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