def testSetWeight(self):
#Try weight = 0 and weight = 1
decisionTree = DecisionTree()
decisionTree.setWeight(0.0)
decisionTree.learnModel(self.X, self.y)
predY = decisionTree.predict(self.X)
self.assertTrue((predY == numpy.zeros(predY.shape[0])).all())
decisionTree.setWeight(1.0)
decisionTree.learnModel(self.X, self.y)
predY = decisionTree.predict(self.X)
self.assertTrue((predY == numpy.ones(predY.shape[0])).all())
def testPredict(self):
decisionTree = DecisionTree()
decisionTree.learnModel(self.X, self.y)
predY = decisionTree.predict(self.X)
inds = numpy.random.permutation(self.X.shape[0])
predY2 = decisionTree.predict(self.X[inds, :])
self.assertTrue((predY[inds] == predY2).all())
#Let's test on -1, +1 labels
y2 = (self.y*2)-1
decisionTree.learnModel(self.X, y2)
predY2 = decisionTree.predict(self.X)
self.assertTrue((predY2 == predY*2-1).all())
