平均处理速度越来越慢

我不擅长英语。所以我要求你的耐心和编辑。平均处理速度越来越慢

我已经分析了韩国的人口普查数据。我使用“adaboost”作为分析方法，并为每个城市实施了adaboost。目标变量，自变量，公式，选项，观察次数等在城市之间都是一样的。没有区别。而且我制作了一个宏观实现城市的adaboost一个接一个。该宏在开始分析下一个城市之前删除除自身以外的所有对象。

我发现平均过程速度随着时间的推移变慢。我的意思是，城市1-30所需的平均时间大约为10分钟，但对于城市80-100，平均所需时间为25分钟。于是我终止了R会话并重新开放。我从第80个城市重新运行宏，然后80-100城市的平均时间缩短为10分钟。

为什么会发生这种情况？我怎么解决这个问题？我需要你的帮助。提前致谢。

r代码和宏如下。 'sgg'宏变量表示城市代码。目标变量有六个类别。所以我用'一人'的方法进行多类分类。

memory.limit(size=4095) 

library(gbm) 
library(gtools) 

dbset<-strmacro(sgg,sampnum, 
expr= 
{ 
setwd("C:\\Users\\Kim\\Desktop\\JQ\\"); 
db.sgg<-read.table('sgg.TXT', header=TRUE); 
db.sgg<-subset(db.sgg, AGE>=10) 

N<-nrow(db.sgg); 
if(N>=sampnum) 
    { 
    db.sgg<-db.sgg[sample(1:N, sampnum),] 
    }; 

db.sgg$SEX<-factor(letters[db.sgg$SEX]) 

ordered(db.sgg$AREA); 

db.sgg$josagu_attri_mod2<-factor(letters[db.sgg$josagu_attri_mod]) 
db.sgg$geocheo_type_mod2<-factor(letters[db.sgg$geocheo_type_mod]) 
db.sgg$dandok_type_mod2<-factor(letters[db.sgg$dandok_type_mod]) 

colnames(db.sgg)[10:15]<-c('FLC3.1','FLC3.3','FLC3.4','FLC3.5','FLC3.6','FLC3.7'); 
} 
); 

train<-strmacro(i, sgg, 
expr={ 
    res.sgg.i<-gbm(FLC3.i~SEX+AREA+AGE+josagu_attri_mod2+geocheo_type_mod2+dandok_type_mod2, 
    data=db.sgg, distribution='adaboost', n.trees=3000,shrinkage = 0.005, 
    cv.folds=5, keep.data=TRUE, 
    bag.fraction=0.7, interaction.depth=3); 
    BI.sgg.i<-gbm.perf(res.sgg.i, method='cv'); 
    PRED.sgg.i<-predict.gbm(res.sgg.i, db.sgg, BI.sgg.i); 
    } 
    ); 


clean<-strmacro(sgg, 
    expr={ 
     rm(BI.sgg.1);rm(BI.sgg.3);rm(BI.sgg.4);rm(BI.sgg.5);rm(BI.sgg.6);rm(BI.sgg.7); 
     rm(res.sgg.1);rm(res.sgg.3);rm(res.sgg.4);rm(res.sgg.5);rm(res.sgg.6);rm(res.sgg.7); 
     rm(PRED.sgg.1);rm(PRED.sgg.3);rm(PRED.sgg.4);rm(PRED.sgg.5);rm(PRED.sgg.6);rm(PRED.sgg.7); 
     rm(db.sgg); 
     } 
     ); 

all<-strmacro(sgg,num, 
    expr={ 
     dbset(sgg,num); 
     train(1,sgg);train(3,sgg);train(4,sgg);train(5,sgg);train(6,sgg);train(7,sgg); 
     clean(sgg); 
     } 
    ); 

all(11010,3000) 
all(11020,3000) 
all(11030,3000)

