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v1.0.4.1 function Calibration_Michel() cleaned

parent 4b4c4aee
dev 115-add-pcict-date-format-managment 118-plot-outputsmodel-remove-case-sensitivity-to-the-which-argument 136-decreased-performance-of-calibration-execution-time 161-runmodel_lag-proposal-for-a-better-transformation-and-screening-of-the-parameters 165-add-missing-isintstore-argument-in-createcaliboptions 171-check-error-direct-call-of-as-data-frame-posixct-is-deprecated-use-as-data-frame-vector-or-as 191-speed-up-runmodel_lag-when-it-is-used-alone 33-merge-airgr-and-airgrplus-functions 60-migrate-transfoparam-functions-and-createcaliboptions-to-s3-methods 96-parallelize-the-grid-screening-step-during-calibration airGRplus master rmOptions CRAN_v1.7.6 CRAN_v1.7.5 CRAN_v1.7.4 CRAN_v1.7.3 CRAN_v1.7.2 CRAN_v1.7.1 CRAN_v1.7.0 CRAN_v1.6.12 CRAN_v1.6.11 CRAN_v1.6.10.4 CRAN_v1.6.9.27 CRAN_v1.6.9.24 CRAN_v1.6.9.23 CRAN_v1.6.9.21 CRAN_v1.4.3.65 CRAN_v1.4.3.60 CRAN_v1.4.3.52 CRAN_v1.3.2.42 CRAN_v1.3.2.41 CRAN_v1.3.2.40 CRAN_v1.3.2.39 CRAN_v1.3.2.23 CRAN_v1.3.2.17 CRAN_v1.2.13.16 CRAN_v1.2.13.13 CRAN_v1.0.14.1 CRAN_v1.0.13.19 CRAN_v1.0.10.11 CRAN_v1.0.9.64 CRAN_v1.0.5.12 CRAN_v1.0.5.11 CRAN_v1.0.5.10
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DESCRIPTION
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View file @ 336d5e9b
Package: airGR
Type: Package
Title: Suite of GR hydrological models for precipitation-runoff modelling
Version: 1.0.3
Date: 2016-12-09
Version: 1.0.4.1
Date: 2017-01-18
Authors@R: c(
person("Laurent", "Coron", role = c("aut", "trl")),
person("Charles", "Perrin", role = c("aut", "ths")),
......
R/Calibration_Michel.R
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− 202
View file @ 336d5e9b
Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FUN_MOD,FUN_CRIT,FUN_TRANSFO=NULL, verbose = TRUE){
Calibration_Michel <- function(InputsModel, RunOptions, InputsCrit, CalibOptions, FUN_MOD, FUN_CRIT, FUN_TRANSFO = NULL, verbose = TRUE) {
##_____Arguments_check_____________________________________________________________________
if(inherits(InputsModel,"InputsModel")==FALSE){ stop("InputsModel must be of class 'InputsModel' \n"); return(NULL); }
if(inherits(RunOptions,"RunOptions")==FALSE){ stop("RunOptions must be of class 'RunOptions' \n"); return(NULL); }
if(inherits(InputsCrit,"InputsCrit")==FALSE){ stop("InputsCrit must be of class 'InputsCrit' \n"); return(NULL); }
if(inherits(CalibOptions,"CalibOptions")==FALSE){ stop("CalibOptions must be of class 'CalibOptions' \n"); return(NULL); }
if(inherits(CalibOptions,"HBAN")==FALSE){ stop("CalibOptions must be of class 'HBAN' if Calibration_Michel is used \n"); return(NULL); }
if (!inherits(InputsModel, "InputsModel")) {
stop("InputsModel must be of class 'InputsModel' \n")
return(NULL)
}
if (!inherits(RunOptions, "RunOptions")) {
stop("RunOptions must be of class 'RunOptions' \n")
return(NULL)
}
if (!inherits(InputsCrit, "InputsCrit")) {
stop("InputsCrit must be of class 'InputsCrit' \n")
return(NULL)
}
if (!inherits(CalibOptions, "CalibOptions")) {
stop("CalibOptions must be of class 'CalibOptions' \n")
return(NULL)
}
if (!inherits(CalibOptions, "HBAN")) {
stop("CalibOptions must be of class 'HBAN' if Calibration_Michel is used \n")
return(NULL)
}
##_check_FUN_TRANSFO
if(is.null(FUN_TRANSFO)){
if(identical(FUN_MOD,RunModel_GR4H )){ FUN_TRANSFO <- TransfoParam_GR4H ; }
if(identical(FUN_MOD,RunModel_GR4J )){ FUN_TRANSFO <- TransfoParam_GR4J ; }
if(identical(FUN_MOD,RunModel_GR5J )){ FUN_TRANSFO <- TransfoParam_GR5J ; }
if(identical(FUN_MOD,RunModel_GR6J )){ FUN_TRANSFO <- TransfoParam_GR6J ; }
if(identical(FUN_MOD,RunModel_GR2M )){ FUN_TRANSFO <- TransfoParam_GR2M ; }
