python实现求联合信息熵

要求联合信息熵，需要先计算出每个变量的独立信息熵，然后再进行合并得到联合信息熵。具体实现可以参考以下代码：

import math

def entropy(data):
    """
    计算信息熵
    :param data: 数据集
    :return: 信息熵
    """
    n = len(data)
    label_counts = {}
    for feat_vec in data:
        current_label = feat_vec[-1]
        if current_label not in label_counts.keys():
            label_counts[current_label] = 0
        label_counts[current_label] += 1
    entropy = 0.0
    for key in label_counts:
        prob = float(label_counts[key]) / n
        entropy -= prob * math.log2(prob)
    return entropy

def joint_entropy(data):
    """
    计算联合信息熵
    :param data: 数据集
    :return: 联合信息熵
    """
    n = len(data)
    feature_counts = {}
    for feat_vec in data:
        for i in range(len(feat_vec) - 1):
            feat = feat_vec[i]
            label = feat_vec[-1]
            if feat not in feature_counts.keys():
                feature_counts[feat] = {}
            if label not in feature_counts[feat].keys():
                feature_counts[feat][label] = 0
            feature_counts[feat][label] += 1
    joint_entropy = 0.0
    for feat in feature_counts:
        for label in feature_counts[feat]:
            prob = float(feature_counts[feat][label]) / n
            joint_entropy -= prob * math.log2(prob)
    return joint_entropy

其中，entropy函数用于计算独立信息熵，joint_entropy函数用于计算联合信息熵。这两个函数均接收一个数据集作为参数，数据集应该是一个二维列表，每一行代表一个样本，最后一列代表样本所属的类别。