数据如下所示。这些数据存储在'11010.txt'中。

C3 mem_num josagu_attri_mod SEX AGE geocheo_type_mod dandok_type_mod C97 FLC3 FLC3_1 FLC3_3 FLC3_4 FLC_4 FLC3_6 FLC3_7 AREA 
1 3 3 1 14 1 1 49 3 0 0 1 0 0 0 2 
1 1 1 2 44 1 1 49 3 0 0 1 0 0 0 2 
2 1 1 1 67 1 1 46 6 0 0 0 0 0 1 2 
2 2 2 2 60 1 1 46 6 0 0 0 0 0 1 2 
3 1 1 2 65 1 1 49 6 0 0 0 0 0 1 2 
4 1 1 1 66 1 4 16 6 0 0 0 0 0 1 1 
4 2 2 2 61 1 4 16 6 0 0 0 0 0 1 1 
5 1 1 2 32 1 6 15 1 1 0 0 0 0 0 1 
7 2 2 1 55 1 6 59 5 0 0 0 0 1 0 2 
7 4 3 1 23 1 6 59 5 0 0 0 0 1 0 2 
7 1 1 2 50 1 6 59 5 0 0 0 0 1 0 2 
7 3 3 2 24 1 6 59 5 0 0 0 0 1 0 2 
8 1 1 1 68 1 1 46 6 0 0 0 0 0 1 2 
8 2 2 2 64 1 1 46 6 0 0 0 0 0 1 2 
9 1 1 1 57 1 1 59 5 0 0 0 0 1 0 2 
9 2 2 2 55 1 1 59 5 0 0 0 0 1 0 2 
9 3 3 2 28 1 1 59 5 0 0 0 0 1 0 2 
10 2 3 1 17 1 6 16 4 0 0 0 1 0 0 1 
10 1 1 2 41 1 6 16 4 0 0 0 1 0 0 1 
12 1 1 2 24 1 6 16 1 1 0 0 0 0 0 1 
14 1 1 1 72 2 1 119 6 0 0 0 0 0 1 4 
14 2 2 2 72 2 1 119 6 0 0 0 0 0 1 4 
17 2 2 1 42 1 4 50 3 0 0 1 0 0 0 2 
17 1 1 2 41 1 4 50 3 0 0 1 0 0 0 2 
19 1 1 1 36 1 4 50 2 0 1 0 0 0 0 2 
19 2 2 2 27 1 4 50 2 0 1 0 0 0 0 2 
20 2 2 1 52 1 1 50 5 0 0 0 0 1 0 2 
20 3 3 1 21 1 1 50 5 0 0 0 0 1 0 2 
20 4 3 1 24 1 1 50 5 0 0 0 0 1 0 2 
20 1 1 2 50 1 1 50 5 0 0 0 0 1 0 2 
21 1 1 1 38 1 6 59 3 0 0 1 0 0 0 2 
21 5 3 1 14 1 6 59 3 0 0 1 0 0 0 2 
21 2 2 2 39 1 6 59 3 0 0 1 0 0 0 2 
21 3 3 2 13 1 6 59 3 0 0 1 0 0 0 2 
22 1 1 1 42 1 4 50 2 0 1 0 0 0 0 2 
22 2 2 2 37 1 4 50 2 0 1 0 0 0 0 2 
23 1 1 2 70 1 6 50 6 0 0 0 0 0 1 2 
24 1 1 2 49 1 4 50 5 0 0 0 0 1 0 2 
24 2 3 2 22 1 4 50 5 0 0 0 0 1 0 2 
25 1 1 1 67 4 1 66 5 0 0 0 0 1 0 2 
25 4 3 1 41 4 1 66 5 0 0 0 0 1 0 2 
25 2 2 2 66 4 1 66 5 0 0 0 0 1 0 2 
25 3 3 2 43 4 1 66 5 0 0 0 0 1 0 2 
27 1 1 1 81 1 1 43 6 0 0 0 0 0 1 2 
27 2 2 2 80 1 1 43 6 0 0 0 0 0 1 2 
29 1 1 1 48 1 2 43 4 0 0 0 1 0 0 2 
29 3 3 1 17 1 2 43 4 0 0 0 1 0 0 2 
29 2 2 2 45 1 2 43 4 0 0 0 1 0 0 2 
29 4 3 2 15 1 2 43 4 0 0 0 1 0 0 2 
32 1 1 1 67 2 1 85 6 0 0 0 0 0 1 3 
32 2 2 2 60 2 1 85 6 0 0 0 0 0 1 3 
33 1 1 1 30 2 1 82 2 0 1 0 0 0 0 3 
33 2 2 2 30 2 1 82 2 0 1 0 0 0 0 3 
34 1 1 1 55 2 1 85 5 0 0 0 0 1 0 3 