if(identical(FUN_MOD,RunModel_GR1A )){ FUN_TRANSFO <- TransfoParam_GR1A ; }
if(identical(FUN_MOD,RunModel_CemaNeige )){ FUN_TRANSFO <- TransfoParam_CemaNeige; }
if(identical(FUN_MOD,RunModel_CemaNeigeGR4J) | identical(FUN_MOD,RunModel_CemaNeigeGR5J) | identical(FUN_MOD,RunModel_CemaNeigeGR6J)){
if(identical(FUN_MOD,RunModel_CemaNeigeGR4J)){ FUN1 <- TransfoParam_GR4J; FUN2 <- TransfoParam_CemaNeige; }
if(identical(FUN_MOD,RunModel_CemaNeigeGR5J)){ FUN1 <- TransfoParam_GR5J; FUN2 <- TransfoParam_CemaNeige; }
if(identical(FUN_MOD,RunModel_CemaNeigeGR6J)){ FUN1 <- TransfoParam_GR6J; FUN2 <- TransfoParam_CemaNeige; }
FUN_TRANSFO <- function(ParamIn,Direction){
Bool <- is.matrix(ParamIn);
if(Bool==FALSE){ ParamIn <- rbind(ParamIn); }
ParamOut <- NA*ParamIn;
NParam <- ncol(ParamIn);
ParamOut[, 1:(NParam-2)] <- FUN1(ParamIn[, 1:(NParam-2)],Direction);
ParamOut[,(NParam-1):NParam ] <- FUN2(ParamIn[,(NParam-1):NParam ],Direction);
if(Bool==FALSE){ ParamOut <- ParamOut[1,]; }
return(ParamOut);
if (is.null(FUN_TRANSFO)) {
if (identical(FUN_MOD, RunModel_GR4H )) {
FUN_TRANSFO <- TransfoParam_GR4H
}
if (identical(FUN_MOD, RunModel_GR4J )) {
FUN_TRANSFO <- TransfoParam_GR4J
}
if (identical(FUN_MOD, RunModel_GR5J )) {
FUN_TRANSFO <- TransfoParam_GR5J
}
if (identical(FUN_MOD, RunModel_GR6J )) {
FUN_TRANSFO <- TransfoParam_GR6J
}
if (identical(FUN_MOD, RunModel_GR2M )) {
FUN_TRANSFO <- TransfoParam_GR2M
}
if (identical(FUN_MOD, RunModel_GR1A )) {
FUN_TRANSFO <- TransfoParam_GR1A
}
if (identical(FUN_MOD, RunModel_CemaNeige )) {
FUN_TRANSFO <- TransfoParam_CemaNeige
}
if (identical(FUN_MOD, RunModel_CemaNeigeGR4J) | identical(FUN_MOD, RunModel_CemaNeigeGR5J) | identical(FUN_MOD, RunModel_CemaNeigeGR6J)) {
if (identical(FUN_MOD, RunModel_CemaNeigeGR4J)) {
FUN1 <- TransfoParam_GR4J
FUN2 <- TransfoParam_CemaNeige
}
if (identical(FUN_MOD, RunModel_CemaNeigeGR5J)) {
FUN1 <- TransfoParam_GR5J
FUN2 <- TransfoParam_CemaNeige
}
if (identical(FUN_MOD,RunModel_CemaNeigeGR6J)) {
FUN1 <- TransfoParam_GR6J
FUN2 <- TransfoParam_CemaNeige
}
FUN_TRANSFO <- function(ParamIn, Direction) {
Bool <- is.matrix(ParamIn)
if (Bool == FALSE) {
ParamIn <- rbind(ParamIn)
}
ParamOut <- NA*ParamIn
NParam <- ncol(ParamIn)
ParamOut[, 1:(NParam-2)] <- FUN1(ParamIn[, 1:(NParam-2)], Direction)
ParamOut[, (NParam-1):NParam ] <- FUN2(ParamIn[, (NParam-1):NParam ], Direction)
if (Bool == FALSE) {
ParamOut <- ParamOut[1, ]
}
return(ParamOut)
}
}
if (is.null(FUN_TRANSFO)) {
stop("FUN_TRANSFO was not found (in Calibration function) \n")
return(NULL)
}
if(is.null(FUN_TRANSFO)){ stop("FUN_TRANSFO was not found (in Calibration function) \n"); return(NULL); }
}
##_variables_initialisation
ParamFinalR <- NULL; ParamFinalT <- NULL; CritFinal <- NULL;
NRuns <- 0; NIter <- 0;
if("StartParamDistrib" %in% names(CalibOptions)){ PrefilteringType <- 2; } else { PrefilteringType <- 1; }
if(PrefilteringType==1){ NParam <- ncol(CalibOptions$StartParamList); }
if(PrefilteringType==2){ NParam <- ncol(CalibOptions$StartParamDistrib); }
if(NParam>20){ stop("Calibration_Michel can handle a maximum of 20 parameters \n"); return(NULL); }
HistParamR <- matrix(NA,nrow=500*NParam,ncol=NParam);
HistParamT <- matrix(NA,nrow=500*NParam,ncol=NParam);
HistCrit <- matrix(NA,nrow=500*NParam,ncol=1);
CritName <- NULL;
CritBestValue <- NULL;
Multiplier <- NULL;
CritOptim <- +1E100;
ParamFinalR <- NULL
ParamFinalT <- NULL
CritFinal <- NULL
NRuns <- 0
NIter <- 0
if ("StartParamDistrib" %in% names(CalibOptions)) {
PrefilteringType <- 2
} else {
PrefilteringType <- 1
}
if (PrefilteringType == 1) {
NParam <- ncol(CalibOptions$StartParamList)
}
if (PrefilteringType == 2) {
NParam <- ncol(CalibOptions$StartParamDistrib)
}
if (NParam > 20) {
