I'm trying to run a parametric analysis in EnergyPlus (idf file) through R, by using the eplusr and epluspar packages. But when I set my optimization variables (from EnergyPlus objects) I'm getting an error in match.call that I don't know where is it. Could anyone help me please?
My code is:
dir <- eplus_config(9.6)$dir
path_model <- file.path(dir, "RWTH/SchoolA-calibrated_MO-R01.idf")
path_weather <- file.path(dir, "RWTH/BRA_SP_Campinas-Viracopos.Intl.AP.837210_TMYx.2007-2021.epw")
# read model
idf <- read_idf(path_model)
# define a measure to change the window status
set_window_status <- function (idf, wst) {
wst <- as.character(wst)
idf$set(wst_scenario = list(program_name_1 = wst))
idf
}
# define a measure to change occupancy
set_occupancy <- function (idf, occ) {
occ <- as.character(occ)
idf$set(ocupacao_z1 = list(number_of_people = occ))
}
# combine all measures into one
design_options <- function (idf, window_status, occupancy) {
idf <- set_window_status(idf, window_status)
idf <- set_occupancy(idf, occupancy)
idf
}
# specify design space of parameters
ga$apply_measure(design_options,
window_status = choice_space(as.character(seq(1, 12, 1))),
occupancy = choice_space(as.character(seq(6, 31, 5))),
)
The error appears after I run the "specify design space of parameters" part and it is:
Error in match.call(definition, call, expand.dots, envir) :
unused argument (..3)
The following names are provided from the EnergyPlus idf file:
wst_scenario: name of the object
program_name_1: name of the line I want the parametric variation to occur
ocupacao_z1: name of the object
number_of_people: name of the line I want the parametric variation to occur
I tried to change their names (including/ excluding capital letters, including excluding the "_" in between words) but it didn't work.
Here is the path to the idf and weather files: https://drive.google.com/drive/folders/1bX8ZB2aUXMRrEUqlpMMM6K8avs6LyIdK?usp=share_link
Related
I've recently been attempting to evaluate output from k-modes (a cluster label), relative to a so-called True cluster label (labelled 'class' below).
In other words: I've been attempting to external validate the clustering output. However, when I tried external validation measures from the 'fpc' package, I was unsuccessful (error term posted below script).
I've attached my code for the mushroom dataset. I would appreciate if anyone could show me how to successful execute these external validation measures in the context of categorical data.
Any help appreciated.
# LIBRARIES
install.packages('klaR')
install.packages('fpc')
library(klaR)
library(fpc)
#MUSHROOM DATA
mushrooms <- read.csv(file = "https://raw.githubusercontent.com/miachen410/Mushrooms/master/mushrooms.csv", header = FALSE)
names(mushrooms) <- c("edibility", "cap-shape", "cap-surface", "cap-color",
"bruises", "odor", "gill-attachment", "gill-spacing",
"gill-size", "gill-color", "stalk-shape", "stalk-root",
"stalk-surface-above-ring", "stalk-surface-below-ring",
"stalk-color-above-ring", "stalk-color-below-ring", "veil-type",
"veil-color", "ring-number", "ring-type", "spore-print-color",
"population", "habitat")
names(mushrooms)[names(mushrooms)=="edibility"] <- "class"
indexes <- apply(mushrooms, 2, function(x) any(is.na(x) | is.infinite(x)))
colnames(mushrooms)[indexes]
table(mushrooms$class)
str(mushrooms)
#REMOVING CLASS VARIABLE
mushroom.df <- subset(mushrooms, select = -c(class))
#KMODES ANALYSIS
result.kmode <- kmodes(mushroom.df, 2, iter.max = 50, weighted = FALSE)
#EXTERNAL VALIDATION ATTEMPT
mushrooms$class <- as.factor(mushrooms$class)
class <- as.numeric(mushrooms$class))
clust_stats <- cluster.stats(d = dist(mushroom.df),
class, result.kmode$cluster)
#ERROR TERM
Error in silhouette.default(clustering, dmatrix = dmat) :
NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning message:
In dist(mushroom.df) : NAs introduced by coercion
I've worked through the great tutorial 'A short introduction to acoustic template matching with monitoR'
I'm now attempting to detect calls using spectogram cross correlation within a 30 second sample field recording. The function corMatch is returning the following error...