34 2 2 2 48 2 1 85 5 0 0 0 0 1 0 3 
34 3 3 2 28 2 1 85 5 0 0 0 0 1 0 3 
37 1 1 1 55 2 4 82 5 0 0 0 0 1 0 3 
37 3 3 1 21 2 4 82 5 0 0 0 0 1 0 3 
37 2 2 2 51 2 4 82 5 0 0 0 0 1 0 3 
38 1 1 1 41 2 4 50 2 0 1 0 0 0 0 2 
38 2 2 2 32 2 4 50 2 0 1 0 0 0 0 2 
40 2 3 1 14 2 1 50 4 0 0 0 1 0 0 2 
40 1 1 2 45 2 1 50 4 0 0 0 1 0 0 2 
40 3 3 2 15 2 1 50 4 0 0 0 1 0 0 2 
41 1 1 1 46 2 4 40 4 0 0 0 1 0 0 2 
41 3 3 1 17 2 4 40 4 0 0 0 1 0 0 2 
41 2 2 2 44 2 4 40 4 0 0 0 1 0 0 2 
41 4 3 2 14 2 4 40 4 0 0 0 1 0 0 2 
42 2 3 1 17 2 6 50 5 0 0 0 0 1 0 2 
42 1 1 2 41 2 6 50 5 0 0 0 0 1 0 2 
42 3 3 2 18 2 6 50 5 0 0 0 0 1 0 2 
43 1 1 1 34 2 4 50 2 0 1 0 0 0 0 2 
43 2 2 2 35 2 4 50 2 0 1 0 0 0 0 2 
44 1 1 1 46 2 1 40 4 0 0 0 1 0 0 2 
44 2 2 2 44 2 1 40 4 0 0 0 1 0 0 2 
44 3 3 2 13 2 1 40 4 0 0 0 1 0 0 2 
45 1 1 1 43 2 1 72 3 0 0 1 0 0 0 3 
45 2 2 2 38 2 1 72 3 0 0 1 0 0 0 3 
45 3 3 2 12 2 1 72 3 0 0 1 0 0 0 3 
46 1 1 1 55 2 4 61 4 0 0 0 1 0 0 2 
46 3 3 1 19 2 4 61 4 0 0 0 1 0 0 2 
46 2 2 2 54 2 4 61 4 0 0 0 1 0 0 2 
46 4 3 2 18 2 4 61 4 0 0 0 1 0 0 2 
47 1 1 1 30 2 4 61 1 1 0 0 0 0 0 2 
47 2 2 2 30 2 4 61 1 1 0 0 0 0 0 2 
48 1 1 1 64 2 4 72 5 0 0 0 0 1 0 3 
48 2 2 2 54 2 4 72 5 0 0 0 0 1 0 3 
48 3 3 2 23 2 4 72 5 0 0 0 0 1 0 3 
49 1 1 1 39 2 4 61 3 0 0 1 0 0 0 2 
49 2 2 2 36 2 4 61 3 0 0 1 0 0 0 2 
50 1 1 1 63 2 1 72 6 0 0 0 0 0 1 3 
50 2 2 2 59 2 1 72 6 0 0 0 0 0 1 3 
52 1 1 2 53 2 6 58 5 0 0 0 0 1 0 2 
52 2 3 2 26 2 6 58 5 0 0 0 0 1 0 2 
53 2 3 1 45 2 1 58 5 0 0 0 0 1 0 2 
53 1 1 2 69 2 1 58 5 0 0 0 0 1 0 2 
54 1 1 2 55 2 6 58 5 0 0 0 0 1 0 2 
54 2 3 2 29 2 6 58 5 0 0 0 0 1 0 2 
56 3 3 1 16 2 4 58 4 0 0 0 1 0 0 2 
56 1 1 2 45 2 4 58 4 0 0 0 1 0 0 2 
56 2 3 2 12 2 4 58 4 0 0 0 1 0 0 2 
59 1 1 1 56 2 1 50 5 0 0 0 0 1 0 2 
59 3 3 1 29 2 1 50 5 0 0 0 0 1 0 2 
59 2 2 2 53 2 1 50 5 0 0 0 0 1 0 2 
62 1 1 1 59 2 1 50 5 0 0 0 0 1 0 2 
62 2 2 2 54 2 1 50 5 0 0 0 0 1 0 2 
62 3 3 2 26 2 1 50 5 0 0 0 0 1 0 2 
63 1 1 1 42 2 4 50 3 0 0 1 0 0 0 2 
63 3 3 1 10 2 4 50 3 0 0 1 0 0 0 2 
63 2 2 2 37 2 4 50 3 0 0 1 0 0 0 2 
64 1 1 1 31 2 6 50 2 0 1 0 0 0 0 2 
64 2 2 2 33 2 6 50 2 0 1 0 0 0 0 2 