stop("Calibration_Michel can handle a maximum of 20 parameters \n")
return(NULL)
}
HistParamR <- matrix(NA, nrow = 500*NParam, ncol = NParam)
HistParamT <- matrix(NA, nrow = 500*NParam, ncol = NParam)
HistCrit <- matrix(NA, nrow = 500*NParam, ncol = 1)
CritName <- NULL
CritBestValue <- NULL
Multiplier <- NULL
CritOptim <- +1E100
##_temporary_change_of_Outputs_Sim
RunOptions$Outputs_Sim <- RunOptions$Outputs_Cal; ### this reduces the size of the matrix exchange with fortran and therefore speeds the calibration
RunOptions$Outputs_Sim <- RunOptions$Outputs_Cal ### this reduces the size of the matrix exchange with fortran and therefore speeds the calibration
......@@ -59,98 +118,128 @@ Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FU
##Definition_of_the_function_creating_all_possible_parameter_sets_from_different_values_for_each_parameter
ProposeCandidatesGrid <- function(DistribParam){
ProposeCandidatesGrid <- function(DistribParam) {
##Managing_matrix_sizes
Nvalmax <- nrow(DistribParam);
NParam <- ncol(DistribParam);
Nvalmax <- nrow(DistribParam)
NParam <- ncol(DistribParam)
##we_add_columns_to_MatDistrib_until_it_has_20_columns
DistribParam2 <- matrix(NA,nrow=Nvalmax,ncol=20);
DistribParam2[1:Nvalmax,1:NParam] <- DistribParam;
DistribParam2 <- matrix(NA, nrow = Nvalmax, ncol = 20)
DistribParam2[1:Nvalmax, 1:NParam] <- DistribParam
##we_check_the_number_of_values_to_test_for_each_param
NbDistrib <- rep(1,20);
for(iC in 1:20){ NbDistrib[iC] <- max( 1 , Nvalmax-sum(is.na(DistribParam2[,iC])) ); }
NbDistrib <- rep(1, 20)
for (iC in 1:20) {
NbDistrib[iC] <- max(1, Nvalmax-sum(is.na(DistribParam2[, iC])))
}
##Loop_on_the_various_values_to_test ###(if 4 param and 3 values for each => 3^4 sets)
##NB_we_always_do_20_loops ###which_is_here_the_max_number_of_param_that_can_be_optimised
VECT <- NULL;
for(iL01 in 1:NbDistrib[01]){ for(iL02 in 1:NbDistrib[02]){ for(iL03 in 1:NbDistrib[03]){ for(iL04 in 1:NbDistrib[04]){ for(iL05 in 1:NbDistrib[05]){
for(iL06 in 1:NbDistrib[06]){ for(iL07 in 1:NbDistrib[07]){ for(iL08 in 1:NbDistrib[08]){ for(iL09 in 1:NbDistrib[09]){ for(iL10 in 1:NbDistrib[10]){
for(iL11 in 1:NbDistrib[11]){ for(iL12 in 1:NbDistrib[12]){ for(iL13 in 1:NbDistrib[13]){ for(iL14 in 1:NbDistrib[14]){ for(iL15 in 1:NbDistrib[15]){
for(iL16 in 1:NbDistrib[16]){ for(iL17 in 1:NbDistrib[17]){ for(iL18 in 1:NbDistrib[18]){ for(iL19 in 1:NbDistrib[19]){ for(iL20 in 1:NbDistrib[20]){
VECT <- NULL
for (iL01 in 1:NbDistrib[01]) { for (iL02 in 1:NbDistrib[02]) { for (iL03 in 1:NbDistrib[03]) { for (iL04 in 1:NbDistrib[04]) { for (iL05 in 1:NbDistrib[05]) {
for (iL06 in 1:NbDistrib[06]) { for (iL07 in 1:NbDistrib[07]) { for (iL08 in 1:NbDistrib[08]) { for (iL09 in 1:NbDistrib[09]) { for (iL10 in 1:NbDistrib[10]) {
for (iL11 in 1:NbDistrib[11]) { for (iL12 in 1:NbDistrib[12]) { for (iL13 in 1:NbDistrib[13]) { for (iL14 in 1:NbDistrib[14]) { for (iL15 in 1:NbDistrib[15]) {
for (iL16 in 1:NbDistrib[16]) { for (iL17 in 1:NbDistrib[17]) { for (iL18 in 1:NbDistrib[18]) { for (iL19 in 1:NbDistrib[19]) { for (iL20 in 1:NbDistrib[20]) {
VECT <- c(VECT,
DistribParam2[iL01,01],DistribParam2[iL02,02],DistribParam2[iL03,03],DistribParam2[iL04,04],DistribParam2[iL05,05],
DistribParam2[iL06,06],DistribParam2[iL07,07],DistribParam2[iL08,08],DistribParam2[iL09,09],DistribParam2[iL10,10],
DistribParam2[iL11,11],DistribParam2[iL12,12],DistribParam2[iL13,13],DistribParam2[iL14,14],DistribParam2[iL15,15],
DistribParam2[iL16,16],DistribParam2[iL17,17],DistribParam2[iL18,18],DistribParam2[iL19,19],DistribParam2[iL20,20]);
DistribParam2[iL16,16],DistribParam2[iL17,17],DistribParam2[iL18,18],DistribParam2[iL19,19],DistribParam2[iL20,20])
} } } } }
} } } } }
} } } } }
} } } } }
MAT <- matrix(VECT,ncol=20,byrow=TRUE)[,1:NParam];