'Error in !all.equal(template#t.step, t.step, tolerance = t.step/10000) :
invalid argument type'
What have I done wrong?
I've used the following code:
survey <- readWave('20180901_160000.wav', from = 64, to = 64.5, units='minutes')
mtemp1 <- readWave('mew.wav')
mtemp2 <- readWave('mew2.wav')
mtemp1.fp <- file.path(tempdir(), "mtemp1.wav")
writeWave(mtemp1, mtemp1.fp)
mtemp2.fp <- file.path(tempdir(), "mtemp2.wav")
writeWave(mtemp2, mtemp2.fp)
survey.fp <- file.path(tempdir(), "survey2018-09-01_160400_ACDT.wav")
writeWave(survey, survey.fp)
mt1 <- makeCorTemplate(mtemp1.fp, frq.lim=c(6,9), name='m1')
mt2 <- makeCorTemplate(mtemp2.fp, frq.lim=c(5.5,8.5), name='m2')
MewTemps <- combineCorTemplates(mt1, mt2)
MewTempScores <- corMatch(survey.fp, MewTemps)
As per https://github.com/jonkatz2/monitoR/issues/2 - the sampling rate of the survey wave file doesn't match the sampling rate of the template.
You can use either seewave::resamp or monitoR::changeSampRate to resample one to make them match
I'm trying to run this code, and I'm using mhadaptive package, but the problem is that when I run these code without writing metropolis_hastings (that is one part of mhadaptive package) error does not occur, but when I add mhadaptive package the error occur. What should I do?
li_F1<-function(pars,data) #defining first function
{
a01<-pars[1] #defining parameters
a11<-pars[2]
epsilon<<-pars[3]
b11<-pars[4]
a02<-pars[5]
a12<-pars[6]
b12<-pars[7]
h<-pars[8]
h[[i]]<-list() #I want my output is be listed in the h
h[[1]]<-0.32082184 #My first value of h is known and other values should calculate by formula
for(i in 2:nrow(F_2_))
{
h[[i]]<- ((a01+a11*(h[[i-1]])*(epsilon^2)*(h[[i-1]])*b11)+(F1[,2])*((a02+a12*(h[[i-1]])*(epsilon^2)+(h[[i-1]])*b12)))
pred<- h[[i]]
}
log_likelihood<-sum(dnorm(prod(h[i]),pred,sd = 1 ,log = TRUE))
return(h[i])
prior<- prior_reg(pars)
return(log_likelihood + prior)
options(digits = 22)
}
prior_reg<-function(pars) #defining another function
{
epsilon<<-pars[3] #error
prior_epsilon<-pt(0.95,5,lower.tail = TRUE,log.p = FALSE)
return(prior_epsilon)
}
F1<-as.matrix(F_2_) #defining my importing data and simulatunig data with them
x<-F1[,1]
y<-F1[,2]
d<-cbind(x,y)
#using mhadaptive package
mcmc_r<-Metro_Hastings(li_func = li_F1,pars=c(10,15,10,10,10,15),par_names=c('a01','a02','a11','a12','b11','b12'),data=d)
By running this code this error occur.
Error in h[[i]] <- list() : replacement has length zero
I'll so much appreciate who help me.
I am using the 'RNetLogo' package to run sensitivity analyses on my NetLogo model. My model has 24 parameters I need to vary - so parallelising this process would be ideal! I've been following along with the example in Thiele's "Parallel processing with the RNetLogo package" vignette, which uses the 'parallel' package in conjunction with 'RNetLogo'.
