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Commit 2a65e98e authored by hgb's avatar hgb
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update small fixes all over

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.gitignore
+ 3
− 1
View file @ 2a65e98e
......@@ -19,4 +19,6 @@ vignettes/*_files*
tmp/
onlineforecast.Rcheck
\ No newline at end of file
onlineforecast.Rcheck
images/
\ No newline at end of file
R/qmodel.R
+ 53
− 49
View file @ 2a65e98e
qmodel <- R6::R6Class("qmodel", public = list(
####### Store Data and information for algorithm #####
info = list(),
####### Store Coefficients Data #######
beta = list(),
####### Fixed Parameters ######
## Weighted
W = NA,
n_in_bin = NA,
## Number of predictiors
K = NA,
## Quantiles
tau = NA,
## Cold start size
N1 = NA,
debug = NA,
####### Quantile Information ########
## Index of the columns of the X matrix of Xny
IX = NA,
## Index of the column of y
Iy = NA,
## The design matrix
X = list(),
####### debug Information ########
listIH = list(),
####### Forecast Information ########
inputs = list(),
kseq = NA,
output = NA,
prm = NA,
regprmexpr = NA,
regprm = NA,
## Offline parameters
prmbounds = as.matrix(data.frame(lower=NA, init =NA, upper=NA)),
datatr = NA,
qrFIT = NA,
maxlagAR = NA,
yAR = NA,
Ypred = NA,
initialize = function(N1 = NULL, debug = FALSE){
if(is.null(N1)) stop("The number of data points for the cold start needs to be defined")
self$N1 <- N1
self$debug <- debug
},
####### Store Data and information for algorithm #####
info = list(),
####### Store Coefficients Data #######
beta = list(),
####### Fixed Parameters ######
## Weighted
W = NA,
n_in_bin = NA,
## Number of predictiors
K = NA,
## Quantiles
tau = NA,
## Cold start size
N1 = NA,
debug = NA,
####### Quantile Information ########
## Index of the columns of the X matrix of Xny
IX = NA,
## Index of the column of y
Iy = NA,
## The design matrix
X = list(),
####### debug Information ########
listIH = list(),
####### Forecast Information ########
inputs = list(),
kseq = NA,
output = NA,
prm = NA,
regprmexpr = NA,
regprm = NA,
## Offline parameters
prmbounds = as.matrix(data.frame(lower=NA, init =NA, upper=NA)),
datatr = NA,
qrFIT = NA,
maxlagAR = NA,
yAR = NA,
Ypred = NA,
## Initialise the model with the size of the cold start.
## We need some starting point for the iteration of the quantile fit, using simplex method.
initialize = function(N1 = NULL, debug = FALSE){
if(is.null(N1)) stop("The number of data points for the cold start needs to be defined")
self$N1 <- N1
self$debug <- debug
},
### Should be the same to forecastmodel.R.. maybe have an inheritance from that instead of rewrite.
#----------------------------------------------------------------
# Get the transformation parameters
......@@ -241,6 +247,4 @@ qmodel <- R6::R6Class("qmodel", public = list(
return(expr)
}
#----------------------------------------------------------------
))
R/quantile_fit.R
+ 15
− 11
View file @ 2a65e98e
......@@ -20,9 +20,11 @@ quantile_fit <- function(prm, model, q, data, printout = TRUE, returnanalysis =
# First insert the prm into the model input expressions
model$insert_prm(prm)
model$n_in_bin <- round(2*(1/(1-model$regprm$lambda)))
## Annoying!
## Compute the effective memory of lambda, the window size
model$n_in_bin <- round(2*(1/(1-model$regprm$lambda)))
## If the effective memory is less then the cold start window, then set it to the cold start window.
## HGB 11/02/2025: Unsure if this is correct
if(model$n_in_bin < model$N1){
model$n_in_bin = model$N1 + 1
}
......@@ -32,6 +34,7 @@ quantile_fit <- function(prm, model, q, data, printout = TRUE, returnanalysis =
# Since rls_fit is run from scratch, the init the stored inputs data (only needed when running iteratively)
#model$datatr <- NA
#model$yAR <- NA
# Reset the model state (e.g. inputs state, stored iterative data, ...)
model$reset_state()
......@@ -49,20 +52,21 @@ quantile_fit <- function(prm, model, q, data, printout = TRUE, returnanalysis =
Ypred <- NULL
for(k in model$kseq){
cat("horizon :", k, "\n")
cat("n_in_bin :", model$n_in_bin, "\n")
# cat("horizon :", k, "\n")
# cat("n_in_bin :", model$n_in_bin, "\n")
## Setup the design matrix for k step
X <- as.data.frame(subset(model$datatr, kseq=k))