if(is.matrix(MAT)==FALSE){ MAT <- cbind(MAT); }
Output <- NULL;
Output$NewCandidates <- MAT;
return(Output);
MAT <- matrix(VECT, ncol = 20, byrow = TRUE)[, 1:NParam]
if (!is.matrix(MAT)) {
MAT <- cbind(MAT)
}
Output <- NULL
Output$NewCandidates <- MAT
return(Output)
}
##Creation_of_new_candidates_______________________________________________
OptimParam <- is.na(CalibOptions$FixedParam)
if(PrefilteringType==1){ CandidatesParamR <- CalibOptions$StartParamList; }
if(PrefilteringType==2){ DistribParamR <- CalibOptions$StartParamDistrib; DistribParamR[,!OptimParam] <- NA; CandidatesParamR <- ProposeCandidatesGrid(DistribParamR)$NewCandidates; }
if (PrefilteringType == 1) {
CandidatesParamR <- CalibOptions$StartParamList
}
if (PrefilteringType == 2) {
DistribParamR <- CalibOptions$StartParamDistrib
DistribParamR[,!OptimParam] <- NA
CandidatesParamR <- ProposeCandidatesGrid(DistribParamR)$NewCandidates
}
##Remplacement_of_non_optimised_values_____________________________________
CandidatesParamR <- apply(CandidatesParamR,1,function(x){ x[!OptimParam] <- CalibOptions$FixedParam[!OptimParam]; return(x); });
if(NParam>1){ CandidatesParamR <- t(CandidatesParamR); } else { CandidatesParamR <- cbind(CandidatesParamR); }
CandidatesParamR <- apply(CandidatesParamR, 1, function(x) {
x[!OptimParam] <- CalibOptions$FixedParam[!OptimParam]
return(x)})
if (NParam>1) {
CandidatesParamR <- t(CandidatesParamR)
} else { CandidatesParamR <- cbind(CandidatesParamR)
}
##Loop_to_test_the_various_candidates______________________________________
iNewOptim <- 0;
Ncandidates <- nrow(CandidatesParamR);
if(verbose & Ncandidates>1){
if(PrefilteringType==1){ message("List-Screening in progress (", appendLF = FALSE) }
if(PrefilteringType==2){ message("Grid-Screening in progress (", appendLF = FALSE) }
iNewOptim <- 0
Ncandidates <- nrow(CandidatesParamR)
if (verbose & Ncandidates > 1) {
if (PrefilteringType == 1) {
message("List-Screening in progress (", appendLF = FALSE)
}
if (PrefilteringType == 2) {
message("Grid-Screening in progress (", appendLF = FALSE)
}
message("0%", appendLF = FALSE)
}
for(iNew in 1:nrow(CandidatesParamR)){
if(verbose & Ncandidates>1){
for(k in c(2,4,6,8)){ if(iNew==round(k/10*Ncandidates)){ message(" ", 10*k, "%",appendLF = FALSE) } }
for (iNew in 1:nrow(CandidatesParamR)) {
if (verbose & Ncandidates > 1) {
for (k in c(2, 4, 6, 8)) {
if (iNew == round(k/10*Ncandidates)) {
message(" ", 10*k, "%", appendLF = FALSE)
}
}
}
##Model_run
Param <- CandidatesParamR[iNew,];
OutputsModel <- FUN_MOD(InputsModel,RunOptions,Param);
Param <- CandidatesParamR[iNew, ]
OutputsModel <- FUN_MOD(InputsModel, RunOptions, Param)
##Calibration_criterion_computation
OutputsCrit <- FUN_CRIT(InputsCrit,OutputsModel, verbose = FALSE);
if(!is.na(OutputsCrit$CritValue)){ if(OutputsCrit$CritValue*OutputsCrit$Multiplier < CritOptim){
CritOptim <- OutputsCrit$CritValue*OutputsCrit$Multiplier;
iNewOptim <- iNew;
} }
OutputsCrit <- FUN_CRIT(InputsCrit, OutputsModel, verbose = FALSE)
if (!is.na(OutputsCrit$CritValue)) {
if (OutputsCrit$CritValue*OutputsCrit$Multiplier < CritOptim) {
CritOptim <- OutputsCrit$CritValue*OutputsCrit$Multiplier
iNewOptim <- iNew
}
}
##Storage_of_crit_info
if(is.null(CritName) | is.null(CritBestValue) | is.null(Multiplier)){
CritName <- OutputsCrit$CritName;
CritBestValue <- OutputsCrit$CritBestValue;
Multiplier <- OutputsCrit$Multiplier;
if (is.null(CritName) | is.null(CritBestValue) | is.null(Multiplier)) {
CritName <- OutputsCrit$CritName
CritBestValue <- OutputsCrit$CritBestValue
Multiplier <- OutputsCrit$Multiplier
}
}
if(verbose & Ncandidates>1){ message(" 100%)\n", appendLF = FALSE) }
if (verbose & Ncandidates > 1) {
message(" 100%)\n", appendLF = FALSE)