I've managed to get R to initialise the NetLogo model across all 12 of my processors, which I've verified using gui=TRUE. The problem comes when I try to run the simulation code across the 12 processors using 'parApply'. This line runs without error, but it only runs on one of the processors (using around 8% of my total CPU power). Here's a mock up of my R code file - I've included some commented-out code at the end, showing how I run the simulation without trying to parallelise:
### Load packages
library(parallel)
### Set up initialisation function
prepro <- function(dummy, gui, nl.path, model.path) {
library(RNetLogo)
NLStart(nl.path, gui=gui)
NLLoadModel(model.path)
}
### Set up finalisation function
postpro <- function(x) {
NLQuit()
}
### Set paths
# For NetLogo
nl.path <- "C:/Program Files/NetLogo 6.0/app"
nl.jarname <- "netlogo-6.0.0.jar"
# For the model
model.path <- "E:/Model.nlogo"
# For the function "sim" code
sim.path <- "E:/sim.R"
### Set base values for parameters
base.param <- c('prey-max-velocity' = 25,
'prey-agility' = 3.5,
'prey-acceleration' = 20,
'prey-deceleration' = 25,
'prey-vision-distance' = 10,
'prey-vision-angle' = 240,
'time-to-turn' = 5,
'time-to-return-to-foraging' = 300,
'time-spent-circling' = 2,
'predator-max-velocity' = 35,
'predator-agility' = 3.5,
'predator-acceleration' = 20,
'predator-deceleration' = 25,
'predator-vision-distance' = 20,
'predator-vision-angle' = 200,
'time-to-give-up' = 120,
'number-of-safe-zones' = 1,
'number-of-target-patches' = 5,
'proportion-obstacles' = 0.05,
'obstacle-radius' = 2.0,
'obstacle-radius-range' = 0.5,
'obstacle-sensitivity-for-prey' = 0.95,
'obstacle-sensitivity-for-predators' = 0.95,
'safe-zone-attractiveness' = 500
)
## Get names of parameters
param.names <- names(base.param)
### Load the code of the simulation function (name: sim)
source(file=sim.path)
### Convert "base.param" to a matrix, as required by parApply
base.param <- matrix(base.param, nrow=1, ncol=24)
### Get the number of simulations we want to run
design.combinations <- length(base.param[[1]])
already.processed <- 0
### Initialise NetLogo
processors <- detectCores()
cl <- makeCluster(processors)
clusterExport(cl, 'sim')
gui <- FALSE
invisible(parLapply(cl, 1:processors, prepro, gui=gui, nl.path=nl.path, model.path=model.path))
### Run the simulation across all processors, using parApply
sim.result.base <- parApply(cl, base.param, 1, sim,
param.names,
no.repeated.sim = 100,
trace.progress = FALSE,
iter.length = design.combinations,
function.name = "base parameters")
### Run the simulation on a single processor
#sim.result.base <- sim(base.param,
# param.names,
# no.repeated.sim = 100,
# my.nl1,
# trace.progress = TRUE,
# iter.length = design.combinations,
# function.name = "base parameters")
Here's a mock up for the 'sim' function (adapted from Thiele's paper "Facilitating parameter estimation and sensitivity analyses of agent-based models - a cookbook using NetLogo and R"):
sim <- function(param.set, parameter.names, no.repeated.sim, trace.progress, iter.length, function.name) {
# Some security checks
if (length(param.set) != length(parameter.names))
{ stop("Wrong length of param.set!") }
if (no.repeated.sim <= 0)
{ stop("Number of repetitions must be > 0!") }
if (length(parameter.names) <= 0)
{ stop("Length of parameter.names must be > 0!") }
# Create an empty list to save the simulation results
eval.values <- NULL
# Run the repeated simulations (to control stochasticity)
for (i in 1:no.repeated.sim)
{
# Create a random-seed for NetLogo from R, based on min/max of NetLogo's random seed
NLCommand("random-seed",runif(1,-2147483648,2147483647))
## This is the stuff for one simulation
cal.crit <- NULL
# Set NetLogo parameters to current parameter values
lapply(seq(1:length(parameter.names)), function(x) {NLCommand("set ",parameter.names[x], param.set[x])})
NLCommand("setup")
# This should run "go" until prey-win =/= 5, i.e. when the pursuit ends
NLDoCommandWhile("prey-win = 5", "go")
# Report a value
prey <- NLReport("prey-win")
# Report another value
pred <- NLReport("predator-win")
## Extract the values we are interested in
cal.crit <- rbind(cal.crit, c(prey, pred))
# append to former results
eval.values <- rbind(eval.values,cal.crit)
}
