def testSetTau(self):
tau = 0.0
tol = 10 - 6
kernel = LinearKernel()
predictor = PrimalDualCCARegression(kernel, tau, tau)
self.assertEquals(predictor.getTau1(), tau)
predictor.setTau1(0.1)
self.assertEquals(predictor.getTau1(), 0.1)
def testPredict2(self):
# Test predicting on low-rank matrices
numExamples = 10
numFeatures = 5
X = numpy.random.rand(numExamples, numFeatures)
Y = numpy.random.rand(numExamples, numFeatures)
tau = 0.0
tol = 10 - 6
kernel = LinearKernel()
predictor = PrimalDualCCARegression(kernel, tau, tau)
A = predictor.learnModel(X, Y)
predY = predictor.predict(X)
def testPredict2(self):
#Test predicting on low-rank matrices
numExamples = 10
numFeatures = 5
X = numpy.random.rand(numExamples, numFeatures)
Y = numpy.random.rand(numExamples, numFeatures)
tau = 0.0
tol = 10 - 6
kernel = LinearKernel()
predictor = PrimalDualCCARegression(kernel, tau, tau)
A = predictor.learnModel(X, Y)
predY = predictor.predict(X)
def testPredict(self):
numExamples = 10
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
Y = X
tau = 0.0
tol = 10 - 6
kernel = LinearKernel()
predictor = PrimalDualCCARegression(kernel, tau, tau)
A = predictor.learnModel(X, Y)
testX = X[0:5, :]
predY = predictor.predict(testX)
self.assertTrue(numpy.linalg.norm(predY - Y[0:5, :]) < tol)
def testLearnModel(self):
numExamples = 10
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
Y = X
tau = 0.0
tol = 10 - 6
kernel = LinearKernel()
predictor = PrimalDualCCARegression(kernel, tau, tau)
A = predictor.learnModel(X, Y)
self.assertTrue(numpy.linalg.norm(numpy.dot(numpy.dot(X, X.T), A) - Y) < tol)
self.assertTrue(numpy.linalg.norm(predictor.predict(X) - Y) < tol)
def testPredict(self):
numExamples = 10
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
Y = X
tau = 0.0
tol = 10 - 6
kernel = LinearKernel()
predictor = PrimalDualCCARegression(kernel, tau, tau)
A = predictor.learnModel(X, Y)
testX = X[0:5, :]
predY = predictor.predict(testX)
self.assertTrue(numpy.linalg.norm(predY - Y[0:5, :]) < tol)
def testLearnModel(self):
numExamples = 10
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
Y = X
tau = 0.0
tol = 10 - 6
kernel = LinearKernel()
predictor = PrimalDualCCARegression(kernel, tau, tau)
A = predictor.learnModel(X, Y)
self.assertTrue(
numpy.linalg.norm(numpy.dot(numpy.dot(X, X.T), A) - Y) < tol)
self.assertTrue(numpy.linalg.norm(predictor.predict(X) - Y) < tol)
def testSetTau(self):
tau = 0.0
tol = 10 - 6
kernel = LinearKernel()
predictor = PrimalDualCCARegression(kernel, tau, tau)
self.assertEquals(predictor.getTau1(), tau)
predictor.setTau1(0.1)
self.assertEquals(predictor.getTau1(), 0.1)