## Lag them to match to the
## Lag them to match to the output, so the input forecast matches the output.
X <- onlineforecast:::lagdf.matrix(X, k)
X$y <- data[[model$output]]
X <- X[-1:-k,]
if (k > 0) {
X <- X[-(1:k), ]
}
fit <- rq(paste("y ~ -1 + ", paste(names(X[, -which(names(X) %in% c("y"))]), collapse = " + "), sep = ""), tau = q, data = X[1:(model$N1-k),])
beta <- as.matrix(fit$coefficients)
## Fill it from start (index 1) to the end of the cold start
beta <- fit$coefficients
model$beta[[paste0("q", q)]][[paste0("k", k)]] <- matrix(beta, nrow = model$N1, ncol = model$K, byrow = T)
r <- as.matrix(fit$residuals, ncol = length(q))
for(i in 1:dim(r)[2]){
......@@ -86,7 +90,7 @@ quantile_fit <- function(prm, model, q, data, printout = TRUE, returnanalysis =
## Compute the loss function, maybe the residuals are compute in different place
loss <- sapply(model$kseq, function(i) pinball_loss(r = model$info[[paste0("q", q)]][[paste0("k", i)]]$R[data$scoreperiod[(i+1):length(data$scoreperiod)]], q))
model$Ypred <- Ypred
if(!returnanalysis){
#Yhat <- quantile_prediction(model = model, multistep = model$kseq)
cat("Quantile, tau:", q, "\n")
......
R/quantile_optim.R
+ 1
− 1
View file @ 2a65e98e
......@@ -25,7 +25,7 @@ quantile_optim <- function(model, data){
# Parameters to pass to quantile_fit
model = model,
q = model$tau[i],
data = Dnew,
data = data,
returnanalysis = FALSE,
# Parameters to pass to optim
lower = lower,
......
R/quantile_predict.R
+ 4
− 10
View file @ 2a65e98e
......@@ -13,17 +13,11 @@ quantile_predict <- function(model, datatr){
## Setup the design matrix for k step
X <- as.data.frame(subset(datatr, kseq=k))
## Lag them to match to the
#X <- onlineforecast:::lagdf.matrix(X, k)
X <- onlineforecast:::lagdf.matrix(X, k)
yhat <- as.numeric(rep(NA, nrow(X)))
for(i in ((k):nrow(X))){
if(i <= (model$N1)) {
j <- 1
} else{
j <- i - model$N1
}
#browser()
yhat[i] <- t(as.numeric(X[i,])) %*% model$beta[[paste0("q",q)]][[paste0("k",k)]][j,]
for(i in ((1):nrow(X))){
yhat[i] <- t(as.numeric(X[i,])) %*% model$beta[[paste0("q",q)]][[paste0("k",k)]][i,]
}
return(yhat)
......
R/update_rq.R
+ 14
− 1
View file @ 2a65e98e
......@@ -25,7 +25,7 @@ update_rq <- function(Xny, tau, k, model = NULL, debug = TRUE){
Ypred <- matrix(as.numeric(NA), nrow = N, ncol = 1)
if(debug) {
print("yeah")
# print("yeah")
list_idx <- matrix(as.numeric(NA), nrow = 1, ncol = length(res$Ih)+1)
list_idx[1,] <- c(0,res$Ih)
}
......@@ -71,6 +71,19 @@ update_rq <- function(Xny, tau, k, model = NULL, debug = TRUE){
list_idx <- rbind(list_idx, c(counter, res$Ih))
}
# if(FALSE){
# invXh <- solve(Xold[res$Ih, 1:model$K])
# cB <- as.numeric(res$P < 0) + res$P * tau
# cC <- c(rep(tau, model$K), rep(1 - tau, model$K))
# #IB2 <- -(t(res$P %*% t(rep(1, model$K))) %*% Xold[res$Ihc+1, 1:model$K]) %*% invXh
# g <- t(IB2) %*% cB
# d <- cC - c(g, -g)
# d[abs(d) < 1e-15] <- 0
# s <- order(d)
# md <- sort(d)
# }
resAlg <- rq_simplex_cpp(X = Xold[,1:model$K],
Ih = res$Ih,
......