65 1 1 1 60 2 1 50 5 0 0 0 0 1 0 2 
65 3 3 1 27 2 1 50 5 0 0 0 0 1 0 2 
65 2 2 2 57 2 1 50 5 0 0 0 0 1 0 2 
66 1 1 1 49 2 1 50 5 0 0 0 0 1 0 2 
66 2 2 2 54 2 1 50 5 0 0 0 0 1 0 2 
66 3 3 2 25 2 1 50 5 0 0 0 0 1 0 2 
67 1 1 1 67 2 1 120 6 0 0 0 0 0 1 4 
67 2 2 2 65 2 1 120 6 0 0 0 0 0 1 4 
68 1 1 1 49 2 4 98 5 0 0 0 0 1 0 3 
68 4 3 1 18 2 4 98 5 0 0 0 0 1 0 3 
68 2 2 2 48 2 4 98 5 0 0 0 0 1 0 3 
68 3 3 2 20 2 4 98 5 0 0 0 0 1 0 3 
70 1 1 1 66 2 1 101 6 0 0 0 0 0 1 4 
70 2 2 2 56 2 1 101 6 0 0 0 0 0 1 4 
71 1 1 1 37 2 2 101 2 0 1 0 0 0 0 4 
71 2 2 2 37 2 2 101 2 0 1 0 0 0 0 4 
72 1 1 1 74 1 1 91 5 0 0 0 0 1 0 3 
72 3 3 1 41 1 1 91 5 0 0 0 0 1 0 3 
72 2 2 2 71 1 1 91 5 0 0 0 0 1 0 3 
73 1 1 1 60 1 1 91 5 0 0 0 0 1 0 3 
73 3 3 1 27 1 1 91 5 0 0 0 0 1 0 3 
73 2 2 2 57 1 1 91 5 0 0 0 0 1 0 3 
74 1 1 1 61 2 1 119 5 0 0 0 0 1 0 4 
74 4 3 1 30 2 1 119 5 0 0 0 0 1 0 4 
74 2 2 2 56 2 1 119 5 0 0 0 0 1 0 4 
74 3 3 2 32 2 1 119 5 0 0 0 0 1 0 4 
75 2 2 1 72 1 1 99 6 0 0 0 0 0 1 3 
75 1 1 2 71 1 1 99 6 0 0 0 0 0 1 3 
78 1 1 2 28 1 4 15 1 1 0 0 0 0 0 1 
80 1 1 1 68 1 1 92 6 0 0 0 0 0 1 3 
80 2 2 2 66 1 1 92 6 0 0 0 0 0 1 3 
81 2 3 1 27 1 6 46 5 0 0 0 0 1 0 2 
81 3 3 1 29 1 6 46 5 0 0 0 0 1 0 2 
81 1 1 2 57 1 6 46 5 0 0 0 0 1 0 2 
83 1 1 1 30 1 4 43 2 0 1 0 0 0 0 2 
83 2 2 2 30 1 4 43 2 0 1 0 0 0 0 2 
84 1 1 2 64 1 4 33 6 0 0 0 0 0 1 2 
85 1 1 1 35 1 6 26 2 0 1 0 0 0 0 1 
85 2 2 2 33 1 6 26 2 0 1 0 0 0 0 1 
88 1 1 1 52 2 1 145 5 0 0 0 0 1 0 5 
88 2 2 2 49 2 1 145 5 0 0 0 0 1 0 5 
88 3 3 2 25 2 1 145 5 0 0 0 0 1 0 5 
89 2 3 1 13 2 1 145 4 0 0 0 1 0 0 5 
89 3 3 1 22 2 1 145 4 0 0 0 1 0 0 5 
89 1 1 2 47 2 1 145 4 0 0 0 1 0 0 5 
90 1 1 1 57 2 1 83 5 0 0 0 0 1 0 3 
90 3 3 1 25 2 1 83 5 0 0 0 0 1 0 3 
90 4 3 1 23 2 1 83 5 0 0 0 0 1 0 3 
90 2 2 2 52 2 1 83 5 0 0 0 0 1 0 3 
91 1 1 1 33 2 4 83 2 0 1 0 0 0 0 3 
91 2 2 2 32 2 4 83 2 0 1 0 0 0 0 3 
92 1 1 1 35 2 1 83 3 0 0 1 0 0 0 3 
92 2 2 2 34 2 1 83 3 0 0 1 0 0 0 3 
93 1 1 1 35 2 4 83 2 0 1 0 0 0 0 3 
93 2 2 2 35 2 4 83 2 0 1 0 0 0 0 3 
94 1 1 1 55 2 4 83 5 0 0 0 0 1 0 3 
94 2 2 2 53 2 4 83 5 0 0 0 0 1 0 3 
