-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathCmp_Algorithms.R
139 lines (117 loc) · 5.12 KB
/
Cmp_Algorithms.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
## Step.2
## Use nine algorithms to get the results.
## The nine algorithms are used to experiment with the data,
## including gs,hc,iamb,mmpc,rsmax2,tabu,fastiamb,interiamb,mmhc.
library(bnlearn)
library(igraph)
library(Rgraphviz)
## Function : CompareAlgorithm
## methodd : discrete method.
## dimsize : discrete value.
## sourcee : input data / Source data.
## cmpdata : the right/standard dataset.
## outfile : the path of output file.
CompareAlgorithm <- function(sourcee,cmpdata,methodd,dimsize,outfile){
mydata <- read.table(sourcee, header = FALSE)
Alarm1_graph <- read.table(cmpdata)
store_text <- outfile
t <- data.frame(matrix(as.numeric(unlist(mydata)),ncol = length(mydata[1,])))
rownames(t) <- rownames(mydata)
colnames(t) <- colnames(mydata)
mydata <- t
## discretize.
mydata <- discretize(mydata,method = 'interval',breaks = 7) #ÀëÉ¢»¯
rownames(Alarm1_graph) <- colnames(Alarm1_graph)
Alarm1_graph <- data.frame(t(Alarm1_graph))
Alarm1_graph <- as.matrix(Alarm1_graph)
g1 <- graph_from_adjacency_matrix(Alarm1_graph)
## Standardization of standard datasets.
result2 <- as_edgelist(g1, names = TRUE)
paste_12 <- function(result){
string1 <- c()
for(i in 1:length(result[,1])){
t <- sort(c(result[i,1],result[i,2]))
t <- paste(t[1],t[2])
string1 <- c(string1,t)
}
string1 <- union(string1,string1)
}
string1 <- paste_12(result2)
## Execute algorithm 'gs', save time and compare with standard set.
timestart4 <- Sys.time()
gs_2 <- gs(mydata)
timeend4 <- Sys.time()
time_gs_2 <- c(timeend4-timestart4)
string_gs_2 <- paste_12(gs_2$arcs)
length(intersect(string1, string_gs_2))
## Execute algorithm 'hc', save time and compare with standard set.
timestart1 <- Sys.time()
hc_2 <- hc(mydata)
timeend1 <- Sys.time()
time_hc_2 <- c(timeend1-timestart1)
string_hc_2 <- paste_12(hc_2$arcs)
length(intersect(string1, string_hc_2))
## Execute algorithm 'iamb', save time and compare with standard set.
timestart5 <- Sys.time()
iamb_2 <- iamb(mydata)
timeend5 <- Sys.time()
time_iamb_2 <- c(timeend5-timestart5)
string_iamb_2 <- paste_12(iamb_2$arcs)
length(intersect(string1, string_iamb_2))
## Execute algorithm 'mmpc', save time and compare with standard set.
timestart8 <- Sys.time()
mmpc_2 <- mmpc(mydata)
timeend8 <- Sys.time()
time_mmpc_2 <- c(timeend8-timestart8)
string_mmpc_2 <- paste_12(mmpc_2$arcs)
length(intersect(string1, string_mmpc_2))
## Execute algorithm 'rsmax2', save time and compare with standard set.
timestart9 <- Sys.time()
rsmax2_2 <- rsmax2(mydata)
timeend9 <- Sys.time()
time_rsmax2_2 <- c(timeend9-timestart9)
string_rsmax2_2 <- paste_12(rsmax2_2$arcs)
length(intersect(string1, string_rsmax2_2))
## Execute algorithm 'tabu', save time and compare with standard set.
timestart2 <- Sys.time()
tabu_2 <- tabu(mydata)
timeend2 <- Sys.time()
time_tabu_2 <- c(timeend2-timestart2)
string_tabu_2 <- paste_12(tabu_2$arcs)
length(intersect(string1, string_tabu_2))
## Execute algorithm 'fastiamb', save time and compare with standard set.
timestart6 <- Sys.time()
fastiamb_2 <- fast.iamb(mydata)
timeend6 <- Sys.time()
time_fastiamb_2 <- c(timeend6-timestart6)
string_fastiamb_2 <- paste_12(fastiamb_2$arcs)
length(intersect(string1, string_fastiamb_2))
## Execute algorithm 'interiamb', save time and compare with standard set.
timestart7 <- Sys.time()
interiamb_2 <- inter.iamb(mydata)
timeend7 <- Sys.time()
time_interiamb_2 <- c(timeend7-timestart7)
string_interiamb_2 <- paste_12(interiamb_2$arcs)
length(intersect(string1, string_interiamb_2))
## Execute algorithm 'mmhc', save time and compare with standard set.
timestart3 <- Sys.time()
mmhc_2 <- mmhc(mydata)
timeend3 <- Sys.time()
time_mmhc_2 <- c(timeend3-timestart3)
string_mmhc_2 <- paste_12(mmhc_2$arcs)
length(intersect(string1, string_mmhc_2))
## Store the comparison results.
write.table(TEST_DATA,append = TRUE,file = store_text ,row.names = F, quote = F)
d <- data.frame(
gs = c( length(string_gs_2) ,length(intersect(string1, string_gs_2))),
hc = c( length(string_hc_2) ,length(intersect(string1, string_hc_2))),
iamb = c( length(string_iamb_2) ,length(intersect(string1, string_iamb_2))),
mmpc = c( length(string_mmpc_2) ,length(intersect(string1, string_mmpc_2))),
rsmax = c( length(string_rsmax2_2) ,length(intersect(string1, string_rsmax2_2))),
tabu = c( length(string_tabu_2) ,length(intersect(string1, string_tabu_2))),
fastiamb = c( length(string_fastiamb_2) ,length(intersect(string1, string_fastiamb_2))),
interiamb = c( length(string_interiamb_2) ,length(intersect(string1, string_interiamb_2))),
mmhc = c( length(string_mmhc_2) ,length(intersect(string1, string_mmhc_2)) )
)
write.table(d, append = TRUE,file = store_text, row.names = F, quote = F,sep = "\t")
}