}
##End_of_first_step_Parameter_Screening____________________________________
ParamStartR <- CandidatesParamR[iNewOptim,]; if(!is.matrix(ParamStartR)){ ParamStartR <- matrix(ParamStartR,nrow=1); }
ParamStartT <- FUN_TRANSFO(ParamStartR,"RT");
CritStart <- CritOptim;
NRuns <- NRuns+nrow(CandidatesParamR);
if(verbose){
if(Ncandidates> 1){
ParamStartR <- CandidatesParamR[iNewOptim, ]
if (!is.matrix(ParamStartR)) {
ParamStartR <- matrix(ParamStartR, nrow = 1)
}
ParamStartT <- FUN_TRANSFO(ParamStartR, "RT")
CritStart <- CritOptim
NRuns <- NRuns+nrow(CandidatesParamR)
if (verbose) {
if (Ncandidates > 1) {
message("\t Screening completed (", NRuns, " runs):")
}
if(Ncandidates==1){
if (Ncandidates == 1) {
message("\t Starting point for steepest-descent local search:")
}
message("\t Param = ", paste(formatC(ParamStartR, format = "f", width = 8, digits = 3), collapse = " , "))
message("\t Crit ", format(CritName, width = 12, justify = "left"), " = ", formatC(CritStart*Multiplier, format = "f", digits = 4))
}
##Results_archiving________________________________________________________
HistParamR[1,] <- ParamStartR;
HistParamT[1,] <- ParamStartT;
HistCrit[1,] <- CritStart;
HistParamR[1, ] <- ParamStartR
HistParamT[1, ] <- ParamStartT
HistCrit[1, ] <- CritStart
......@@ -159,186 +248,235 @@ Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FU
##Definition_of_the_function_creating_new_parameter_sets_through_a_step_by_step_progression_procedure
ProposeCandidatesLoc <- function(NewParamOptimT,OldParamOptimT,RangesT,OptimParam,Pace){
ProposeCandidatesLoc <- function(NewParamOptimT, OldParamOptimT, RangesT, OptimParam,Pace) {
##Format_checking
if(nrow(NewParamOptimT)!=1 | nrow(OldParamOptimT)!=1){ stop("each input set must be a matrix of one single line \n"); return(NULL); }
if(ncol(NewParamOptimT)!=ncol(OldParamOptimT) | ncol(NewParamOptimT)!=length(OptimParam)){ stop("each input set must have the same number of values \n"); return(NULL); }
if (nrow(NewParamOptimT) != 1 | nrow(OldParamOptimT) != 1) {
stop("each input set must be a matrix of one single line \n")
return(NULL)
}
if (ncol(NewParamOptimT)!=ncol(OldParamOptimT) | ncol(NewParamOptimT) != length(OptimParam)) {
stop("each input set must have the same number of values \n")
return(NULL)
}
##Proposal_of_new_parameter_sets ###(local search providing 2*NParam-1 new sets)
NParam <- ncol(NewParamOptimT);
VECT <- NULL;
for(I in 1:NParam){
NParam <- ncol(NewParamOptimT)
VECT <- NULL
for (I in 1:NParam) {
##We_check_that_the_current_parameter_should_indeed_be_optimised
if(OptimParam[I]==TRUE){
for(J in 1:2){
Sign <- 2*J-3; #Sign can be equal to -1 or +1
if (OptimParam[I] == TRUE) {
for (J in 1:2) {
Sign <- 2 * J - 3 #Sign can be equal to -1 or +1
##We_define_the_new_potential_candidate
Add <- TRUE;
PotentialCandidateT <- NewParamOptimT;
PotentialCandidateT[1,I] <- NewParamOptimT[I]+Sign*Pace;
Add <- TRUE
PotentialCandidateT <- NewParamOptimT
PotentialCandidateT[1, I] <- NewParamOptimT[I] + Sign * Pace
##If_we_exit_the_range_of_possible_values_we_go_back_on_the_boundary
if(PotentialCandidateT[1,I]<RangesT[1,I]){ PotentialCandidateT[1,I] <- RangesT[1,I]; }
if(PotentialCandidateT[1,I]>RangesT[2,I]){ PotentialCandidateT[1,I] <- RangesT[2,I]; }
if (PotentialCandidateT[1, I] < RangesT[1, I] ) {
PotentialCandidateT[1,I] <- RangesT[1, I]
}
if (PotentialCandidateT[1, I] > RangesT[2, I]) {
PotentialCandidateT[1,I] <- RangesT[2,I]
}
##We_check_the_set_is_not_outside_the_range_of_possible_values
if( NewParamOptimT[I]==RangesT[1,I] & Sign<0 ){ Add <- FALSE; }
if( NewParamOptimT[I]==RangesT[2,I] & Sign>0 ){ Add <- FALSE; }
if (NewParamOptimT[I] == RangesT[1, I] & Sign < 0) {
Add <- FALSE
}
if (NewParamOptimT[I] == RangesT[2, I] & Sign > 0) {