misc-R/quantile/adaptive_quantile.R 0 → 100644
+ 185
− 0
View file @ 2a65e98e
#----------------------------------------------------------------
# Init by deleting all variables and functions
rm(list=ls())
# Set the working directory
setwd("/home/hgb/git/onlineforecast/misc-R/quantile/")
#----------------------------------------------------------------
#----------------------------------------------------------------
# Exercise on RLS
# Init
rm(list = ls())
sapply(dir("functions",full.names=TRUE), source)
# A function for fitting a recursive least squares estimation
source("rls.R")
rls
#----------------------------------------------------------------
#----------------------------------------------------------------
# Generate some data from a linear model
n <- 200
x <- runif(n)
beta0 <- 2
beta1 <- -3
y <- beta0 + beta1 * x + rnorm(n, sd=0.1)
# Estimate the coefficients
fit <- lm(y ~ x)
plot(x, y)
abline(fit)
fit
# Generate again with other coefficient values
x1 <- runif(n)
beta0 <- -2
beta1 <- 3
y1 <- beta0 + beta1 * x1 + rnorm(n, sd=0.1)
# Estimate again
fit1 <- lm(y1 ~ x1)
plot(x1, y1)
abline(fit1)
fit
# Combine them as two "periods",
# such that at the start of the the second part, at i=201, the
# coefficients change and have different values
X <- data.frame(y=c(y,y1), x=c(x,x1))
# See clearly there is two "regimes"
plot(X$x, X$y)
# As a time series you only see the change in mean
plot(X$y)
# Fit a linear regression on the combined data
lm(y ~ x, X)
#----------------------------------------------------------------
#----------------------------------------------------------------
# Fit a recursive least squares (linear regression) on the combined data
val <- rls(y ~ x, lambda = 0.97, data = X, k = 1)
# Plot the tracked coefficients
colnames(val$Theta) <- c("beta0","beta1")
plot.ts(val$Theta)
plot.ts(val$y)
lines(val$y - val$residuals, col ="red")
#----------------------------------------------------------------
setwd("/home/hgb/git/onlineforecast/")
library(devtools)
library(splines)
library(quantreg)
load_all(".") # need to be under the package directory
## create an fcst matrix of the input data
createFCSTmatrix <- function(vec, kHor) {
# Create a matrix of the temperature measurements
# vec: vector of temperature measurements
# kHor: horizon of the forecast
# Return: matrix of the temperature measurements
N <- length(vec)
M <- length(kHor)
mat <- matrix(NA, nrow = N, ncol = M)
for (i in 1:(N - M)) {
mat[i, ] <- vec[i:(i + M - 1)]
}
colnames(mat) <- paste0("k", kHor)
return(mat)
}
inputX <- createFCSTmatrix(X$x, kHor = 0:24)
D <- list()
D$t <- seq_along(X$x)
D$x <- inputX
D$y <- X$y
## subset the data, as the algorithm does not handle NA! I had forgotten that.
## it is due to the calculation for the simplex, which is not defined for NA
#class(D) <- "data.list"
idx <- in_range(1,D$t,350)
Dtrain <- list()
Dtrain$t <- D$t[idx]
Dtrain$x <- inputX[idx,]
Dtrain$y <- X$y[idx]
## needs to have scoreperiod, for computing the loss
Dtrain$scoreperiod <- in_range(1,D$t,300)
class(Dtrain) <- "data.list"
str(Dtrain)
## Create the model, where N1 is the initial number of data points - for the cold start
model <- qmodel$new(N1 = 30)
## The output is the y
model$output = "y"
## The design matrix, intercept and the x
model$add_inputs(Xinput = "x",
mu = "one()")
## Add the regularization parameter, lambda, and the bounds
model$add_regprm("rls_prm(lambda=0.9)")
# model$add_prmbounds(lambda = c(0.99, 0.999, 0.9999))
### Setup of the booking keeping matrix -
##############################################################
####### THIS SHOULD BE DONE IN quantile_fit.R ################
##############################################################
## First 2 are the intercept and the x
model$IX <- 0:1
## The number of predictors, we could just use the length of the input matrix or the IX
model$K <- 2
## The output column is the 2nd one
model$Iy <- 2
## Select which quantiles we want to optimise and fit
model$tau <- c(0.05,0.2, 0.5, 0.8,0.95)
## Select which prediction horizon
model$kseq <- 0:24
#opt_model <- quantile_optim(model = model, data = Dtrain)
PAR <- c("lambda" = 0.999)
quantile_fit(prm = PAR, model = model, data = Dtrain, q = model$tau[1])
PAR <- c("lambda" = 0.999)
quantile_fit(prm = PAR, model = model, data = Dtrain, q = model$tau[2])
PAR <- c("lambda" = 0.97)
quantile_fit(prm = PAR, model = model, data = Dtrain, q = model$tau[3])
PAR <- c("lambda" = 0.999)
quantile_fit(prm = PAR, model = model, data = Dtrain, q = model$tau[4])
PAR <- c("lambda" = 0.999)
quantile_fit(prm = PAR, model = model, data = Dtrain, q = model$tau[5])
