numpy.linalg.norm(P - numpy.eye(self.numVertices)) <= self.tol)
S1, U = numpy.linalg.eigh(self.sGraph1.getWeightMatrix())
S2, U = numpy.linalg.eigh(self.sGraph2.getWeightMatrix())
evaluation2 = numpy.dot(S1, S1)
self.assertTrue(numpy.linalg.norm(SW1 - S1) <= self.tol)
self.assertTrue(numpy.linalg.norm(SW2 - S1) <= self.tol)
self.assertTrue(abs(evaluation - evaluation2) <= self.tol)
(evaluation, f, P, SW1, SW2, SK1, SK2) = graphKernel.evaluate(self.sGraph2, self.sGraph2, True)
self.assertTrue(numpy.linalg.norm(P - numpy.eye(self.numVertices)) <= self.tol)
evaluation2 = numpy.dot(S2, S2)
self.assertTrue(numpy.linalg.norm(SW1 - S2) <= self.tol)
self.assertTrue(numpy.linalg.norm(SW2 - S2) <= self.tol)
self.assertTrue(abs(evaluation - evaluation2) <= self.tol)
#Test symmetry
self.assertEquals(graphKernel.evaluate(self.sGraph1, self.sGraph2), graphKernel.evaluate(self.sGraph2, self.sGraph1))
#Now we choose tau != 1
tau = 0.5
graphKernel = PermutationGraphKernel(tau, linearKernel)
(evaluation, f, P, SW1, SW2, SK1, SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph1, True)
self.assertTrue(numpy.linalg.norm(P - numpy.eye(self.numVertices)) <= self.tol)
self.assertTrue(graphKernel.evaluate(self.sGraph1, self.sGraph1) >= 0)
self.assertTrue(graphKernel.evaluate(self.sGraph2, self.sGraph2) >= 0)
self.assertTrue((graphKernel.evaluate(self.sGraph1, self.sGraph2)- graphKernel.evaluate(self.sGraph2, self.sGraph1)) <= self.tol)
(evaluation, f, P, SW1, SW2, SK1, SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph2, True)
self.assertTrue(numpy.linalg.norm(numpy.dot(P.T, P) - numpy.eye(self.numVertices)) <= self.tol)
#Choose tau=0
tau = 0.0
graphKernel = PermutationGraphKernel(tau, linearKernel)
(evaluation, f, P, SW1, SW2, SK1, SK2) = graphKernel.evaluate(self.sGraph1, self.sGraph1, True)
self.assertTrue(numpy.linalg.norm(P - numpy.eye(self.numVertices)) <= self.tol)
self.assertTrue(numpy.linalg.norm(numpy.dot(P.T, P) - numpy.eye(self.numVertices)) <= self.tol)
X1 = self.sGraph1.getVertexList().getVertices(list(range(0, (self.sGraph1.getNumVertices()))))
X2 = self.sGraph2.getVertexList().getVertices(list(range(0, (self.sGraph2.getNumVertices()))))
S1, U = numpy.linalg.eigh(numpy.dot(X1, X1.T))
S2, V = numpy.linalg.eigh(numpy.dot(X2, X2.T))
evaluation2 = numpy.dot(S1, S1)
self.assertTrue(numpy.linalg.norm(SK1 - S1) <= self.tol)
self.assertTrue(numpy.linalg.norm(SK2 - S1) <= self.tol)
self.assertTrue(abs(evaluation - evaluation2) <= self.tol)
self.assertTrue((graphKernel.evaluate(self.sGraph1, self.sGraph2)- graphKernel.evaluate(self.sGraph2, self.sGraph1)) <= self.tol)