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標題: knn算法的MATLAB源碼 [打印本頁]
作者: lixiansheng    時間: 2018-6-9 15:57
標題: knn算法的MATLAB源碼
  1. %knn 歸一化自寫

  3. % 把數(shù)據(jù)分為測試集和訓(xùn)練集
  4. % train=[wine(1:30,:);wine(60:89,:);wine(131:160,:)];
  5. % test=[wine(31:59,:);wine(90:130,:);wine(161:178,:)];
  6. % train_labels=[wine_labels(1:30,:);wine_labels(60:89,:);wine_labels(131:160,:)];
  7. % test_labels=[wine_labels(31:59,:);wine_labels(90:130,:);wine_labels(161:178,:)];
  8. %把標簽加到數(shù)據(jù)的最后一列
  9. wine(:,14)=wine_labels;
  10. %按行隨機打亂數(shù)據(jù)
  11. wine_data =wine(randperm(size(wine,1)),:);
  12. %取前一百條數(shù)據(jù)給訓(xùn)練集
  13. train=wine_data(1:100,1:13);
  14. train_labels=wine_data(1:100,14);
  15. %剩下的78條給測試集
  16. test=wine_data(101:178,1:13);
  17. test_labels=wine_data(101:178,14);
  18. %數(shù)據(jù)歸一化
  19. h=max(train,[],1);
  20. l=min(train,[],1);
  21. for i=1:13
  22.     train(:,i)=(train(:,i)-l(i))./(h(i)-l(i));
  23.     test(:,i)=(test(:,i)-l(i))./(h(i)-l(i));
  24. end
  25. %[train,strc]=mapminmax(train');  
  26. %strc歸一化返回的參數(shù) max min
  27. %test=mapminmax.apply(test',strc);
  28. %計算測試集與所有訓(xùn)練集間的距離
  29. all_distance=dist(test,train');
  30. %升序排序 B為返回的索引
  31. [A,B]=sort(all_distance,2);
  32. %Knn  K近鄰
  33. K=3;
  34. all_labels=train_labels(B(:,1:K));
  35. %統(tǒng)計矩陣中出現(xiàn)的類別
  36. b=unique(all_labels);
  37. %統(tǒng)計各個類別出現(xiàn)的次數(shù)
  38. c=histc(all_labels',b);
  39. %新標簽賦值
  40. [m,predict_labels]=max(c);
  41. %求準確率
  42. accuracy=1-(length(find((predict_labels-test_labels')~=0))/length(test_labels))
  43. %畫圖
  44. figure;
  45. hold on;
  46. plot(test_labels,'o');
  47. plot(predict_labels,'*');
  48. xlabel('測試集數(shù)據(jù)','FontSize',12);
  49. ylabel('類別標簽','FontSize',12);
  50. legend('實際測試集分類','預(yù)測測試集分類');
  51. title('測試集的實際分類和預(yù)測分類圖','FontSize',12);
  52. grid on;





  58. %knn 歸一化使用mapminmax函數(shù)進行的
  59. %knn

  61. % 把數(shù)據(jù)分為測試集和訓(xùn)練集
  62. % train=[wine(1:30,:);wine(60:89,:);wine(131:160,:)];
  63. % test=[wine(31:59,:);wine(90:130,:);wine(161:178,:)];
  64. % train_labels=[wine_labels(1:30,:);wine_labels(60:89,:);wine_labels(131:160,:)];
  65. % test_labels=[wine_labels(31:59,:);wine_labels(90:130,:);wine_labels(161:178,:)];
  66. %把標簽加到數(shù)據(jù)的最后一列
  67. wine(:,14)=wine_labels;
  68. %按行隨機打亂數(shù)據(jù)
  69. wine_data =wine(randperm(size(wine,1)),:);
  70. %取前一百條數(shù)據(jù)給訓(xùn)練集
  71. train=wine_data(1:100,1:13);
  72. train_labels=wine_data(1:100,14);
  73. %剩下的78條給測試集
  74. test=wine_data(101:178,1:13);
  75. test_labels=wine_data(101:178,14);
  76. %數(shù)據(jù)歸一化
  77. [train,strc]=mapminmax(train');  
  78. %strc歸一化返回的參數(shù) max min
  79. test=mapminmax.apply(test',strc);
  80. %計算測試集與所有訓(xùn)練集間的距離
  81. all_distance=dist(test',train);
  82. %升序排序 B為返回的索引
  83. [A,B]=sort(all_distance,2);
  84. %Knn  K近鄰
  85. K=5;
  86. all_labels=train_labels(B(:,1:K));
  87. %統(tǒng)計矩陣中出現(xiàn)的類別
  88. b=unique(all_labels);
  89. %統(tǒng)計各個類別出現(xiàn)的次數(shù)
  90. c=histc(all_labels',b);
  91. %新標簽賦值
  92. [~,predict_labels]=max(c);
  93. %求準確率
  94. accuracy=1-(length(find((predict_labels-test_labels')~=0))/length(test_labels))
  95. %畫圖
  96. figure;
  97. hold on;
  98. plot(test_labels,'o');
  99. plot(predict_labels,'*');
  100. xlabel('測試集數(shù)據(jù)','FontSize',12);
  101. ylabel('類別標簽','FontSize',12);
  102. legend('實際測試集分類','預(yù)測測試集分類');
  103. title('測試集的實際分類和預(yù)測分類圖','FontSize',12);
  104. grid on;
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