def testParallelPenaltyGridRbf(self):
svm = self.svm
svm.setKernel("gaussian")
trainX = self.X[0:40, :]
trainY = self.y[0:40]
idealPenalties = svm.parallelPenaltyGridRbf(trainX, trainY, self.X,
self.y)
idealPenalties2 = numpy.zeros((svm.Cs.shape[0], svm.gammas.shape[0]))
idealPenalties3 = numpy.zeros((svm.Cs.shape[0], svm.gammas.shape[0]))
for i in range(svm.Cs.shape[0]):
C = svm.Cs[i]
for j in range(svm.gammas.shape[0]):
gamma = svm.gammas[j]
svm.setGamma(gamma)
svm.setC(C)
svm.learnModel(trainX, trainY)
predY = svm.predict(self.X)
predTrainY = svm.predict(trainX)
penalty = Evaluator.binaryError(
predY, self.y) - Evaluator.binaryError(predTrainY, trainY)
idealPenalties2[i, j] = penalty
args = (trainX, trainY, self.X, self.y, svm)
idealPenalties3[i, j] = computeIdealPenalty(args)
tol = 10**-6
self.assertTrue(
numpy.linalg.norm(idealPenalties2.T - idealPenalties) < tol)
def testParallelPenaltyGrid(self):
svm = self.svm
svm.setKernel("gaussian")
trainX = self.X[0:40, :]
trainY = self.y[0:40]
paramDict = {}
paramDict["setC"] = svm.getCs()
paramDict["setGamma"] = svm.getGammas()
idealPenalties = svm.parallelPenaltyGrid(trainX, trainY, self.X, self.y, paramDict)
idealPenalties2 = numpy.zeros((svm.Cs.shape[0], svm.gammas.shape[0]))
idealPenalties3 = numpy.zeros((svm.Cs.shape[0], svm.gammas.shape[0]))
for i in range(svm.Cs.shape[0]):
C = svm.Cs[i]
for j in range(svm.gammas.shape[0]):
gamma = svm.gammas[j]
svm.setGamma(gamma)
svm.setC(C)
svm.learnModel(trainX, trainY)
predY = svm.predict(self.X)
predTrainY = svm.predict(trainX)
penalty = Evaluator.binaryError(predY, self.y) - Evaluator.binaryError(predTrainY, trainY)
idealPenalties2[i, j] = penalty
args = (trainX, trainY, self.X, self.y, svm)
idealPenalties3[i, j] = computeIdealPenalty(args)
tol = 10**-6
self.assertTrue(numpy.linalg.norm(idealPenalties2.T - idealPenalties) < tol)
def testComputeIdealPenalty(self):
C = 10.0
gamma = 0.5
svm = LibSVM("gaussian", gamma, C)
args = (self.X, self.y, self.X, self.y, svm)
error = computeIdealPenalty(args)
def testComputeIdealPenalty(self):
C = 10.0
gamma = 0.5
svm = LibSVM("gaussian", gamma, C)
args = (self.X, self.y, self.X, self.y, svm)
error = computeIdealPenalty(args)