def testProject(self):
numExamples = 5
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
Y = X*2
tau = 0.0
kernel = LinearKernel()
tol = 10**--6
k = 5
cca = KernelCCA(kernel, kernel, tau)
alpha, beta, lmbdas = cca.learnModel(X, Y)
XU, YU = cca.project(X, Y, k)
self.assertTrue(numpy.linalg.norm(XU-YU) < tol)
self.assertTrue(numpy.linalg.norm(numpy.dot(XU.T, XU) - numpy.ones(k)) < tol)
self.assertTrue(numpy.linalg.norm(numpy.dot(YU.T, YU) - numpy.ones(k)) < tol)
def testLearnModel(self):
numExamples = 5
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
Y = X
tau = 0.0
kernel = LinearKernel()
tol = 10**--6
cca = KernelCCA(kernel, kernel, tau)
alpha, beta, lmbdas = cca.learnModel(X, Y)
self.assertTrue(numpy.linalg.norm(alpha-beta) < tol)
self.assertTrue(numpy.linalg.norm(lmbdas-numpy.ones(numExamples)) < tol)
Y = X*2
cca = KernelCCA(kernel, kernel, tau)
alpha, beta, lmbdas = cca.learnModel(X, Y)
self.assertTrue(numpy.linalg.norm(alpha-beta) < tol)
self.assertTrue(numpy.linalg.norm(lmbdas-numpy.ones(numExamples)) < tol)
#Test case with tau = 1 is the same as 1st KPLS direction
Y = numpy.random.rand(numExamples, numFeatures)
tau = 1.0
cca = KernelCCA(kernel, kernel, tau)
alpha, beta, lmbdas = cca.learnModel(X, Y)
Kx = kernel.evaluate(X, X)
Ky = kernel.evaluate(Y, Y)
lmbdas2, alpha2 = scipy.linalg.eig(numpy.dot(Ky, Kx))
alpha = alpha[:, numpy.argmax(lmbdas)]/numpy.linalg.norm(alpha[:, numpy.argmax(lmbdas)])
alpha2 = alpha2[:, numpy.argmax(lmbdas2)]/numpy.linalg.norm(alpha2[:, numpy.argmax(lmbdas2)])
self.assertTrue(numpy.linalg.norm(alpha-alpha2) < tol)
def testProject(self):
#Test if it is the same as KCCA
numExamples = 50
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
Y = numpy.random.rand(numExamples, numFeatures)
tau = 0.0
tol = 10**--6
k = 5
cca = PrimalCCA(tau)
u, v, lmbdas = cca.learnModel(X, Y)
XU, YU = cca.project(X, Y, k)
kernel = LinearKernel()
kcca = KernelCCA(kernel, kernel, tau)
alpha, beta, lmbdas2 = kcca.learnModel(X, Y)
XU2, YU2 = kcca.project(X, Y, k)
#Seem to get an error in this for some reason
#self.assertTrue(numpy.linalg.norm(XU-XU2) < tol)
#self.assertTrue(numpy.linalg.norm(YU-YU2) < tol)
#Now try with different tau
tau = 0.5
cca = PrimalCCA(tau)
u, v, lmbdas = cca.learnModel(X, Y)
XU, YU = cca.project(X, Y, k)
kernel = LinearKernel()
kcca = KernelCCA(kernel, kernel, tau)
alpha, beta, lmbdas = kcca.learnModel(X, Y)
XU2, YU2 = kcca.project(X, Y, k)
self.assertTrue(numpy.linalg.norm(XU - XU2) < tol)
self.assertTrue(numpy.linalg.norm(YU - YU2) < tol)
self.assertTrue(
numpy.linalg.norm(numpy.dot(XU.T, XU) - numpy.ones(k)) < tol)
self.assertTrue(
numpy.linalg.norm(numpy.dot(YU.T, YU) - numpy.ones(k)) < tol)
def testProject(self):
#Test if it is the same as KCCA
numExamples = 50
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
Y = numpy.random.rand(numExamples, numFeatures)
tau = 0.0
tol = 10**--6
k = 5
cca = PrimalCCA(tau)
u, v, lmbdas = cca.learnModel(X, Y)
XU, YU = cca.project(X, Y, k)
kernel = LinearKernel()
kcca = KernelCCA(kernel, kernel, tau)
alpha, beta, lmbdas2 = kcca.learnModel(X, Y)
XU2, YU2 = kcca.project(X, Y, k)
#Seem to get an error in this for some reason
#self.assertTrue(numpy.linalg.norm(XU-XU2) < tol)
#self.assertTrue(numpy.linalg.norm(YU-YU2) < tol)
#Now try with different tau
tau = 0.5
cca = PrimalCCA(tau)
u, v, lmbdas = cca.learnModel(X, Y)
XU, YU = cca.project(X, Y, k)
kernel = LinearKernel()
kcca = KernelCCA(kernel, kernel, tau)
alpha, beta, lmbdas = kcca.learnModel(X, Y)
XU2, YU2 = kcca.project(X, Y, k)
self.assertTrue(numpy.linalg.norm(XU-XU2) < tol)
self.assertTrue(numpy.linalg.norm(YU-YU2) < tol)
self.assertTrue(numpy.linalg.norm(numpy.dot(XU.T, XU) - numpy.ones(k)) < tol)
self.assertTrue(numpy.linalg.norm(numpy.dot(YU.T, YU) - numpy.ones(k)) < tol)
