归纳决策树ID3（Java实现）

ID3就不介绍了，最终的决策树保存在了XML中，使用了Dom4J，注意如果要让Dom4J支持按XPath选择节点，还得引入包jaxen.jar。程序代码要求输入文件满足ARFF格式，并且属性都是标称变量。

001	package dt;

002

003	import java.io.BufferedReader;

004	import java.io.File;

005	import java.io.FileReader;

006	import java.io.FileWriter;

007	import java.io.IOException;

008	import java.util.ArrayList;

009	import java.util.Iterator;

010	import java.util.LinkedList;

011	import java.util.List;

012	import java.util.regex.Matcher;

013	import java.util.regex.Pattern;

014

015	import org.dom4j.Document;

016	import org.dom4j.DocumentHelper;

017	import org.dom4j.Element;

018	import org.dom4j.io.OutputFormat;

019	import org.dom4j.io.XMLWriter;

020

021	public class ID3 {

022	private ArrayList<String> attribute = new ArrayList<String>(); // 存储属性的名称

023	private ArrayList<ArrayList<String>> attributevalue = new ArrayList<ArrayList<String>>(); // 存储每个属性的取值

024	private ArrayList<String[]> data = new ArrayList<String[]>();; // 原始数据

025	int decatt; // 决策变量在属性集中的索引

026	public static final String patternString = "@attribute(.*)[{](.*?)[}]";

027

028	Document xmldoc;

029	Element root;

030

031	public ID3() {

032	xmldoc = DocumentHelper.createDocument();

033	root = xmldoc.addElement("root");

034	root.addElement("DecisionTree").addAttribute("value", "null");

035

}

036

037	public static void main(String[] args) {

038	ID3 inst = new ID3();

039	inst.readARFF(new File("/home/orisun/test/weather.nominal.arff"));

040	inst.setDec("play");

041	LinkedList<Integer> ll=new LinkedList<Integer>();

042	for(int i=0;i<inst.attribute.size();i++){

043	if(i!=inst.decatt)

044	ll.add(i);

045

}

046	ArrayList<Integer> al=new ArrayList<Integer>();

047	for(int i=0;i<inst.data.size();i++){

048	al.add(i);

049

}

050	inst.buildDT("DecisionTree", "null", al, ll);

051	inst.writeXML("/home/orisun/test/dt.xml");

052

return;

053

}

054

055	//读取arff文件，给attribute、attributevalue、data赋值

056	public void readARFF(File file) {

057

try {

058	FileReader fr = new FileReader(file);

059	BufferedReader br = new BufferedReader(fr);

060	String line;

061	Pattern pattern = Pattern.compile(patternString);

062	while ((line = br.readLine()) != null) {

063	Matcher matcher = pattern.matcher(line);

064	if (matcher.find()) {

065	attribute.add(matcher.group(1).trim());

066	String[] values = matcher.group(2).split(",");

067	ArrayList<String> al = new ArrayList<String>(values.length);

068	for (String value : values) {

069	al.add(value.trim());

070

}

071	attributevalue.add(al);

072	} else if (line.startsWith("@data")) {

073	while ((line = br.readLine()) != null) {

074	if(line=="")

075

continue;

076	String[] row = line.split(",");

077	data.add(row);

078

}

079

} else {

080

continue;

081

}

082

}

083	br.close();

084	} catch (IOException e1) {

085	e1.printStackTrace();

086

}

087

}

088

089

//设置决策变量

090	public void setDec(int n) {

091	if (n < 0 || n >= attribute.size()) {

092	System.err.println("决策变量指定错误。");

093	System.exit(2);

094

}

095	decatt = n;

096

}

097	public void setDec(String name) {

098	int n = attribute.indexOf(name);

099	setDec(n);

100

}

101

102	//给一个样本（数组中是各种情况的计数），计算它的熵

103	public double getEntropy(int[] arr) {

104	double entropy = 0.0;

105	int sum = 0;

106	for (int i = 0; i < arr.length; i++) {

107	entropy -= arr[i] * Math.log(arr[i]+Double.MIN_VALUE)/Math.log(2);

108	sum += arr[i];

109

}

110	entropy += sum * Math.log(sum+Double.MIN_VALUE)/Math.log(2);

111	entropy /= sum;

112	return entropy;

113

}

114

115	//给一个样本数组及样本的算术和，计算它的熵

116	public double getEntropy(int[] arr, int sum) {

117	double entropy = 0.0;