Add <- FALSE
}
##We_check_that_this_set_has_not_been_tested_during_the_last_iteration
if(identical(PotentialCandidateT,OldParamOptimT)){ Add <- FALSE; }
if (identical(PotentialCandidateT, OldParamOptimT)) {
Add <- FALSE
}
##We_add_the_candidate_to_our_list
if(Add==TRUE){ VECT <- c(VECT,PotentialCandidateT); }
if (Add == TRUE) {
VECT <- c(VECT, PotentialCandidateT)
}
}
}
}
Output <- NULL;
Output$NewCandidatesT <- matrix(VECT,ncol=NParam,byrow=TRUE);
return(Output);
Output <- NULL
Output$NewCandidatesT <- matrix(VECT, ncol = NParam, byrow = TRUE)
return(Output)
}
##Initialisation_of_variables
if(verbose){
if (verbose) {
message("Steepest-descent local search in progress")
}
Pace <- 0.64;
PaceDiag <- rep(0,NParam);
CLG <- 0.7^(1/NParam);
Compt <- 0;
CritOptim <- CritStart;
Pace <- 0.64
PaceDiag <- rep(0, NParam)
CLG <- 0.7^(1/NParam)
Compt <- 0
CritOptim <- CritStart
##Conversion_of_real_parameter_values
RangesR <- CalibOptions$SearchRanges;
RangesT <- FUN_TRANSFO(RangesR,"RT");
NewParamOptimT <- ParamStartT;
OldParamOptimT <- ParamStartT;
RangesR <- CalibOptions$SearchRanges
RangesT <- FUN_TRANSFO(RangesR, "RT")
NewParamOptimT <- ParamStartT
OldParamOptimT <- ParamStartT
##START_LOOP_ITER_________________________________________________________
for(ITER in 1:(100*NParam)){
for (ITER in 1:(100*NParam)) {
##Exit_loop_when_Pace_becomes_too_small___________________________________
if(Pace<0.01){ break; }
if (Pace < 0.01) {
break
}
##Creation_of_new_candidates______________________________________________
CandidatesParamT <- ProposeCandidatesLoc(NewParamOptimT,OldParamOptimT,RangesT,OptimParam,Pace)$NewCandidatesT;
CandidatesParamR <- FUN_TRANSFO(CandidatesParamT,"TR");
CandidatesParamT <- ProposeCandidatesLoc(NewParamOptimT, OldParamOptimT, RangesT, OptimParam, Pace)$NewCandidatesT
CandidatesParamR <- FUN_TRANSFO(CandidatesParamT, "TR")
##Remplacement_of_non_optimised_values_____________________________________
CandidatesParamR <- apply(CandidatesParamR,1,function(x){ x[!OptimParam] <- CalibOptions$FixedParam[!OptimParam]; return(x); });
if(NParam>1){ CandidatesParamR <- t(CandidatesParamR); } else { CandidatesParamR <- cbind(CandidatesParamR); }
CandidatesParamR <- apply(CandidatesParamR, 1, function(x) {
x[!OptimParam] <- CalibOptions$FixedParam[!OptimParam]
return(x)})
if (NParam > 1) {
CandidatesParamR <- t(CandidatesParamR)
} else {
CandidatesParamR <- cbind(CandidatesParamR)
}
##Loop_to_test_the_various_candidates_____________________________________
iNewOptim <- 0;
for(iNew in 1:nrow(CandidatesParamR)){
iNewOptim <- 0
for (iNew in 1:nrow(CandidatesParamR)) {
##Model_run
Param <- CandidatesParamR[iNew,];
OutputsModel <- FUN_MOD(InputsModel,RunOptions,Param);
Param <- CandidatesParamR[iNew, ]
OutputsModel <- FUN_MOD(InputsModel, RunOptions, Param)
##Calibration_criterion_computation
OutputsCrit <- FUN_CRIT(InputsCrit,OutputsModel, verbose = FALSE);
if(!is.na(OutputsCrit$CritValue)){ if(OutputsCrit$CritValue*OutputsCrit$Multiplier < CritOptim){
CritOptim <- OutputsCrit$CritValue*OutputsCrit$Multiplier;
iNewOptim <- iNew;
} }
OutputsCrit <- FUN_CRIT(InputsCrit, OutputsModel, verbose = FALSE)
if (!is.na(OutputsCrit$CritValue)) {
if (OutputsCrit$CritValue*OutputsCrit$Multiplier < CritOptim) {
CritOptim <- OutputsCrit$CritValue*OutputsCrit$Multiplier
iNewOptim <- iNew
}
}
}
NRuns <- NRuns+nrow(CandidatesParamR);
NRuns <- NRuns + nrow(CandidatesParamR)
##When_a_progress_has_been_achieved_______________________________________
if(iNewOptim!=0){
if (iNewOptim != 0) {
##We_store_the_optimal_set
OldParamOptimT <- NewParamOptimT;
NewParamOptimT <- matrix(CandidatesParamT[iNewOptim,1:NParam],nrow=1);
Compt <- Compt+1;
OldParamOptimT <- NewParamOptimT
