def testSetEpsilon(self): """ Test out the parameter for the regressive SVM, vary epsilon and look at number of support vectors. """ try: import sklearn except ImportError as error: return svm = LibSVM() svm.setC(10.0) svm.setEpsilon(0.1) svm.setSvmType("Epsilon_SVR") numExamples = 100 numFeatures = 10 X = numpy.random.randn(numExamples, numFeatures) c = numpy.random.randn(numFeatures) y = numpy.dot(X, numpy.array([c]).T).ravel() + numpy.random.randn(100) svm.setEpsilon(1.0) svm.learnModel(X, y) numSV = svm.getModel().support_.shape svm.setEpsilon(0.5) svm.learnModel(X, y) numSV2 = svm.getModel().support_.shape svm.setEpsilon(0.01) svm.learnModel(X, y) numSV3 = svm.getModel().support_.shape #There should be fewer SVs as epsilon increases self.assertTrue(numSV < numSV2) self.assertTrue(numSV2 < numSV3)