## Make sure eval.values has column names
names(eval.values) <- c("PreySuccess", "PredSuccess")
# Return the mean of the repeated simulation results
if (no.repeated.sim > 1) {
return(colMeans(eval.values))
}
else {
return(eval.values)
}
}
I think the problem might lie in the "nl.obj" string that RNetLogo uses to identify the NetLogo instance you want to run the code on - however, I've tried several different methods of fixing this, and I haven't been able to come up with a solution that works. When I initialise NetLogo across all the processors using the code provided in Thiele's example, I don't set an "nl.obj" value for each instance, so I'm guessing RNetLogo uses some kind of default list? However, in Thiele's original code, the "sim" function requires you to specify which NetLogo instance you want to run it on - so R will spit an error when I try to run the final line (Error in checkForRemoteErrors(val) : one node produced an error: argument "nl.obj" is missing, with no default). I have modified the "sim" function code so that it doesn't require this argument and just accepts the default setting for nl.obj - but then my simulation only runs on a single processor. So, I think that by default, "sim" must only be running the code on a single instance of NetLogo. I'm not certain how to fix it.
This is also the first time I've used the 'parallel' package, so I could be missing something obvious to do with 'parApply'. Any insight would be much appreciated!
Thanks in advance!
I am still in the process of applying a similar technique to perform a Morris Elementary Effects screening with my NetLogo model. For me the parallel execution works fine. I compared your script to mine and noticed that in my version the 'parApply' call of the simulation function (simfun) is embedded in a function statement (see below). Maybe including the function already solves your issue.
sim.results.morris <- parApply(cl, mo$X, 1, function(x) {simfun(param.set=x,
no.repeated.sim=no.repeated.sim,
parameter.names=input.names,
iter.length=iter.length,
fixed.values=fixed.values,
model.seed=new.model.seed,
function.name="Morris")})
I have a large dataset (questionnaire results) of mostly categorical variables. I have tested for dependency between the variables using chi-square test. There are incomprehensible number of dependencies between variables. I used the chaid() function in the CHAID package to detect interactions and separate out (what I hope to be) the underlying structure of these dependencies for each variable. What typically happens is that the chi-square test will reveal a large number of dependencies (say 10-20) for a variable and the chaid function will reduce this to something much more comprehensible (say 3-5). What I want to do is to extract the names of those variable that were shown to be relevant in the chaid() results.
The chaid() output is in the form of a constparty object. My question is how to extract the variable names associated with the nodes in such an object.
Here is a self contained code example:
library(evtree) # for the ContraceptiveChoice dataset
library(CHAID)
library(vcd)
library(MASS)
data("ContraceptiveChoice")
longform = formula(contraceptive_method_used ~ wifes_education +
husbands_education + wifes_religion + wife_now_working +
husbands_occupation + standard_of_living_index + media_exposure)
z = chaid(longform, data = ContraceptiveChoice)
# plot(z)
z
# This is the part I want to do programatically
shortform = formula(contraceptive_method_used ~ wifes_education + husbands_occupation)
# The thing I want is a programatic way to extract 'shortform' from 'z'
# Examples of use of 'shortfom'
loglm(shortform, data = ContraceptiveChoice)
One possible sollution:
nn <- nodeapply(z)
n.names= names(unlist(nn[[1]]))
ext <- unlist(sapply(n.names, function(x) grep("split.varid.", x, value=T)))
ext <- gsub("kids.split.varid.", "", ext)
ext <- gsub("split.varid.", "", ext)
dep.var <- as.character(terms(z)[1][[2]]) # get the dependent variable
plus = paste(ext, collapse=" + ")
mul = paste(ext, collapse=" * ")
shortform <- as.formula(paste (dep.var, plus, sep = " ~ "))
satform <- as.formula(paste (dep.var, mul, sep = " ~ "))
mosaic(shortform, data = ContraceptiveChoice)
#stp <- step(glm(satform, data=ContraceptiveChoice, family=binomial), direction="both")