Description Usage Arguments Value Note See Also Examples
View source: R/Algorithms_assessment.R
This function mainly use function Assessment_via_cluster to get
assessments both from fuzzy and hard mode. Specifically, it will return the accuracy and precision of
MAE
,CMAPE
,BIAS
, and CMRPE
which would be seemed as the input value of
function Scoring_system.
1 2 3 4 5 6 7 8 9 10 11 12 |
sample.size |
Sample size. This supports a bootstrap way to run the function Assessment_via_cluster. The number should not be larger than the row number of pred or so. |
replace |
Logical, replace, default as |
pred |
Prediction matrix or data.frame |
meas |
Measured (actual) matrix or data.frame |
memb |
Membership matrix |
metrics_used |
The metric combination used in the function. Default is If If |
cluster |
Cluster vector. Could be calculated by the parameter |
seed |
Seed number for fixing the random process. See |
log10 |
pass to Sampling_by_sort. |
valid.definition |
The definition of valid prediction, default as
|
A list containing fuzzy and hard results from Assessment_via_cluster
The row number of pred
, meas
, memb
, and cluster
should be the same.
This function is designed for bootstrapping process to get Chla algorithms assessment. Therefore,
parameters of Assessment_via_cluster is set as fixed such as log10 = TRUE
,
na.process = TRUE
. Given that, I will not export this function in latter to avoid confuses.
Other Algorithm assessment:
Assessment_via_cluster()
,
Sampling_via_cluster()
,
Score_algorithms_interval()
,
Score_algorithms_sort()
,
Scoring_system()
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 | library(FCMm)
library(ggplot2)
library(magrittr)
library(stringr)
data("Nechad2015")
x <- Nechad2015[,3:11]
wv <- gsub("X","",names(x)) %>% as.numeric
set.seed(1234)
w <- sample.int(nrow(x), 300)
x <- x[w, ]
names(x) <- wv
nb = 4 # Obtained from the vignette "Cluster a new dataset by FCMm"
set.seed(1234)
FD <- FuzzifierDetermination(x, wv, do.stand=TRUE)
result <- FCM.new(FD, nb, fast.mode = TRUE)
p.spec <- plot_spec(result, show.stand=TRUE)
print(p.spec$p.cluster.spec)
Chla <- Nechad2015$X.Chl_a..ug.L.[w]
Chla[Chla >= 999] <- NA
dt_Chla <- run_all_Chla_algorithms(x) %>% as.data.frame
dt_Chla <- data.frame(Chla_true = Chla,
BR_Gil10 = dt_Chla$BR_Gil10,
OC4_OLCI = dt_Chla$OC4_OLCI,
OCI_Hu12 = dt_Chla$OCI_Hu12,
NDCI_Mi12= dt_Chla$NDCI_Mi12) %>% round(3)
w = which(!is.na(dt_Chla$Chla_true))
dt_Chla = dt_Chla[w,]
memb = result$res.FCM$u[w,] %>% round(4)
cluster = result$res.FCM$cluster[w]
Asses_results <- Getting_Asses_results(sample.size=length(cluster),
pred = dt_Chla[,-1], meas = data.frame(dt_Chla[,1]), memb = memb,
cluster = cluster)
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