## compute the predictions from the input data and the estimated coefficeints.
Pred_model <- quantile_predict(model = model, datatr = model$datatr)
par(mfrow = c(2,1))
plot(1:length(Dtrain$y), rep(NA, length(Dtrain$y)), type = "l", ylim = range(Dtrain$y))
polygon(c(1:length(Pred_model$q0.05[,"k1"]), rev(1:length(Pred_model$q0.95[,"k1"]))), c(Pred_model$q0.05[,"k1"], rev(Pred_model$q0.95[,"k1"])), col = "grey80", border = NA)
lines(Dtrain$y, col = "black")
lines(Pred_model$q0.5[,"k1"], type = "l", col = "red")
lines(val$y - val$residuals, col ="blue")
legend("bottomleft", legend = c("y", "RLS pred", "Quantile Pred"), col = c("black", "blue", "red"), lty = 1, ncol = 3, bty = "n", cex = 1.4)
plot(val$Theta[,"beta0"], type = "l", col = "blue", ylim = range(val$Theta, na.rm = TRUE))
lines(model$beta$q0.5$k1[,2], lty = 1, col = "red")
lines(val$Theta[,"beta1"], type = "l", lty = 2, col = "blue")
lines(model$beta$q0.5$k1[,1], type = "l", lty = 2, col = "red")
legend(x = 50, y = 0, legend = c("RLS Intercept", "RLS Slope", "Quantile Intercept", "Quantile Slope"), col = c("blue", "blue", "red", "red"), lty = c(1,2,1,2), ncol = 2, bty = "n", cex = 1.4)
misc-R/quantile/css/reset.css 0 → 100755
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− 0
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/* http://meyerweb.com/eric/tools/css/reset/ */
/* v1.0 | 20080212 */
html, body, div, span, applet, object, iframe,
h1, h2, h3, h4, h5, h6, p, blockquote, pre,
a, abbr, acronym, address, big, cite, code,
del, dfn, em, font, img, ins, kbd, q, s, samp,
small, strike, strong, sub, sup, tt, var,
b, u, i, center,
dl, dt, dd, ol, ul, li,
fieldset, form, label, legend,
table, caption, tbody, tfoot, thead, tr, th, td {
margin: 0;
padding: 0;
border: 0;
outline: 0;
font-size: 100%;
vertical-align: baseline;
background: transparent;
}
body {
line-height: 1;
}
ol, ul {
list-style: none;
}
blockquote, q {
quotes: none;
}
blockquote:before, blockquote:after,
q:before, q:after {
content: '';
content: none;
}
/* remember to define focus styles! */
:focus {
outline: 0;
}
/* remember to highlight inserts somehow! */
ins {
text-decoration: none;
}
del {
text-decoration: line-through;
}
/* tables still need 'cellspacing="0"' in the markup */
table {
border-collapse: collapse;
border-spacing: 0;
}
misc-R/quantile/css/scianimator.blue.css 0 → 100755
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View file @ 2a65e98e
/**
* SciAnimator - Scientific Image Animator Plugin for jQuery
*
* Copyright (c) 2010 Brent Ertz
* Released under the MIT license.
