機器學習 花卉識別分類源程序如下:
- #include <bits/stdc++.h>
- using namespace std;
- const int maxn=155;
- vector<int> v[maxn];
- map<string, int> mp;
- struct Iris
- {
- double prop[4];
- //0_Sepal_Length 1_Sepal_Width
- //2_Petal_Length 3_Petal_Width
- int sum;//原本分類;
- int pv;//預測分類;
- }all[maxn];
- void print(Iris a)
- {
- printf("%12.1f %11.1f %12.1f %11.1f %12d %15d ",a.prop[0],a.prop[1],a.prop[2],a.prop[3],a.sum,a.pv);
- if(a.sum==a.pv) printf("\n");
- else printf(" 預測錯誤\n");
- }
- void Iris_Division(int t)
- //分層隨機分成t份，用于t折交叉驗證；
- {
- srand(time(NULL));//重置隨機數(shù)種子；
- int book[maxn], cnt;
- memset(book, 0, sizeof(book));
- for(int i=1; i<=t; i++)
- {
- for(int j=1; j<=3; j++)
- {
- cnt=0;
- while(cnt<150/(t*3))
- {
- int x=rand()%50+1+50*(j-1);
- if(book[x]==1) continue;
- book[x]=1;
- v[i].push_back(x);
- cnt++;
- }
- }
- }
- }
- struct Tree_Node
- {
- int sum;//-1表示非葉節(jié)點， 0、1、2表示該節(jié)點劃分種類
- int basic;//依據(jù)什么劃分
- double div;//分界值
- struct Tree_Node* left;//小于等于分界值的兒子節(jié)點
- struct Tree_Node* right;//大于分界值的兒子節(jié)點
- };
- double log2(double x) { return log(x)/log((double)(2.0)); }
- double Cal_Ent_D(int cnt[], int n, int sum)
- {
- double Ent_D=0;
- for(int i=0; i<n; i++)
- if(cnt[i]!=0)
- {
- double x;
- x=cnt[i]*1.0/sum;
- Ent_D=Ent_D-x*log2(x);
- }
- return Ent_D;
- }
- void Cal_Max_Gain(set<int>train, int* basic, double* div)
- //計算最大的信息增益，選擇劃分依據(jù);
- {
- int cnt[3]={0};//3種分類結果的數(shù)量；
- int sum=train.size();
- //cout<<sum<<endl;
- for(auto x : train) cnt[all[x].sum]++;
- double Ent_D=Cal_Ent_D(cnt, 3, sum);
- vector<double> t;
- double maxx=-1e5;
- for(int i=0; i<4; i++)
- {
- t.clear();
- for(auto x : train) t.push_back(all[x].prop[i]);
- sort(t.begin(), t.end());//連續(xù)值排序，用于二分離散化
- for(int j=0; j<(int)(t.size()-1); j++)
- {
- double y=(t[j]+t[j+1])/2.0;
- double ans=Ent_D;
- memset(cnt, 0, sizeof(cnt));
- sum=0;
- for(auto x : train) if(all[x].prop[i]<=y) cnt[all[x].sum]++, sum++;
- if(sum!=0) ans=ans-(double)sum/(double)(t.size())*Cal_Ent_D(cnt, 3, sum);
- memset(cnt, 0, sizeof(cnt));
- sum=0;
- for(auto x : train) if(all[x].prop[i]>y) cnt[all[x].sum]++, sum++;
- if(sum!=0) ans=ans-(double)sum/(double)(t.size())*Cal_Ent_D(cnt, 3, sum);
- if(ans>maxx)
- {
- maxx=ans;
- *basic=i;
- *div=y;
- }
- }
- }
- }
- void Build_Tree(Tree_Node** T, set<int>train)//建立決策樹
- {
- *T=(Tree_Node*)malloc(sizeof(Tree_Node));
- ((*T)->sum)=-1; ((*T)->basic)=0; ((*T)->div)=0;
- set<int> s;
- for(auto x : train) s.insert(all[x].sum);
- if(s.size()==1) { ((*T)->sum)=(*s.begin()); s.clear(); return; }
- Cal_Max_Gain(train, &((*T)->basic), &((*T)->div));
- set<int> left_train, right_train;
- for(auto x : train)
- {
- if(all[x].prop[(*T)->basic] <= ((*T)->div)) left_train.insert(x);
- else right_train.insert(x);
- }
- Build_Tree(&(*T)->left, left_train);
- Build_Tree(&(*T)->right, right_train);
- }
- int Test_Data(Tree_Node* T, int x)
- {
- if((T->sum)!=-1) return T->sum;
- if(all[x].prop[T->basic]<=(T->div))
- return Test_Data(T->left, x);
- else return Test_Data(T->right, x);
- }
- int main()
- {
- freopen("iris.txt","r",stdin);
- int cnt=0;
- for(int i=1; i<=150; i++)
- {
- string s;
- scanf("%lf,%lf,%lf,%lf,",&all[i].prop[0],&all[i].prop[1],&all[i].prop[2],&all[i].prop[3]);
- cin>>s;
- if(!mp.count(s)) { mp[s]=cnt; cnt++;}
- all[i].sum=mp[s];
- }
- int t=10;
- Iris_Division(t);//分層隨機分成t份，用于t折交叉驗證；
- set<int> train, test;
- double Ans=0.0;
- for(int i=1; i<=10; i++)
- {
- train.clear(); test.clear();
- for(int j=1; j<=t; j++)
- {
- if(j==i) for(int k=0; k<(int)v[j].size(); k++) test.insert(v[j][k]);
- else for(int k=0; k<(int)v[j].size(); k++) train.insert(v[j][k]);
- }
- Tree_Node* tree;
- Build_Tree(&tree, train);
- printf("Sepal_Length Sepal_Width Petal_Length Petal_Width Actual_Value Predicted_Value\n");
- int Cor_Num=0, All_Num=0;
- for(auto x: test)
- {
- all[x].pv=Test_Data(tree, x);
- if(all[x].sum==all[x].pv) Cor_Num++;
- All_Num++;
- print(all[x]);
- }
- printf("第%d次 預測正確率：%.3f\n\n", i, (double)Cor_Num/(double)All_Num);
- Ans+=(double)Cor_Num/(double)All_Num;
- }
- printf("平均預測正確率：%.3f\n\n", Ans/(double)t);
- return 0;
- }
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機器學習程序（c++）.7z
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2020-6-2 03:33 上傳
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