def testGenerate(self): generate = LinearSvmFGs.generate() self.X[:, 15:25] = self.X[:, 15:25]*100 svc = generate() svc.setWaveletInds(numpy.arange(100)) svc.learnModel(self.X, self.y) self.assertEquals(numpy.intersect1d(numpy.arange(15,25), svc.getFeatureInds()).shape[0], 10) predY = svc.predict(self.X) #Now test when all features are wavelets svc = generate() svc.learnModel(self.X, self.y) self.assertEquals(numpy.intersect1d(numpy.arange(15,25), svc.getFeatureInds()).shape[0], 10) predY = svc.predict(self.X)
def testGenerate(self): generate = LinearSvmFGs.generate() self.X[:, 15:25] = self.X[:, 15:25] * 100 svc = generate() svc.setWaveletInds(numpy.arange(100)) svc.learnModel(self.X, self.y) self.assertEquals( numpy.intersect1d(numpy.arange(15, 25), svc.getFeatureInds()).shape[0], 10) predY = svc.predict(self.X) #Now test when all features are wavelets svc = generate() svc.learnModel(self.X, self.y) self.assertEquals( numpy.intersect1d(numpy.arange(15, 25), svc.getFeatureInds()).shape[0], 10) predY = svc.predict(self.X)
def testSetWeight(self): learner = LinearSvmFGs() learner.setWeight(0.8)