Getting_Asses_results: Get the result of function Assessment_via_cluster

In bishun945/FCMm: Fuzzy Cluster Method Based on the Optimized m Value (Fuzzifier)

Description

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.

Usage

Getting_Asses_results(
  sample.size,
  replace = FALSE,
  pred,
  meas,
  memb,
  metrics_used = 1,
  cluster = apply(memb, 1, which.max),
  seed = NULL,
  log10 = TRUE,
  valid.definition = list(negative = FALSE, percent = 0.6)
)

Arguments

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 FALSE
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 1. If metrics_used = 1 then the used metrics are c("MAE", "CMAPE", "BIAS", "CMRPE") If metrics_used = 2 then the used metrics are c("MAE", "CMAPE", "BIAS", "CMRPE", "RATIO")
cluster	Cluster vector. Could be calculated by the parameter memb. Will be deprecated later.
seed	Seed number for fixing the random process. See help(set.seed) for more details.
log10	pass to Sampling_by_sort.
valid.definition	The definition of valid prediction, default as list(negative=FALSE, percent = 0.6). The invalid prediction will not be removed to calculate error metrics. negative=FALSE means the negative values are considered as invalid predictions while percent means the tolerance of error-percentage that only the predictions between (y-y*percent), y+y*percent) are valid.

Value

A list containing fuzzy and hard results from Assessment_via_cluster

Note

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.

See Also

Other Algorithm assessment: Assessment_via_cluster(), Sampling_via_cluster(), Score_algorithms_interval(), Score_algorithms_sort(), Scoring_system()

Examples

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)

bishun945/FCMm documentation built on Oct. 15, 2021, 6:43 p.m.