數(shù)據(jù)挖掘決策樹算法實現(xiàn)

1、實驗數(shù)據(jù)挖掘決策樹算法實現(xiàn)實驗內容決策樹算法是非常常用的分類算法，是逼近離散目標函數(shù)的方法，學習得到的函數(shù)以決策樹 的形式表示。其基木思路是不斷選取產(chǎn)生信息增益最人的屬性來劃分樣例集和，構造決策樹。 信息增益定義為結點與其子結點的信息爛之差。信息癇是香農(nóng)提出的，用于描述信息不純度 (不穩(wěn)定性)，其計算公式是entropy (s)二 -z a log p,z = 1pi子集合中不同性(而二元分類即正樣例和負樣例)的樣例的比例。這樣信息收益nj以定義為 樣本按照某丿肉性劃分吋造成嫡減少的期望，可以區(qū)分訓練樣本中正負樣本的能力，其計算公 式是：gain (s9a) = entropy (s) 工 -

2、'entropy (s、.)ver(j) i s i 7(a)是屬件a的值域s足樣本集介s、.是s屮在屈件a i值等jf的樣木集介實現(xiàn)該算法針對的樣例集侖如下outlook temperaturehumidinwindplavtennis1sunnyhothighweakno<-'sutmvhothighstrongno3overcasthothighweakves4rainyfmildhighweakv亡s3if5rainvcoolnomialweakves6rainvjcoolnomialstrongno a7overcastcoolnomialstrongy亡sa8

3、sunnymildhighweak門oq9sunnycoolnormalweakv亡s310rainvniildnomialweakyes111suimyjmildnomialweakvesa12overcastmildhighstrongy亡s313overcasthotnormalweakves<-*j14rainymildhighstrongnoq該農(nóng)記錄了在不同氣候條件下是否公打球的悄況，要求根據(jù)該表用程序輸出決策樹二算法實現(xiàn)c+代碼如下：#include <iostrcam> #include <string>#include <vector>

4、;#include <map>#include <algorithm>#include <cmath> using namespace std;#define maxlen 6輸入每行的數(shù)據(jù)個數(shù)vector <vector <string> > state; vector <string> item(maxlen); vector <string> attribute_row; string end(”end”);/輸入結束 string yes(nyesn);string no(nnon);string bl

5、ank(“”)；map<string,vector < string > > map. int tree_size = 0;struct nodestring attribute;string arrived_value; vector<node *> childs;node()attribute = blank; arrived_value = blank;node * root;實例集對應一行實例集保存首行即屬性行數(shù)據(jù)attributevalues;/存儲屬性對應的所冇的值決策樹節(jié)點屬性值到達的屬性值所有的孩了根據(jù)數(shù)據(jù)實例計算屬性與值組成的mapvoid

6、 computemapfrom2dvector() unsigned int i,jjk;bool exited = false;vector<string> values;for(i = 1;i< maxleng-1; i+)按照列遍歷for (j = 1; j < statc.sizc(); j+)for (k = 0; k < values.size(); k+) if(!pare(statejlli) exited = true;if(!exited)values.push_back(stateji);exited = false;m

7、ap_attribute_valuesstateoi = values; values.erase(values.begin(), values.end();根據(jù)具體屈性和值來計算爛double computeentropy(vector <vector <string> > remain_state, string attribute, string value,bool ifparent)vcctor<int> count (2,0);unsigned int ij;bool done_flag = false;for(j = l;j< maxle

8、n; j+)if(done_flag) break;if(! attributc_row j .comparc(attributc) for(i = 1; i < remain_state.size(); i+)if(!ifparent&&!remain_pare(value) ii ifparent)/ifparent 記錄是否算父節(jié)點if(!remain_stateilmaxlen - pare(yes)count0+;else countll j+；donc_flag = true;)if(count0 = 0 ii count 1

9、= 0 ) return 0;double sum = count()l + countfl;doubleentropy=-count0/sum*log(count0/sum)/log(2.0)-countl /sum*log(count 1 /sum)/log(2.0);return entropy;計算按照屬性attribute劃分當前剩余實例的信息增益double computegain(vector <vector <string> > remain_state, string attribute)!unsigned int j,k,m;double paren

10、t_entropy = computeentropy(remain_state, attribute, blank, true);double children_entropy = 0;vcctor<string> values = map_attributc_valucsattributc;vector<double> ratio;vector<int> count_values;int tcmpint;for(m = 0; m < values.size(); m+)tempint = 0;for(k = l;k< maxlen 1; k+)