118	for (int i = 0; i < arr.length; i++) {

119	entropy -= arr[i] * Math.log(arr[i]+Double.MIN_VALUE)/Math.log(2);

120

}

121	entropy += sum * Math.log(sum+Double.MIN_VALUE)/Math.log(2);

122	entropy /= sum;

123	return entropy;

124

}

125

126	public boolean infoPure(ArrayList<Integer> subset) {

127	String value = data.get(subset.get(0))[decatt];

128	for (int i = 1; i < subset.size(); i++) {

129	String next=data.get(subset.get(i))[decatt];

130	//equals表示对象内容相同，==表示两个对象指向的是同一片内存

131	if (!value.equals(next))

132	return false;

133

}

134	return true;

135

}

136

137	// 给定原始数据的子集(subset中存储行号),当以第index个属性为节点时计算它的信息熵

138	public double calNodeEntropy(ArrayList<Integer> subset, int index) {

139	int sum = subset.size();

140	double entropy = 0.0;

141	int[][] info = new int[attributevalue.get(index).size()][];

142	for (int i = 0; i < info.length; i++)

143	info[i] = new int[attributevalue.get(decatt).size()];

144	int[] count = new int[attributevalue.get(index).size()];

145	for (int i = 0; i < sum; i++) {

146	int n = subset.get(i);

147	String nodevalue = data.get(n)[index];

148	int nodeind = attributevalue.get(index).indexOf(nodevalue);

149	count[nodeind]++;

150	String decvalue = data.get(n)[decatt];

151	int decind = attributevalue.get(decatt).indexOf(decvalue);

152	info[nodeind][decind]++;

153

}

154	for (int i = 0; i < info.length; i++) {

155	entropy += getEntropy(info[i]) * count[i] / sum;

156

}

157	return entropy;

158

}

159

160

// 构建决策树

161	public void buildDT(String name, String value, ArrayList<Integer> subset,

162	LinkedList<Integer> selatt) {

163	Element ele = null;

164	@SuppressWarnings("unchecked")

165	List<Element> list = root.selectNodes("//"+name);

166	Iterator<Element> iter=list.iterator();

167	while(iter.hasNext()){

168	ele=iter.next();

169	if(ele.attributeValue("value").equals(value))

170

break;

171

}

172	if (infoPure(subset)) {

173	ele.setText(data.get(subset.get(0))[decatt]);

174

return;

175

}

176	int minIndex = -1;

177	double minEntropy = Double.MAX_VALUE;

178	for (int i = 0; i < selatt.size(); i++) {

179	if (i == decatt)

180

continue;

181	double entropy = calNodeEntropy(subset, selatt.get(i));

182	if (entropy < minEntropy) {

183	minIndex = selatt.get(i);

184	minEntropy = entropy;

185

}

186

}

187	String nodeName = attribute.get(minIndex);

188	selatt.remove(new Integer(minIndex));

189	ArrayList<String> attvalues = attributevalue.get(minIndex);

190	for (String val : attvalues) {

191	ele.addElement(nodeName).addAttribute("value", val);

192	ArrayList<Integer> al = new ArrayList<Integer>();

193	for (int i = 0; i < subset.size(); i++) {

194	if (data.get(subset.get(i))[minIndex].equals(val)) {

195	al.add(subset.get(i));

196

}

197

}

198	buildDT(nodeName, val, al, selatt);

199

}

200

}

201

202	// 把xml写入文件

203	public void writeXML(String filename) {

204

try {

205	File file = new File(filename);

206	if (!file.exists())

207	file.createNewFile();

208	FileWriter fw = new FileWriter(file);

209	OutputFormat format = OutputFormat.createPrettyPrint(); // 美化格式

210	XMLWriter output = new XMLWriter(fw, format);

211	output.write(xmldoc);

212	output.close();

213	} catch (IOException e) {

214	System.out.println(e.getMessage());

215

}

216

}

217

}

最终生成的文件如下：

view sourceprint?

<?xml version="1.0" encoding="UTF-8"?>

<root>

<DecisionTree value="null">

<outlook value="sunny">

<humidity value="high">no</humidity>

<humidity value="normal">yes</humidity>

</outlook>

<outlook value="overcast">yes</outlook>

<outlook value="rainy">

<windy value="TRUE">no</windy>

<windy value="FALSE">yes</windy>

</outlook>

</DecisionTree>

</root>