94 3 3 2 27 2 4 83 5 0 0 0 0 1 0 3 
94 4 3 2 24 2 4 83 5 0 0 0 0 1 0 3 
95 1 1 1 49 2 1 83 4 0 0 0 1 0 0 3 
95 2 3 1 21 2 1 83 4 0 0 0 1 0 0 3 
95 3 3 2 15 2 1 83 4 0 0 0 1 0 0 3 
96 1 1 1 35 2 1 83 1 1 0 0 0 0 0 3 
96 2 2 2 35 2 1 83 1 1 0 0 0 0 0 3 
97 1 1 1 54 2 1 83 4 0 0 0 1 0 0 3 
97 4 3 1 13 2 1 83 4 0 0 0 1 0 0 3 
97 2 2 2 51 2 1 83 4 0 0 0 1 0 0 3 
97 3 3 2 21 2 1 83 4 0 0 0 1 0 0 3 
98 1 1 1 49 4 1 85 4 0 0 0 1 0 0 3 
98 2 2 2 42 4 1 85 4 0 0 0 1 0 0 3 
98 3 3 2 20 4 1 85 4 0 0 0 1 0 0 3 
98 4 3 2 17 4 1 85 4 0 0 0 1 0 0 3 
99 1 1 1 69 4 1 96 6 0 0 0 0 0 1 3 
99 2 2 2 66 4 1 96 6 0 0 0 0 0 1 3 
100 1 1 1 69 4 1 96 6 0 0 0 0 0 1 3 
100 2 2 2 60 4 1 96 6 0 0 0 0 0 1 3 
101 1 1 1 32 4 4 33 1 1 0 0 0 0 0 2 
101 2 2 2 32 4 4 33 1 1 0 0 0 0 0 2 
103 1 1 1 39 1 6 27 4 0 0 0 1 0 0 1 
103 2 2 2 34 1 6 27 4 0 0 0 1 0 0 1 
103 3 3 2 13 1 6 27 4 0 0 0 1 0 0 1 
105 2 3 1 19 1 6 27 5 0 0 0 0 1 0 1 
105 3 3 1 25 1 6 27 5 0 0 0 0 1 0 1 
105 1 1 2 49 1 6 27 5 0 0 0 0 1 0 1 
106 1 1 1 28 1 6 23 1 1 0 0 0 0 0 1 
107 1 1 1 49 2 1 78 5 0 0 0 0 1 0 3 
107 3 3 1 18 2 1 78 5 0 0 0 0 1 0 3 
107 4 3 1 21 2 1 78 5 0 0 0 0 1 0 3 
107 2 2 2 49 2 1 78 5 0 0 0 0 1 0 3 
108 1 1 1 33 2 4 78 1 1 0 0 0 0 0 3 
108 2 2 2 28 2 4 78 1 1 0 0 0 0 0 3 
109 1 1 1 55 2 1 78 5 0 0 0 0 1 0 3 
109 3 3 1 21 2 1 78 5 0 0 0 0 1 0 3 
109 2 2 2 53 2 1 78 5 0 0 0 0 1 0 3 
110 1 1 1 48 2 1 78 4 0 0 0 1 0 0 3 
110 2 2 2 45 2 1 78 4 0 0 0 1 0 0 3 
110 3 3 2 20 2 1 78 4 0 0 0 1 0 0 3 
110 4 3 2 15 2 1 78 4 0 0 0 1 0 0 3 
111 1 1 1 37 2 4 78 2 0 1 0 0 0 0 3 
111 2 2 2 34 2 4 78 2 0 1 0 0 0 0 3 
112 1 1 1 28 4 4 82 2 0 1 0 0 0 0 3 
112 2 2 2 30 4 4 82 2 0 1 0 0 0 0 3 
113 1 1 1 64 4 1 63 5 0 0 0 0 1 0 2 
113 3 3 1 26 4 1 63 5 0 0 0 0 1 0 2 
113 2 2 2 55 4 1 63 5 0 0 0 0 1 0 2 
113 4 3 2 24 4 1 63 5 0 0 0 0 1 0 2 
115 1 1 1 42 4 4 58 3 0 0 1 0 0 0 2 
115 4 3 1 10 4 4 58 3 0 0 1 0 0 0 2 
115 2 2 2 39 4 4 58 3 0 0 1 0 0 0 2 
115 3 3 2 12 4 4 58 3 0 0 1 0 0 0 2 
116 1 1 1 56 4 1 63 5 0 0 0 0 1 0 2 
116 3 3 1 29 4 1 63 5 0 0 0 0 1 0 2 
116 2 2 2 51 4 1 63 5 0 0 0 0 1 0 2 
118 1 1 1 75 4 1 70 6 0 0 0 0 0 1 3 
