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