* http://github.com/brentertz/scianimator
*/
/* Blue theme */
.scianimator.blue,
.scianimator.blue a,
.scianimator.blue a:visited {
color: #3985a8;
}
.scianimator.blue {
background: #ddeef6;
}
.scianimator.blue .controls,
.scianimator.blue .control {
background-color: #ddeef6;
border: 1px solid #89bad2;
color: #3985a8;
text-shadow: 0 1px #fff;
}
.scianimator.blue .controls {
box-shadow: inset 0 1px 3px #fff, inset 0 -17px #cbe6f2, 0 0 3px #89bad2;
-o-box-shadow: inset 0 1px 3px #fff, inset 0 -17px #cbe6f2, 0 0 3px #89bad2;
-webkit-box-shadow: inset 0 1px 3px #fff, inset 0 -17px #cbe6f2, 0 0 3px #89bad2;
-moz-box-shadow: inset 0 1px 3px #fff, inset 0 -17px #cbe6f2, 0 0 3px #89bad2;
}
.scianimator.blue .control {
box-shadow: inset 0 1px 3px #fff, inset 0 -10px #cbe6f2, 0 0 3px #89bad2;
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-moz-box-shadow: inset 0 1px 3px #fff, inset 0 -10px #cbe6f2, 0 0 3px #89bad2;
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box-shadow: inset 0 1px 3px #fff, inset 0 -10px #cbe6f2, 0 0 3px #3985a8;
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-webkit-box-shadow: inset 0 1px 3px #fff, inset 0 -10px #cbe6f2, 0 0 3px #3985a8;
-moz-box-shadow: inset 0 1px 3px #fff, inset 0 -10px #cbe6f2, 0 0 3px #3985a8;
color: #fff;
text-shadow: 0px 1px #3985a8;
}
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misc-R/quantile/css/scianimator.css 0 → 100755
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− 0
View file @ 2a65e98e
/**
* SciAnimator - Scientific Image Animator Plugin for jQuery
*
* Copyright (c) 2010 Brent Ertz
* Released under the MIT license.
* http://github.com/brentertz/scianimator
*/
/* Comment out @imports for themes not in use */
@import url('scianimator.light.css');
@import url('scianimator.dark.css');
@import url('scianimator.blue.css');
.scianimator {
font-family: 'Lucida Grande', sans-serif;
font-size: 12px;
text-align: center;
margin: auto auto 1em auto;
position: relative;
}
.scianimator .controls,
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border: 1px solid;
border-radius: 4px;
-moz-border-radius: 4px;
font-family: 'Lucida Grande', sans-serif;
font-size: 10px;
text-shadow: 0 1px #fff;
margin: 2px;
position: relative;
}
.scianimator .controls {
margin: 0;
padding: 4px;
position: relative;
}
.scianimator a,
.scianimator a:visited {
cursor: pointer;
display: inline-block;
padding: 5px 10px;
text-decoration: none;
}
.scianimator a.small,
.scianimator a:visited.small {
padding: 1px 2px;
}
.scianimator a:active {
top: 1px;
}
.scianimator select.control,
.scianimator label {
margin: 1px;
vertical-align: middle;
}
.scianimator .control.box {
padding: 1px 5px;
display: inline-block;
vertical-align: top;
}
.scianimator .control.box .control {
margin-left: 0;
margin-right: 0;
}
.scianimator .box.control.navigator {
padding: 2px;
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.scianimator .box a.control.navigator,
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display: inline-block;
height: 10px;
margin: 2px;
padding: 0;
width: 10px;
background: #00FF00;
box-shadow: 0 0 3px 1px #666666 inset, 0 0 1px #00FF00;
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.scianimator .box a.navigator.current,
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box-shadow: 0 0 3px 1px #666666 inset, 0 0 1px blue;
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background: red;
color: #fff;
right: 0;
padding: 5px;
position: absolute;
top: 0;
}
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misc-R/quantile/css/scianimator.dark.css 0 → 100755
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− 0
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/**
* SciAnimator - Scientific Image Animator Plugin for jQuery
*
* Copyright (c) 2010 Brent Ertz
* Released under the MIT license.
* http://github.com/brentertz/scianimator
*/
/* Dark theme */
.scianimator.dark,
.scianimator.dark a,
.scianimator.dark a:visited {
color: #ccc;
}
.scianimator.dark {
background: #333;
}
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background-color: #333;
border-color: #666;
color: #ccc;
text-shadow: 0 1px #000;
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box-shadow: inset 0 1px 3px #fff, inset 0 -17px #000, 0 0 3px #000;
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color: #fff;
text-shadow: 0px 1px #000;
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misc-R/quantile/css/scianimator.light.css 0 → 100755
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/**
* SciAnimator - Scientific Image Animator Plugin for jQuery
*
* Copyright (c) 2010 Brent Ertz
* Released under the MIT license.