118 2 2 2 74 4 1 70 6 0 0 0 0 0 1 3 
119 1 1 1 49 1 4 66 4 0 0 0 1 0 0 2 
119 3 3 1 18 1 4 66 4 0 0 0 1 0 0 2 
119 4 3 1 17 1 4 66 4 0 0 0 1 0 0 2 
119 2 2 2 47 1 4 66 4 0 0 0 1 0 0 2 
120 2 3 1 32 4 1 56 5 0 0 0 0 1 0 2 
120 1 1 2 64 4 1 56 5 0 0 0 0 1 0 2 
120 3 3 2 41 4 1 56 5 0 0 0 0 1 0 2 
121 1 1 1 44 4 4 56 4 0 0 0 1 0 0 2 
121 3 3 1 17 4 4 56 4 0 0 0 1 0 0 2 
121 4 3 1 16 4 4 56 4 0 0 0 1 0 0 2 
121 2 2 2 42 4 4 56 4 0 0 0 1 0 0 2 
123 1 1 1 44 4 4 56 3 0 0 1 0 0 0 2 
123 2 2 2 43 4 4 56 3 0 0 1 0 0 0 2 
123 3 3 2 11 4 4 56 3 0 0 1 0 0 0 2 
123 5 3 2 14 4 4 56 3 0 0 1 0 0 0 2 
125 2 3 1 21 1 4 30 5 0 0 0 0 1 0 1 
125 1 1 2 48 1 4 30 5 0 0 0 0 1 0 1 
126 1 1 1 49 1 4 30 5 0 0 0 0 1 0 1 
126 2 2 2 44 1 4 30 5 0 0 0 0 1 0 1 
126 3 3 2 25 1 4 30 5 0 0 0 0 1 0 1 
127 1 1 1 75 1 6 25 6 0 0 0 0 0 1 1 
128 1 1 1 49 2 1 82 3 0 0 1 0 0 0 3 
128 3 3 1 12 2 1 82 3 0 0 1 0 0 0 3 
128 2 2 2 43 2 1 82 3 0 0 1 0 0 0 3 
129 1 1 2 66 2 1 82 6 0 0 0 0 0 1 3 
130 1 1 1 36 2 4 82 3 0 0 1 0 0 0 3 
130 2 2 2 41 2 4 82 3 0 0 1 0 0 0 3 
130 3 3 2 11 2 4 82 3 0 0 1 0 0 0 3 
132 1 1 2 64 2 1 82 6 0 0 0 0 0 1 3 
133 1 1 1 34 2 1 82 2 0 1 0 0 0 0 3 
133 2 2 2 35 2 1 82 2 0 1 0 0 0 0 3 
134 1 1 1 37 2 4 82 2 0 1 0 0 0 0 3 
134 2 2 2 35 2 4 82 2 0 1 0 0 0 0 3 
135 1 1 1 38 2 4 82 2 0 1 0 0 0 0 3 
135 2 2 2 37 2 4 82 2 0 1 0 0 0 0 3 
136 1 1 1 31 2 6 83 1 1 0 0 0 0 0 3 
136 2 2 2 29 2 6 83 1 1 0 0 0 0 0 3 
137 2 2 1 47 2 5 30 3 0 0 1 0 0 0 1 
137 3 3 1 19 2 5 30 3 0 0 1 0 0 0 1 
137 1 1 2 43 2 5 30 3 0 0 1 0 0 0 1 
137 4 3 2 10 2 5 30 3 0 0 1 0 0 0 1 
138 2 3 1 24 2 5 30 5 0 0 0 0 1 0 1 
138 1 1 2 54 2 5 30 5 0 0 0 0 1 0 1 
139 1 1 2 44 2 5 30 4 0 0 0 1 0 0 1 
139 2 3 2 13 2 5 30 4 0 0 0 1 0 0 1 
139 3 3 2 15 2 5 30 4 0 0 0 1 0 0 1 
140 1 1 2 55 2 5 30 5 0 0 0 0 1 0 1 
140 2 3 2 29 2 5 30 5 0 0 0 0 1 0 1 
141 1 1 1 45 2 5 30 4 0 0 0 1 0 0 1 
141 2 2 2 43 2 5 30 4 0 0 0 1 0 0 1 
141 3 3 2 15 2 5 30 4 0 0 0 1 0 0 1 
141 4 3 2 13 2 5 30 4 0 0 0 1 0 0 1 
142 1 1 2 52 2 5 30 4 0 0 0 1 0 0 1 
142 2 3 2 23 2 5 30 4 0 0 0 1 0 0 1 
142 3 3 2 18 2 5 30 4 0 0 0 1 0 0 1 
144 1 1 1 52 2 5 30 5 0 0 0 0 1 0 1 
