def test_classify(self):
my_data, labels = trees.createDataSet()
my_tree = treePlotter.retrieveTree(0)
print(trees.classify(my_tree, labels, [1, 0]))
print(trees.classify(my_tree, labels, [1, 1]))
def test_createDataSet(self):
# 获得数据集和标签
my_data, labels = trees.createDataSet()
print(my_data)
# 计算数据集的信息熵
print(trees.calcShannonEnt(my_data))
def test_createTree(self):
my_data, labels = trees.createDataSet()
my_tree = trees.createTree(my_data, labels)
print(my_tree)
def test_chooseBestFeatureToSplit(self):
my_data, labels = trees.createDataSet()
print(trees.chooseBestFeatureToSplit(my_data))
print(my_data)
def test_splitDataSet(self):
my_data, labels = trees.createDataSet()
print(my_data)
print(trees.splitDataSet(my_data, 0, 1))
print(trees.splitDataSet(my_data, 0, 0))
def test_calcShannonEnt(self):
my_data, labels = trees.createDataSet()
# 增加一个新的分类 'maybe' ，观察信息熵的变化
my_data[0][-1] = 'maybe'
print(trees.calcShannonEnt(my_data))