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linyiqun
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粗糙集属性约简算法
粗糙集属性约简算法
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‎RoughSets/DataMining_RoughSets/Client.java‎

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package DataMining_RoughSets;
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/**
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* ´Ö²Ú¼¯Ô¼¼òËã·¨
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* @author lyq
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*
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*/
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public class Client {
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public static void main(String[] args){
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String filePath = "C:\\Users\\lyq\\Desktop\\icon\\input.txt";
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RoughSetsTool tool = new RoughSetsTool(filePath);
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tool.findingReduct();
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}
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}

‎RoughSets/DataMining_RoughSets/KnowledgeSystem.java‎

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package DataMining_RoughSets;
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import java.util.ArrayList;
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import java.util.HashMap;
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/**
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* 知识系统
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*
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* @author lyq
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*
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*/
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public class KnowledgeSystem {
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// 知识系统内的集合
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ArrayList<RecordCollection> ksCollections;
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public KnowledgeSystem(ArrayList<RecordCollection> ksCollections) {
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this.ksCollections = ksCollections;
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}
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/**
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* 获取集合的上近似集合
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*
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* @param rc
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* 原始集合
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* @return
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*/
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public RecordCollection getUpSimilarRC(RecordCollection rc) {
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RecordCollection resultRc = null;
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ArrayList<String> nameArray;
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ArrayList<String> targetArray;
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ArrayList<RecordCollection> copyRcs = new ArrayList<>();
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ArrayList<RecordCollection> deleteRcs = new ArrayList<>();
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targetArray = rc.getRecordNames();
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// 做一个集合拷贝
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for (RecordCollection recordCollection : ksCollections) {
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copyRcs.add(recordCollection);
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}
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for (RecordCollection recordCollection : copyRcs) {
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nameArray = recordCollection.getRecordNames();
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if (strIsContained(targetArray, nameArray)) {
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removeOverLaped(targetArray, nameArray);
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deleteRcs.add(recordCollection);
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if (resultRc == null) {
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resultRc = recordCollection;
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} else {
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// 进行并运算
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resultRc = resultRc.unionCal(recordCollection);
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}
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if (targetArray.size() == 0) {
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break;
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}
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}
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}
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//去除已经添加过的集合
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copyRcs.removeAll(deleteRcs);
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if (targetArray.size() > 0) {
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// 说明已经完全还未找全上近似的集合
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for (RecordCollection recordCollection : copyRcs) {
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nameArray = recordCollection.getRecordNames();
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if (strHasOverlap(targetArray, nameArray)) {
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removeOverLaped(targetArray, nameArray);
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if (resultRc == null) {
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resultRc = recordCollection;
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} else {
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// 进行并运算
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resultRc = resultRc.unionCal(recordCollection);
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}
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if (targetArray.size() == 0) {
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break;
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}
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}
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}
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}
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return resultRc;
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}
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/**
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* 获取集合的下近似集合
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*
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* @param rc
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* 原始集合
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* @return
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*/
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public RecordCollection getDownSimilarRC(RecordCollection rc) {
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RecordCollection resultRc = null;
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ArrayList<String> nameArray;
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ArrayList<String> targetArray;
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targetArray = rc.getRecordNames();
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for (RecordCollection recordCollection : ksCollections) {
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nameArray = recordCollection.getRecordNames();
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if (strIsContained(targetArray, nameArray)) {
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removeOverLaped(targetArray, nameArray);
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if (resultRc == null) {
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resultRc = recordCollection;
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} else {
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// 进行并运算
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resultRc = resultRc.unionCal(recordCollection);
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}
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if (targetArray.size() == 0) {
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break;
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}
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}
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}
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return resultRc;
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}
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/**
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* 判断2个字符数组之间是否有交集
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*
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* @param str1
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* 字符列表1
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* @param str2
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* 字符列表2
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* @return
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*/
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public boolean strHasOverlap(ArrayList<String> str1, ArrayList<String> str2) {
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boolean hasOverlap = false;
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for (String s1 : str1) {
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for (String s2 : str2) {
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if (s1.equals(s2)) {
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hasOverlap = true;
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break;
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}
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}
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if (hasOverlap) {
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break;
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}
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}
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return hasOverlap;
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}
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/**
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* 判断字符集str2是否完全包含于str1中
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*
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* @param str1
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* @param str2
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* @return
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*/
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public boolean strIsContained(ArrayList<String> str1, ArrayList<String> str2) {
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boolean isContained = false;
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int count = 0;
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for (String s : str2) {
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if (str1.contains(s)) {
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count++;
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}
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}
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if (count == str2.size()) {
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isContained = true;
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}
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return isContained;
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}
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/**
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* 字符列表移除公共元素
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*
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* @param str1
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* @param str2
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*/
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public void removeOverLaped(ArrayList<String> str1, ArrayList<String> str2) {
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ArrayList<String> deleteStrs = new ArrayList<>();
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for (String s1 : str1) {
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for (String s2 : str2) {
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if (s1.equals(s2)) {
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deleteStrs.add(s1);
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break;
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}
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}
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}
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// 进行公共元素的移除
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str1.removeAll(deleteStrs);
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}
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}

‎RoughSets/DataMining_RoughSets/Record.java‎

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package DataMining_RoughSets;
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import java.text.MessageFormat;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.Map;
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/**
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* 数据记录，包含这条记录所有属性
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*
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* @author lyq
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*
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*/
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public class Record {
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// 记录名称
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private String name;
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// 记录属性键值对
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private HashMap<String, String> attrValues;
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public Record(String name, HashMap<String, String> attrValues) {
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this.name = name;
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this.attrValues = attrValues;
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}
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public String getName() {
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return this.name;
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}
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/**
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* 此数据是否包含此属性值
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*
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* @param attr
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* 待判断属性值
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* @return
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*/
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public boolean isContainedAttr(String attr) {
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boolean isContained = false;
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if (attrValues.containsValue(attr)) {
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isContained = true;
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}
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return isContained;
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}
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/**
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* 判断数据记录是否是同一条记录，根据数据名称来判断
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*
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* @param record
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* 目标比较对象
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* @return
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*/
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public boolean isRecordSame(Record record) {
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boolean isSame = false;
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if (this.name.equals(record.name)) {
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isSame = true;
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}
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return isSame;
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}
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/**
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* 数据的决策属性分类
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*
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* @return
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*/
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public String getRecordDecisionClass() {
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String value = null;
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value = attrValues.get(RoughSetsTool.DECISION_ATTR_NAME);
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return value;
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}
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/**
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* 根据约简属性输出决策规则
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*
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* @param reductAttr
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* 约简属性集合
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*/
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public String getDecisionRule(ArrayList<String> reductAttr) {
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String ruleStr = "";
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String attrName = null;
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String value = null;
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String decisionValue;
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decisionValue = attrValues.get(RoughSetsTool.DECISION_ATTR_NAME);
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ruleStr += "属性";
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for (Map.Entry entry : this.attrValues.entrySet()) {
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attrName = (String) entry.getKey();
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value = (String) entry.getValue();
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if (attrName.equals(RoughSetsTool.DECISION_ATTR_NAME)
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|| reductAttr.contains(attrName) || value.equals(name)) {
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continue;
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}
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ruleStr += MessageFormat.format("{0}={1},", attrName, value);
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}
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ruleStr += "他的分类为" + decisionValue;
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return ruleStr;
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}
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}

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