144 4 3 1 19 2 5 30 5 0 0 0 0 1 0 1 
144 2 2 2 48 2 5 30 5 0 0 0 0 1 0 1 
144 3 3 2 22 2 5 30 5 0 0 0 0 1 0 1 
145 1 1 1 61 2 4 72 6 0 0 0 0 0 1 3 
145 2 2 2 55 2 4 72 6 0 0 0 0 0 1 3 
147 1 1 1 32 2 4 85 1 1 0 0 0 0 0 3 
147 2 2 2 33 2 4 85 1 1 0 0 0 0 0 3 
148 1 1 1 30 2 1 85 1 1 0 0 0 0 0 3 
148 2 2 2 35 2 1 85 1 1 0 0 0 0 0 3 
149 2 2 1 54 1 1 46 4 0 0 0 1 0 0 2 
149 1 1 2 51 1 1 46 4 0 0 0 1 0 0 2 
149 3 3 2 20 1 1 46 4 0 0 0 1 0 0 2 
149 4 3 2 13 1 1 46 4 0 0 0 1 0 0 2 
149 5 3 2 27 1 1 46 4 0 0 0 1 0 0 2 
152 1 1 1 72 1 1 99 5 0 0 0 0 1 0 3 
152 3 3 1 39 1 1 99 5 0 0 0 0 1 0 3 
152 2 2 2 69 1 1 99 5 0 0 0 0 1 0 3 
153 1 1 1 28 1 6 49 2 0 1 0 0 0 0 2 
153 2 2 2 27 1 6 49 2 0 1 0 0 0 0 2 
155 1 1 2 54 1 6 27 5 0 0 0 0 1 0 1 
155 2 3 2 29 1 6 27 5 0 0 0 0 1 0 1 
156 1 1 1 48 1 1 76 3 0 0 1 0 0 0 3 
156 3 3 1 17 1 1 76 3 0 0 1 0 0 0 3 
156 2 2 2 43 1 1 76 3 0 0 1 0 0 0 3 
158 1 1 1 52 1 4 50 5 0 0 0 0 1 0 2 
158 3 3 1 23 1 4 50 5 0 0 0 0 1 0 2 
158 2 2 2 47 1 4 50 5 0 0 0 0 1 0 2 
158 4 3 2 20 1 4 50 5 0 0 0 0 1 0 2 
159 1 1 1 54 1 6 40 5 0 0 0 0 1 0 2 
159 3 3 1 30 1 6 40 5 0 0 0 0 1 0 2 
159 2 2 2 51 1 6 40 5 0 0 0 0 1 0 2 
159 4 3 2 28 1 6 40 5 0 0 0 0 1 0 2 
160 1 1 1 35 1 4 36 2 0 1 0 0 0 0 2 
160 2 2 2 34 1 4 36 2 0 1 0 0 0 0 2 
162 1 1 1 74 1 1 76 6 0 0 0 0 0 1 3 
162 2 2 2 74 1 1 76 6 0 0 0 0 0 1 3 
163 3 3 1 16 1 4 40 4 0 0 0 1 0 0 2 
163 1 1 2 44 1 4 40 4 0 0 0 1 0 0 2 
163 2 3 2 20 1 4 40 4 0 0 0 1 0 0 2 
165 1 1 1 72 2 1 114 6 0 0 0 0 0 1 4 
165 2 2 2 69 2 1 114 6 0 0 0 0 0 1 4 
166 1 1 1 59 2 4 114 5 0 0 0 0 1 0 4 
166 3 3 1 26 2 4 114 5 0 0 0 0 1 0 4 
166 2 2 2 54 2 4 114 5 0 0 0 0 1 0 4 
166 4 3 2 28 2 4 114 5 0 0 0 0 1 0 4 
168 1 1 1 55 2 1 114 5 0 0 0 0 1 0 4 
168 3 3 1 28 2 1 114 5 0 0 0 0 1 0 4 
168 2 2 2 49 2 1 114 5 0 0 0 0 1 0 4 
168 4 3 2 26 2 1 114 5 0 0 0 0 1 0 4 
171 1 1 1 57 2 1 114 5 0 0 0 0 1 0 4 
171 3 3 1 26 2 1 114 5 0 0 0 0 1 0 4 
171 2 2 2 50 2 1 114 5 0 0 0 0 1 0 4 
172 1 1 1 58 2 1 114 5 0 0 0 0 1 0 4 
172 3 3 1 22 2 1 114 5 0 0 0 0 1 0 4 
172 4 3 1 25 2 1 114 5 0 0 0 0 1 0 4 
172 2 2 2 53 2 1 114 5 0 0 0 0 1 0 4 
