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)
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)
