def testLearnModel2(self):
numExamples = 200
numFeatures = 100
X = numpy.random.randn(numExamples, numFeatures)
y = numpy.random.randn(numExamples)
preprocessor = Standardiser()
X = preprocessor.standardiseArray(X)
tol = 10**-3
kernel = LinearKernel()
#Try using a low-rank matrix
lmbda = 0.001
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
logging.debug((numpy.linalg.norm(y)))
logging.debug((numpy.linalg.norm(predY - y)))
def testLearnModel2(self):
numExamples = 200
numFeatures = 100
X = numpy.random.randn(numExamples, numFeatures)
y = numpy.random.randn(numExamples)
preprocessor = Standardiser()
X = preprocessor.standardiseArray(X)
tol = 10**-3
kernel = LinearKernel()
#Try using a low-rank matrix
lmbda = 0.001
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
logging.debug((numpy.linalg.norm(y)))
logging.debug((numpy.linalg.norm(predY - y)))
def testLearnModel(self):
numExamples = 50
numFeatures = 200
preprocessor = Standardiser()
X = numpy.random.randn(numExamples, numFeatures)
X = preprocessor.standardiseArray(X)
c = numpy.random.rand(numFeatures)
y = numpy.dot(X, c)
tol = 0.05
kernel = LinearKernel()
lmbda = 0.0001
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
self.assertTrue(Evaluator.rootMeanSqError(y, predY) < tol)
#Try increasing y
y = y + 5
lmbda = 0.2
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
self.assertTrue(numpy.abs(b - 5) < 0.1)
self.assertTrue(Evaluator.rootMeanSqError(y, predY) < 0.1)
#Try making prediction for multilabel Y
C = numpy.random.rand(numFeatures, numFeatures)
Y = numpy.dot(X, C)
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, Y)
predY = predictor.predict(X)
self.assertTrue(Evaluator.rootMeanSqError(Y, predY) < 0.1)
#Now, shift the data
s = numpy.random.rand(numFeatures)
Y = Y + s
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, Y)
predY = predictor.predict(X)
self.assertTrue(numpy.linalg.norm(b - s) < 0.1)
self.assertTrue(Evaluator.rootMeanSqError(Y, predY) < 0.1)
def testLearnModel(self):
numExamples = 50
numFeatures = 200
preprocessor = Standardiser()
X = numpy.random.randn(numExamples, numFeatures)
X = preprocessor.standardiseArray(X)
c = numpy.random.rand(numFeatures)
y = numpy.dot(X, c)
tol = 0.05
kernel = LinearKernel()
lmbda = 0.0001
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
self.assertTrue(Evaluator.rootMeanSqError(y, predY) < tol)
#Try increasing y
y = y + 5
lmbda = 0.2
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
self.assertTrue(numpy.abs(b - 5) < 0.1)
self.assertTrue(Evaluator.rootMeanSqError(y, predY) < 0.1)
#Try making prediction for multilabel Y
C = numpy.random.rand(numFeatures, numFeatures)
Y = numpy.dot(X, C)
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, Y)
predY = predictor.predict(X)
self.assertTrue(Evaluator.rootMeanSqError(Y, predY) < 0.1)
#Now, shift the data
s = numpy.random.rand(numFeatures)
Y = Y + s
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, Y)
predY = predictor.predict(X)
self.assertTrue(numpy.linalg.norm(b - s) < 0.1)
self.assertTrue(Evaluator.rootMeanSqError(Y, predY) < 0.1)