172 5 3 2 27 2 1 114 5 0 0 0 0 1 0 4 
173 1 1 1 45 3 4 59 3 0 0 1 0 0 0 2 
173 3 3 1 12 3 4 59 3 0 0 1 0 0 0 2 
173 2 2 2 33 3 4 59 3 0 0 1 0 0 0 2 
174 1 1 2 80 3 1 59 6 0 0 0 0 0 1 2 
175 1 1 1 71 3 1 59 5 0 0 0 0 1 0 2 
175 2 2 2 67 3 1 59 5 0 0 0 0 1 0 2 
175 3 3 2 28 3 1 59 5 0 0 0 0 1 0 2 
176 1 1 1 55 3 1 59 4 0 0 0 1 0 0 2 
176 3 3 1 25 3 1 59 4 0 0 0 1 0 0 2 
176 2 2 2 50 3 1 59 4 0 0 0 1 0 0 2 
176 4 3 2 17 3 1 59 4 0 0 0 1 0 0 2 
178 2 2 1 60 3 1 59 5 0 0 0 0 1 0 2 
178 3 3 1 26 3 1 59 5 0 0 0 0 1 0 2 
178 4 3 1 23 3 1 59 5 0 0 0 0 1 0 2 
178 1 1 2 50 3 1 59 5 0 0 0 0 1 0 2 
179 1 1 1 52 3 1 59 4 0 0 0 1 0 0 2 
179 3 3 1 18 3 1 59 4 0 0 0 1 0 0 2 
179 2 2 2 43 3 1 59 4 0 0 0 1 0 0 2 
179 4 3 2 17 3 1 59 4 0 0 0 1 0 0 2 
180 1 1 1 60 3 1 59 5 0 0 0 0 1 0 2 
180 3 3 1 27 3 1 59 5 0 0 0 0 1 0 2 
180 2 2 2 60 3 1 59 5 0 0 0 0 1 0 2 
182 1 1 2 47 4 1 53 3 0 0 1 0 0 0 2 
182 2 3 2 10 4 1 53 3 0 0 1 0 0 0 2 
184 1 1 1 36 4 4 59 2 0 1 0 0 0 0 2 
184 2 2 2 38 4 4 59 2 0 1 0 0 0 0 2 
185 1 1 1 43 4 1 59 3 0 0 1 0 0 0 2 
185 3 3 1 15 4 1 59 3 0 0 1 0 0 0 2 
185 4 3 1 11 4 1 59 3 0 0 1 0 0 0 2 
185 2 2 2 41 4 1 59 3 0 0 1 0 0 0 2 
186 1 1 1 36 4 1 50 2 0 1 0 0 0 0 2 
186 2 2 2 32 4 1 50 2 0 1 0 0 0 0 2 
186 5 3 2 10 4 1 50 2 0 1 0 0 0 0 2 
187 1 1 2 55 4 1 53 5 0 0 0 0 1 0 2 
187 2 3 2 25 4 1 53 5 0 0 0 0 1 0 2 
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来源

2012-04-17 TaeHeon Kim

内存问题？您可能不得不使用交换文件进行更大的分析。 – James 2012-04-17 09:08:54

在此过程中，您可以保存每个城市的结果以分开文件并从内存中删除这些数据。 – DrDom 2012-04-17 09:14:59

请提供一个可重现的例子，否则我们会猜测究竟发生了什么问题。 – 2012-04-17 09:50:42

我的理解是，R是作为copy-on-write来实现的。

就拿：

X <- 1:1000 
y <- x  # no copy made. x & y share the reference 
y[1] <- 8 # now a copy is made.

如果x越来越大，拷贝花费更长的时间较长，而且脚本减慢。我并不熟悉宏变量，但我怀疑像这样的事情正在发生。如果您使用的是列表，每次将新元素添加到列表中时，都会被重新复制。

y <- list() 
y$this <- 1:5 
y$that <- 6:10 # now every thing was recopied.

因此，最好先分配。它确实有助于表现。

y <- vector("list", 2) 
y[[1]] <- 1:5 
y[[2]] <- 6:10

这些幻灯片真的不错：a link看一看！

来源

2012-05-24 04:03:48

平均处理速度越来越慢

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