11、if(! attribute_row kj .compare(attribute) for(j = 1; j < remain_state.size(); j+)if(!remain_pare(valuesm) tempint+;) count_values.push_back(tempint);)for(j = 0; j < values.size(); j+)ratio.push_back(doublc)count_valucsj / (doublc)(rcmain_statc.sizc()-1); double temp_entropy;for(j =

12、0; j < values.size(); j+)temp_entropy = computeentropy(remain_state, attribute, valuesjl, false); childrcn_cntropy += ratioj * tcmp_cntropy;return (parent_entropy - children_entropy);int findattrinumbynamc(string attri)for(int i = 0; i < maxlen; i+)if(!pare(attri) return i;cerr«

13、;mcan,t find the numth of attributeu«endl;return 0;找出樣例屮占多數(shù)的正/負性string mostcommonlabel(vector <vector <string> > remain_state) int p = 0, n = 0;for(unsigned i = 0; i < remain_state.size(); i+)if(!remain_stateijmaxlen-pare(yes) p+; else n+;if(p >= n) return yes;else return no;判

14、斷樣例是否正負性都為labelbool allthesamelabel(vector <vector <string> > remain_state, string label) int count = 0;for(unsigned int i = 0; i < remain_state.size(); i+) if(!remain_stateilmaxlen-pare(label) count+;if(count = remain_state.size()-1) return true;else return false;node * buliddcc

15、isiontrccdfs(nodc * p, vector <vcctor <string> > rcmain_statc, vector <string> remain_attribute) /if(remain_state.size() > 0)/pri n t v( re mai n_state);/if (p = null)p = new node();if (allthesamelabel(remain_state, yes)p->attribute = yes;return p;if (ahthesamelabel(remain_st

16、ate5 no) p->attribute = no;return p;if(rcmain_attributc.sizc() = 0)string label = mostcommonlabcl(rcmain_statc);p->attribute = label;return p;double max_gain = 0, tcmp_gain;vector <string>:iterator max_it = remain_attribute.begin();vector <string>:iterator itl;for(itl = remain_attr

17、ibute.begin(); itl < remain_attribute.end(); itl+) temp_gain = computegain(remain_state, (*itl);if(tcmp_gain > max_gain) max_gain = temp_gain;max_it = itl;下面根據(jù)max"指向的屬性來劃分當前樣例，更浙樣例集和屬性集 vector <string> new_attribute;vector <vector <string> > new_state;for(vector <stri

18、ng>:itcrator it2 = rcmain_attributc.bcgin(); it2 < rcmain_d(); it2+)if(*it2).compare(*max_it) new_attribute.push_back(*it2);)p >auribute = *max_it;vector <string> values = map_attribute_values*max_il;int attribuc_num = findattrinumbynamc(*maxt); new_state.push_back(attribute_row);for(

19、vector <string>:iterator it3 = values.begin(); it3 < values.end(); it3+) for(unsigned int i = 1; i < remain_state.size(); i+) if(!remain_stateiattribue_pare(*it3) ncw_statc.push_back(rcmain_statci);node * new_node = new node();new node->arrived value = *it3;if(ncw_statc.sizc()

20、= 0)ncw_nodc->attributc=mostcommonlabel(remain_suite);elsebuliddecisiontreedfs(new_node, new_state, new_attribute); p->childs.push_back(ncw_nodc);new_state.erase(new_stcite.begin()+1 ,new_state.end();/return p;void input() string s; while(cin»s,pare(end) != 0)/-l 為輸入結束 item0 = s;for(int i

21、 = 1 ;i < maxlen; i+) cin»itcmi;state. pu sh_back(item); for(int j = 0;j< maxlen; j+)attribute_row.push_back(state0j);void printtree(node *p, int depth)for (int i = 0; i < depth; i+) cout« v;按照樹的深度先輸出 tabif(!p->arrivcd_valuc.cmpty()cout«p->arrived_value«endl;for (i

22、nt i = 0; i < depth+l; i+) cout« 't' 按照樹的深度先輸出 tabcout«p->attribute«endl;for (vector<node*>:iterator it = p->childs.begin(); it != p->childs.end(); it+) printtree(*it, depth + 1);void freetree(node *p)訐(p = null)return;for (vector<nodeitendor it = p->ch

23、ilds.begin(); it != p->childs.end(); it+) frcctrcc(*it);delete p;tree_size+;int main()input();vector <string> remain_attribute;string outlook(houtlooklf);string temperature(htemperatureh);string humidity("humiditym);string wind("wind,r);remain_attribute.push_back(outlook); remain_attribute.push_back(temperature);remain_attribute.push_back(humidity); remain_attribute.push_back(wind