NewParamOptimT <- matrix(CandidatesParamT[iNewOptim, 1:NParam], nrow = 1)
Compt <- Compt+1
##When_necessary_we_increase_the_pace ### if_successive_progress_occur_in_a_row
if(Compt>2*NParam){
Pace <- Pace*2;
Compt <- 0;
if (Compt > 2*NParam) {
Pace <- Pace * 2
Compt <- 0
}
##We_update_PaceDiag
VectPace <- NewParamOptimT-OldParamOptimT;
for(iC in 1:NParam){ if(OptimParam[iC]){
if(VectPace[iC]!=0){ PaceDiag[iC] <- CLG*PaceDiag[iC]+(1-CLG)*VectPace[iC]; }
if(VectPace[iC]==0){ PaceDiag[iC] <- CLG*PaceDiag[iC]; }
} }
VectPace <- NewParamOptimT-OldParamOptimT
for (iC in 1:NParam) {
if (OptimParam[iC]) {
if (VectPace[iC] != 0) {
PaceDiag[iC] <- CLG * PaceDiag[iC] + (1-CLG) * VectPace[iC]
}
if (VectPace[iC] == 0) {
PaceDiag[iC] <- CLG*PaceDiag[iC]
}
}
}
} else {
##When_no_progress_has_been_achieved_we_decrease_the_pace_________________
Pace <- Pace/2;
Compt <- 0;
Pace <- Pace / 2
Compt <- 0
}
##Test_of_an_additional_candidate_using_diagonal_progress_________________
if(ITER>4*NParam){
NRuns <- NRuns+1;
iNewOptim <- 0; iNew <- 1;
CandidatesParamT <- NewParamOptimT+PaceDiag; if(!is.matrix(CandidatesParamT)){ CandidatesParamT <- matrix(CandidatesParamT,nrow=1); }
##If_we_exit_the_range_of_possible_values_we_go_back_on_the_boundary
for(iC in 1:NParam){ if(OptimParam[iC]){
if(CandidatesParamT[iNew,iC]<RangesT[1,iC]){ CandidatesParamT[iNew,iC] <- RangesT[1,iC]; }
if(CandidatesParamT[iNew,iC]>RangesT[2,iC]){ CandidatesParamT[iNew,iC] <- RangesT[2,iC]; }
} }
CandidatesParamR <- FUN_TRANSFO(CandidatesParamT,"TR");
##Model_run
Param <- CandidatesParamR[iNew,];
OutputsModel <- FUN_MOD(InputsModel,RunOptions,Param);
##Calibration_criterion_computation
OutputsCrit <- FUN_CRIT(InputsCrit,OutputsModel, verbose = FALSE);
if(OutputsCrit$CritValue*OutputsCrit$Multiplier < CritOptim){
CritOptim <- OutputsCrit$CritValue*OutputsCrit$Multiplier;
iNewOptim <- iNew;
}
##When_a_progress_has_been_achieved
if(iNewOptim!=0){
OldParamOptimT <- NewParamOptimT;
NewParamOptimT <- matrix(CandidatesParamT[iNewOptim,1:NParam],nrow=1);
if (ITER > 4*NParam) {
NRuns <- NRuns + 1
iNewOptim <- 0
iNew <- 1
CandidatesParamT <- NewParamOptimT+PaceDiag
if (!is.matrix(CandidatesParamT)) {
CandidatesParamT <- matrix(CandidatesParamT, nrow = 1)
}
##If_we_exit_the_range_of_possible_values_we_go_back_on_the_boundary
for (iC in 1:NParam) {
if (OptimParam[iC]) {
if (CandidatesParamT[iNew, iC] < RangesT[1, iC]) {
CandidatesParamT[iNew, iC] <- RangesT[1, iC]
}
if (CandidatesParamT[iNew, iC] > RangesT[2, iC]) {
CandidatesParamT[iNew, iC] <- RangesT[2, iC]
}
}
}
CandidatesParamR <- FUN_TRANSFO(CandidatesParamT, "TR")
##Model_run
Param <- CandidatesParamR[iNew, ]
OutputsModel <- FUN_MOD(InputsModel, RunOptions, Param)
##Calibration_criterion_computation
OutputsCrit <- FUN_CRIT(InputsCrit, OutputsModel, verbose = FALSE)
if (OutputsCrit$CritValue*OutputsCrit$Multiplier < CritOptim) {
CritOptim <- OutputsCrit$CritValue*OutputsCrit$Multiplier
iNewOptim <- iNew
}
##When_a_progress_has_been_achieved
if (iNewOptim != 0) {
OldParamOptimT <- NewParamOptimT
NewParamOptimT <- matrix(CandidatesParamT[iNewOptim, 1:NParam], nrow = 1)
}
}
##Results_archiving_______________________________________________________
NewParamOptimR <- FUN_TRANSFO(NewParamOptimT,"TR");
HistParamR[ITER+1,] <- NewParamOptimR;
HistParamT[ITER+1,] <- NewParamOptimT;
HistCrit[ITER+1,] <- CritOptim;
### if(verbose){ cat(paste("\t Iter ",formatC(ITER,format="d",width=3)," Crit ",formatC(CritOptim,format="f",digits=4)," Pace ",formatC(Pace,format="f",digits=4),"\n",sep="")); }
NewParamOptimR <- FUN_TRANSFO(NewParamOptimT, "TR")
HistParamR[ITER+1, ] <- NewParamOptimR
HistParamT[ITER+1, ] <- NewParamOptimT
HistCrit[ITER+1, ] <- CritOptim
