Assume we have the following function :
library("doBy")
library("FNN")
x=-0.5 1:6
y=c(0.5,1.5)
z=y
fct_3<function(x,y,z,Alpha=0.6,Max_N_sensors=4,N_targets=7,printing=TRUE,summarized_result=TRUE){
distance=as.matrix(dist(expand.grid(x,y,z),diag = 1,upper = 1))
P=as.matrix(Alpha*exp(-Alpha*distance))
print(c("P dimensions :",dim(P)))
if(Max_N_sensors>=nrow(P)|| N_targets>=nrow(P) || Max_N_sensors N_targets>nrow(P) ){
print("Error : incorrect inputs ")
return(0)
}
diag(P)<-0
rownames(P)<-paste("Sensor_location",1:nrow(P))
colnames(P)<-paste("Target_location",1:nrow(P))
R=P
N_sensors=0
Sensors_Positions=c()
kn=get.knnx(P,rbind(apply(P,2,max)), k=Max_N_sensors)
if(printing==TRUE){
print("KNN")
print(kn)
}
while (N_sensors<=Max_N_sensors) {
N_sensors=N_sensors 1
r_ind=kn$nn.index[N_sensors]
r_val=mean(P[r_ind,])
Sensors_Positions=c(Sensors_Positions,r_ind)
if(printing==TRUE){
print(c("Selected Sensor",r_ind , "Total of used sensors",N_sensors))
print("Sensors_Positions")
print(Sensors_Positions)
}
if(N_sensors==Max_N_sensors){
means=apply(P,1,mean)
Sensors_Positions=Sensors_Positions[!is.na(Sensors_Positions)]
Targets_locations=setdiff(1:nrow(P),Sensors_Positions)
if(printing==TRUE){
print("Possible targets locations")
print(Targets_locations)
}
R[Targets_locations,]=0
R[,Sensors_Positions]=0
R=cbind(R,cumulative_sensor_detection=apply(R, 1,function(x) 1-prod(1-x[x!=0 & !is.na(x)]) ))
R=rbind(R,cumulattive_detection_by_Target=apply(R,2,function(x) 1-prod(1-x[x!=0 & !is.na(x)])))
Targets_Positions=sort(R["cumulattive_detection_by_Target",][1:nrow(P)],decreasing = TRUE,index.return=TRUE)$ix[1:N_targets]
result=list(R,K_nearests=kn,N_sensors=Max_N_sensors,sensors_positions=Sensors_Positions,Targets_locations=Targets_Positions,Targets_cum_detection=R[nrow(R),Targets_Positions])
if(summarized_result==TRUE){
result=list(N_sensors=Max_N_sensors,sensors_positions=Sensors_Positions,Targets_locations=Targets_Positions,Targets_cum_detection=R[nrow(R),Targets_Positions])
}
return(result)
}
}
}
An example of a run :
list_1=fct_3(x,y,z,Alpha=0.4362923,Max_N_sensors=14,N_targets=2,printing=FALSE,summarized_result=TRUE)
What interest me the more is the 4th element of the returned list. I need it to draw some curves :
list_1[[4]],list_1[["Targets_cum_detection"]]
In the other hand , i have this combination of arguments / parameters :
parameters=expand.grid(Alpha=seq(0,1,0.1),Max_N_sensors=1:22,N_targets=2)
nrow(parameters)
[1] 242
Sorting by Alpha :
parameters=parameters[order(parameters[,1],decreasing=FALSE),]
Request :
Each row of parameters represent a run. Examples :
1/ First row :
list_1=fct_3(x,y,z,Alpha=parameters[1,1],Max_N_sensors=parameters[1,2],N_targets=parameters[1,3],printing=FALSE,summarized_result=TRUE)
2/ Second row :
list_2=fct_3(x,y,z,Alpha=parameters[2,1],Max_N_sensors=parameters[2,2],N_targets=parameters[2,3],printing=FALSE,summarized_result=TRUE)
etc...
I need to build the list of all runs based on parameters.
I tried without success :
lapply(1:nrow(parameters), function(x) return(fct_3(x,y,z,Alpha=parameters[x,1],Max_N_sensors=parameters[x,2],N_targets=2,printing=FALSE,summarized_result=TRUE)))
Doing this gives :
list_of_runs=lapply(1:nrow(parameters), function(x) return(fct_3(x,y,z,Alpha=parameters[x,1],Max_N_sensors=parameters[x,2],N_targets=2,printing=FALSE,summarized_result=TRUE)))
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
[1] "Error : incorrect inputs "
For example , you will see that
list_of_runs[[242]]
[1] 0
However we obtain with the associated parameters[242,] the following results, list_of_runs[[242]] should be this :
fct_3(x,y,z,Alpha=parameters[242,1],Max_N_sensors=parameters[242,2],N_targets=2,printing=FALSE,summarized_result=TRUE)
$N_sensors
[1] 22
$sensors_positions
[1] 9 10 15 16 4 21 3 22 8 14 11 5 17 23 2 20 13 7 18 12 1 24
$Targets_locations
[1] 6 19
$Targets_cum_detection
Target_location 6 Target_location 19
0.956163 0.956163
Hope my question clear and feasible.
Thanks before.
CodePudding user response：
The problem is already solved but here is a apply version. It avoids having to create the results list beforehand.
x <- -0.5 1:6
y <- c(0.5, 1.5)
z <- y
parameters <- expand.grid(Alpha = seq(0, 1, 0.1),
Max_N_sensors = 1:22,
N_targets = 2)
parameters <- parameters[order(parameters[, 1], decreasing = FALSE), ]
list_of_runs <- apply(parameters, 1, \(params) {
fct_3(x, y, z,
Alpha = params[1],
Max_N_sensors = params[2],
N_targets = params[3],
printing = FALSE,
summarized_result = TRUE)
})
#> P dimensions : 24 24
#> P dimensions : 24 24
#> P dimensions : 24 24
#> [omitted]
#> P dimensions : 24 24
list_of_runs[[242]]
#> $N_sensors
#> Max_N_sensors
#> 22
#>
#> $sensors_positions
#> [1] 9 10 15 16 4 21 3 22 8 14 11 5 17 23 2 20 13 7 18 12 1 24
#>
#> $Targets_locations
#> [1] 6 19
#>
#> $Targets_cum_detection
#> Target_location 6 Target_location 19
#> 0.956163 0.956163
Created on 2022-09-03 by the reprex package (v2.0.1)
CodePudding user response：
Problem solved.
I just tried :
list_of_runs=list()
for(i in 1:nrow(parameters)){
list_of_runs[[i]]=fct_3(x,y,z,Alpha=parameters[i,1],Max_N_sensors=parameters[i,2],N_targets=2,printing=FALSE,summarized_result=TRUE)
}
This gives the correct output:
list_of_runs[[242]]
$N_sensors
[1] 22
$sensors_positions
[1] 9 10 15 16 4 21 3 22 8 14 11 5 17 23 2 20 13 7 18 12 1 24
$Targets_locations
[1] 6 19
$Targets_cum_detection
Target_location 6 Target_location 19
0.956163 0.956163