hw1_dt.py

import numpy as np
import utils as Util


class DecisionTree():
    def __init__(self):
        self.clf_name = "DecisionTree"
        self.root_node = None

    # TODO: train Decision Tree
    def train(self, features, labels):
        # features: List[List[float]], labels: List[int]
        # init

    # TODO: predic function
    def predict(self, features):
        # features: List[List[any]]
        # return List[int]


class TreeNode(object):
    def __init__(self, features, labels, num_cls):
        # features: List[List[any]], labels: List[int], num_cls: int
        self.features = features
        self.labels = labels
        self.children = []
        self.num_cls = num_cls
        # find the most common labels in current node
        count_max = 0
        for label in np.unique(labels):
            if self.labels.count(label) > count_max:
                count_max = labels.count(label)
                self.cls_max = label
        # splitable is false when all features belongs to one class
        if len(np.unique(labels)) < 2:
            self.splittable = False
        else:
            self.splittable = True

        self.dim_split = None  # the index of the feature to be split

        self.feature_uniq_split = None  # the possible unique values of the feature to be split

    # TODO: implement split function
    def split(self):

    # TODO:treeNode predict function
    def predict(self, feature):