### if (verbose) { cat(paste("\t Iter ",formatC(ITER,format="d",width=3), " Crit ",formatC(CritOptim,format="f",digits=4), " Pace ",formatC(Pace,format="f",digits=4), "\n",sep=""))}
} ##END_LOOP_ITER_________________________________________________________
ITER <- ITER-1;
ITER <- ITER-1
##Case_when_the_starting_parameter_set_remains_the_best_solution__________
if(CritOptim==CritStart & verbose){
message("\t No progress achieved");
if (CritOptim == CritStart & verbose) {
message("\t No progress achieved")
}
##End_of_Steepest_Descent_Local_Search____________________________________
ParamFinalR <- NewParamOptimR;
ParamFinalT <- NewParamOptimT;
CritFinal <- CritOptim;
NIter <- 1+ITER;
if(verbose){
ParamFinalR <- NewParamOptimR
ParamFinalT <- NewParamOptimT
CritFinal <- CritOptim
NIter <- 1 + ITER
if (verbose) {
message("\t Calibration completed (", NIter, " iterations, ", NRuns, " runs)")
message("\t Param = ", paste(formatC(ParamFinalR, format = "f", width = 8, digits = 3), collapse = " , "))
message("\t Crit ", format(CritName, width = 12, justify = "left"), " = ", formatC(CritFinal*Multiplier, format = "f", digits = 4))
}
##Results_archiving_______________________________________________________
HistParamR <- cbind(HistParamR[1:NIter,]); colnames(HistParamR) <- paste("Param",1:NParam,sep="");
HistParamT <- cbind(HistParamT[1:NIter,]); colnames(HistParamT) <- paste("Param",1:NParam,sep="");
HistCrit <- cbind(HistCrit[1:NIter,]); ###colnames(HistCrit) <- paste("HistCrit");
HistParamR <- cbind(HistParamR[1:NIter, ])
colnames(HistParamR) <- paste0("Param", 1:NParam)
HistParamT <- cbind(HistParamT[1:NIter, ])
colnames(HistParamT) <- paste0("Param", 1:NParam)
HistCrit <- cbind(HistCrit[1:NIter, ])
###colnames(HistCrit) <- paste("HistCrit")
BoolCrit_Actual <- InputsCrit$BoolCrit; BoolCrit_Actual[OutputsCrit$Ind_notcomputed] <- FALSE;
MatBoolCrit <- cbind( InputsCrit$BoolCrit , BoolCrit_Actual );
colnames(MatBoolCrit) <- c("BoolCrit_Requested","BoolCrit_Actual");
BoolCrit_Actual <- InputsCrit$BoolCrit
BoolCrit_Actual[OutputsCrit$Ind_notcomputed] <- FALSE
MatBoolCrit <- cbind(InputsCrit$BoolCrit, BoolCrit_Actual)
colnames(MatBoolCrit) <- c("BoolCrit_Requested", "BoolCrit_Actual")
##_____Output______________________________________________________________________________
OutputsCalib <- list(as.double(ParamFinalR),CritFinal*Multiplier,NIter,NRuns,HistParamR,HistCrit*Multiplier,MatBoolCrit,CritName,CritBestValue);
names(OutputsCalib) <- c("ParamFinalR","CritFinal","NIter","NRuns","HistParamR","HistCrit","MatBoolCrit","CritName","CritBestValue");
class(OutputsCalib) <- c("OutputsCalib","HBAN");
return(OutputsCalib);
OutputsCalib <- list(ParamFinalR = as.double(ParamFinalR), CritFinal = CritFinal*Multiplier,
NIter = NIter, NRuns = NRuns,
HistParamR = HistParamR, HistCrit = HistCrit*Multiplier, MatBoolCrit = MatBoolCrit,
CritName = CritName, CritBestValue = CritBestValue)
class(OutputsCalib) <- c("OutputsCalib", "HBAN")
return(OutputsCalib)
......
inst/CITATION
+ 4
− 4
View file @ 336d5e9b
......@@ -17,14 +17,14 @@ citEntry(entry="Manual",
title = "airGR: Suite of GR hydrological models for precipitation-runoff modelling",
author = personList(as.person("L. Coron"), as.person("C. Perrin"), as.person("C. Michel")),
journal = "R News",
year = "2016",
note = "R package version 1.0.3",
year = "2017",
note = "R package version 1.0.4",
url = "https://webgr.irstea.fr/airGR/?lang=en",
textVersion =
paste("Coron, L., Perrin, C. and Michel, C.",
"(2016).",
"(2017).",
"airGR: Suite of GR hydrological models for precipitation-runoff modelling.",
"R package version 1.0.3.",
"R package version 1.0.4.",
"https://webgr.irstea.fr/airGR/?lang=en.",
sep = " ")
)
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