* http://github.com/brentertz/scianimator
*/
/* Light theme */
.scianimator.light,
.scianimator.light a,
.scianimator.light a:visited {
color: #555;
}
.scianimator.light {
background: #ddd;
}
.scianimator.light .controls,
.scianimator.light .control {
background-color: #ddd;
border-color: #999;
color: #555;
text-shadow: 0 1px #fff;
}
.scianimator.light .controls {
box-shadow: inset 0 1px 3px #fff, inset 0 -17px #ccc, 0 0 3px #999;
-o-box-shadow: inset 0 1px 3px #fff, inset 0 -17px #ccc, 0 0 3px #999;
-webkit-box-shadow: inset 0 1px 3px #fff, inset 0 -17px #ccc, 0 0 3px #999;
-moz-box-shadow: inset 0 1px 3px #fff, inset 0 -17px #ccc, 0 0 3px #999;
}
.scianimator.light .control {
box-shadow: inset 0 1px 3px #fff, inset 0 -10px #ccc, 0 0 3px #999;
-o-box-shadow: inset 0 1px 3px #fff, inset 0 -10px #ccc, 0 0 3px #999;
-webkit-box-shadow: inset 0 1px 3px #fff, inset 0 -10px #ccc, 0 0 3px #999;
-moz-box-shadow: inset 0 1px 3px #fff, inset 0 -10px #ccc, 0 0 3px #999;
}
.scianimator.light a:hover {
box-shadow: inset 0 1px 3px #fff, inset 0 -10px #ccc, 0 0 3px #555;
-o-box-shadow: inset 0 1px 3px #fff, inset 0 -10px #ccc, 0 0 3px #555;
-webkit-box-shadow: inset 0 1px 3px #fff, inset 0 -10px #ccc, 0 0 3px #555;
-moz-box-shadow: inset 0 1px 3px #fff, inset 0 -10px #ccc, 0 0 3px #555;
color: #fff;
text-shadow: 0px 1px #555;
}
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misc-R/quantile/css/styles.css 0 → 100755
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body {
margin: 20px;
}
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misc-R/quantile/js/Rplot.js 0 → 100644
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(function($) {
$(document).ready(function() {
$('#Rplot').scianimator({
'images': ['images/Rplot1.png', 'images/Rplot2.png', 'images/Rplot3.png', 'images/Rplot4.png', 'images/Rplot5.png', 'images/Rplot6.png', 'images/Rplot7.png', 'images/Rplot8.png', 'images/Rplot9.png', 'images/Rplot10.png', 'images/Rplot11.png', 'images/Rplot12.png', 'images/Rplot13.png', 'images/Rplot14.png', 'images/Rplot15.png', 'images/Rplot16.png', 'images/Rplot17.png', 'images/Rplot18.png', 'images/Rplot19.png', 'images/Rplot20.png', 'images/Rplot21.png', 'images/Rplot22.png', 'images/Rplot23.png', 'images/Rplot24.png', 'images/Rplot25.png', 'images/Rplot26.png', 'images/Rplot27.png', 'images/Rplot28.png', 'images/Rplot29.png', 'images/Rplot30.png', 'images/Rplot31.png', 'images/Rplot32.png', 'images/Rplot33.png', 'images/Rplot34.png', 'images/Rplot35.png', 'images/Rplot36.png', 'images/Rplot37.png', 'images/Rplot38.png', 'images/Rplot39.png', 'images/Rplot40.png', 'images/Rplot41.png', 'images/Rplot42.png', 'images/Rplot43.png', 'images/Rplot44.png', 'images/Rplot45.png', 'images/Rplot46.png', 'images/Rplot47.png', 'images/Rplot48.png', 'images/Rplot49.png', 'images/Rplot50.png', 'images/Rplot51.png', 'images/Rplot52.png', 'images/Rplot53.png', 'images/Rplot54.png', 'images/Rplot55.png', 'images/Rplot56.png', 'images/Rplot57.png', 'images/Rplot58.png', 'images/Rplot59.png', 'images/Rplot60.png', 'images/Rplot61.png', 'images/Rplot62.png', 'images/Rplot63.png', 'images/Rplot64.png', 'images/Rplot65.png', 'images/Rplot66.png', 'images/Rplot67.png', 'images/Rplot68.png', 'images/Rplot69.png', 'images/Rplot70.png', 'images/Rplot71.png', 'images/Rplot72.png', 