决策树C4.5的原代码

决策树C4.5是一种经典的机器学习算法，用于分类和回归。它基于信息增益来选择最优的分割属性，并采用剪枝技术防止过拟合。C4.5算法的具体实现涉及到大量的数据结构和算法，因此其原代码比较复杂。

如果您想了解C4.5算法的原理和实现，可以参考Ross Quinlan所著的《C4.5: Programs for Machine Learning》一书，其中包含了C4.5算法的详细介绍和实现代码。

不过，建议您先掌握决策树C4.5的基本原理和使用方法，再深入了解其原代码实现。同时，也可以利用现有的开源机器学习库如scikit-learn等来应用C4.5算法进行分类和回归任务。

相关问题

决策树c4.5 pyhton代码

决策树是一种常见的机器学习算法，C4.5是其中一种经典的算法。它通过对数据进行分析，构建出一棵决策树，从而实现对新数据的分类预测。以下是C4.5算法的Python实现代码：

import pandas as pd
import numpy as np
import math

class Node:
    def __init__(self, feature=None, label=None):
        self.feature = feature
        self.label = label
        self.children = {}

class DecisionTree:
    def __init__(self, epsilon=0.1):
        self.epsilon = epsilon
        self.tree = None

    def entropy(self, p):
        if p != 0:
            return -p * math.log(p, 2)
        else:
            return 0

    def calc_ent(self, data):
        n = len(data)
        if n == 0:
            return 0
        count = {}
        for i in range(n):
            label = data[i][-1]
            if label not in count.keys():
                count[label] = 0
            count[label] += 1
        ent = 0
        for key in count:
            p = count[key] / n
            ent += self.entropy(p)
        return ent

    def calc_condition_ent(self, data, axis=0):
        n = len(data)
        if n == 0:
            return 0
        feature_set = {}
        for i in range(n):
            feature = data[i][axis]
            if feature not in feature_set.keys():
                feature_set[feature] = []
            feature_set[feature].append(data[i])
        ent = 0
        for key in feature_set:
            p = len(feature_set[key]) / n
            ent += p * self.calc_ent(feature_set[key])
        return ent

    def calc_info_gain(self, ent, condition_ent):
        return ent - condition_ent

    def calc_info_gain_ratio(self, ent, condition_ent):
        if condition_ent == 0:
            return 0
        return (ent - condition_ent) / condition_ent

    def choose_best_feature(self, data):
        num_features = len(data) - 1
        best_feature = -1
        best_info_gain_ratio = 0
        ent = self.calc_ent(data)
        for i in range(num_features):
            condition_ent = self.calc_condition_ent(data, i)
            info_gain_ratio = self.calc_info_gain_ratio(ent, condition_ent)
            if info_gain_ratio > best_info_gain_ratio:
                best_info_gain_ratio = info_gain_ratio
                best_feature = i
        return best_feature

    def majority_cnt(self, label_list):
        label_count = {}
        for i in range(len(label_list)):
            label = label_list[i]
            if label not in label_count.keys():
                label_count[label] = 0
            label_count[label] += 1
        sorted_label_count = sorted(label_count.items(), key=lambda x: x[1], reverse=True)
        return sorted_label_count

    def create_tree(self, data, labels):
        class_list = [example[-1] for example in data]
        if class_list.count(class_list) == len(class_list):
            return Node(label=class_list)
        if len(data) == 1:
            return Node(label=self.majority_cnt(class_list))
        best_feature = self.choose_best_feature(data)
        best_feature_label = labels[best_feature]
        tree = Node(feature=best_feature_label)
        del(labels[best_feature])
        feature_values = [example[best_feature] for example in data]
        unique_feature_values = set(feature_values)
        for value in unique_feature_values:
            sub_labels = labels[:]
            sub_data = []
            for i in range(len(data)):
                if data[i][best_feature] == value:
                    sub_data.append(data[i][:best_feature] + data[i][best_feature+1:])
            sub_tree = self.create_tree(sub_data, sub_labels)
            tree.children[value] = sub_tree
        return tree

    def fit(self, X_train, y_train):
        data_df = pd.concat([X_train, y_train], axis=1)
        data = np.array(data_df)
        labels = list(X_train.columns) + ['label']
        self.tree = self.create_tree(data, labels)

    def predict(self, X_test):
        res = []
        for _, row in X_test.iterrows():
            input_data = list(row)
            cur_node = self.tree
            while cur_node.children:
                feature_val = input_data[cur_node.feature]
                if feature_val in cur_node.children:
                    cur_node = cur_node.children[feature_val]
                else:
                    break
            res.append(cur_node.label)
        return res

决策树C4.5python代码

根据提供的引用[1]，可以得到C4.5决策树算法的Python代码实现。以下是一个简单的示例：

from math import log
import operator

def calcShannonEnt(dataSet):
    numEntries = len(dataSet)
    labelCounts = {}
    for featVec in dataSet:
        currentLabel = featVec[-1]
        if currentLabel not in labelCounts.keys():
            labelCounts[currentLabel] = 0
        labelCounts[currentLabel] += 1
    shannonEnt = 0.0
    for key in labelCounts:
        prob = float(labelCounts[key])/numEntries
        shannonEnt -= prob * log(prob, 2)
    return shannonEnt

def createDataSet():
    dataSet = [[1, 1, 'yes'],
               [1, 1, 'yes'],
               [1, 0, 'no'],
               [0, 1, 'no'],
               [0, 1, 'no']]
    labels = ['no surfacing','flippers']
    return dataSet, labels

def splitDataSet(dataSet, axis, value):
    retDataSet = []
    for featVec in dataSet:
        if featVec[axis] == value:
            reducedFeatVec = featVec[:axis]
            reducedFeatVec.extend(featVec[axis+1:])
            retDataSet.append(reducedFeatVec)
    return retDataSet

def chooseBestFeatureToSplit(dataSet):
    numFeatures = len(dataSet[0]) - 1
    baseEntropy = calcShannonEnt(dataSet)
    bestInfoGain = 0.0
    bestFeature = -1
    for i in range(numFeatures):
        featList = [example[i] for example in dataSet]
        uniqueVals = set(featList)
        newEntropy = 0.0
        for value in uniqueVals:
            subDataSet = splitDataSet(dataSet, i, value)
            prob = len(subDataSet)/float(len(dataSet))
            newEntropy += prob * calcShannonEnt(subDataSet)
        infoGain = baseEntropy - newEntropy
        if (infoGain > bestInfoGain):
            bestInfoGain = infoGain
            bestFeature = i
    return bestFeature

def majorityCnt(classList):
    classCount = {}
    for vote in classList:
        if vote not in classCount.keys():
            classCount[vote] = 0
        classCount[vote] += 1
    sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

def createTree(dataSet, labels):
    classList = [example[-1] for example in dataSet]
    if classList.count(classList[0]) == len(classList):
        return classList[0]
    if len(dataSet[0]) == 1:
        return majorityCnt(classList)
    bestFeat = chooseBestFeatureToSplit(dataSet)
    bestFeatLabel = labels[bestFeat]
    myTree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)
    return myTree

myDat,labels=createDataSet()
myTree=createTree(myDat,labels)
print(myTree)

该示例代码实现了C4.5决策树算法的主要步骤，包括计算信息熵、选择最优特征、创建决策树等。你可以根据自己的需求进行修改和扩展。