'images/Rplot73.png', 'images/Rplot74.png', 'images/Rplot75.png', 'images/Rplot76.png', 'images/Rplot77.png', 'images/Rplot78.png', 'images/Rplot79.png', 'images/Rplot80.png', 'images/Rplot81.png', 'images/Rplot82.png', 'images/Rplot83.png', 'images/Rplot84.png', 'images/Rplot85.png', 'images/Rplot86.png', 'images/Rplot87.png', 'images/Rplot88.png', 'images/Rplot89.png', 'images/Rplot90.png', 'images/Rplot91.png', 'images/Rplot92.png', 'images/Rplot93.png', 'images/Rplot94.png', 'images/Rplot95.png', 'images/Rplot96.png', 'images/Rplot97.png', 'images/Rplot98.png', 'images/Rplot99.png', 'images/Rplot100.png', 'images/Rplot101.png', 'images/Rplot102.png', 'images/Rplot103.png', 'images/Rplot104.png', 'images/Rplot105.png', 'images/Rplot106.png', 'images/Rplot107.png', 'images/Rplot108.png', 'images/Rplot109.png', 'images/Rplot110.png', 'images/Rplot111.png', 'images/Rplot112.png', 'images/Rplot113.png', 'images/Rplot114.png', 'images/Rplot115.png', 'images/Rplot116.png', 'images/Rplot117.png', 'images/Rplot118.png', 'images/Rplot119.png', 'images/Rplot120.png', 'images/Rplot121.png', 'images/Rplot122.png', 'images/Rplot123.png', 'images/Rplot124.png', 'images/Rplot125.png', 'images/Rplot126.png', 'images/Rplot127.png', 'images/Rplot128.png', 'images/Rplot129.png', 'images/Rplot130.png', 'images/Rplot131.png', 'images/Rplot132.png', 'images/Rplot133.png', 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'images/Rplot220.png', 'images/Rplot221.png', 'images/Rplot222.png', 'images/Rplot223.png', 'images/Rplot224.png', 'images/Rplot225.png', 'images/Rplot226.png', 'images/Rplot227.png', 'images/Rplot228.png', 'images/Rplot229.png', 'images/Rplot230.png', 'images/Rplot231.png', 'images/Rplot232.png', 'images/Rplot233.png', 'images/Rplot234.png', 'images/Rplot235.png', 'images/Rplot236.png', 'images/Rplot237.png', 'images/Rplot238.png', 'images/Rplot239.png', 'images/Rplot240.png', 'images/Rplot241.png', 'images/Rplot242.png', 'images/Rplot243.png', 'images/Rplot244.png', 'images/Rplot245.png', 'images/Rplot246.png', 'images/Rplot247.png', 'images/Rplot248.png', 'images/Rplot249.png', 'images/Rplot250.png', 'images/Rplot251.png', 'images/Rplot252.png', 'images/Rplot253.png', 'images/Rplot254.png', 'images/Rplot255.png', 'images/Rplot256.png', 'images/Rplot257.png', 'images/Rplot258.png', 'images/Rplot259.png', 'images/Rplot260.png', 'images/Rplot261.png', 'images/Rplot262.png', 'images/Rplot263.png', 'images/Rplot264.png', 'images/Rplot265.png', 'images/Rplot266.png', 'images/Rplot267.png', 'images/Rplot268.png', 'images/Rplot269.png', 'images/Rplot270.png', 'images/Rplot271.png', 'images/Rplot272.png', 'images/Rplot273.png', 'images/Rplot274.png', 'images/Rplot275.png', 'images/Rplot276.png', 'images/Rplot277.png', 'images/Rplot278.png', 'images/Rplot279.png', 'images/Rplot280.png', 'images/Rplot281.png', 'images/Rplot282.png', 'images/Rplot283.png', 'images/Rplot284.png', 'images/Rplot285.png', 'images/Rplot286.png', 'images/Rplot287.png', 'images/Rplot288.png', 'images/Rplot289.png', 'images/Rplot290.png', 'images/Rplot291.png', 'images/Rplot292.png', 'images/Rplot293.png', 'images/Rplot294.png', 'images/Rplot295.png', 'images/Rplot296.png', 'images/Rplot297.png', 'images/Rplot298.png', 'images/Rplot299.png', 'images/Rplot300.png', 'images/Rplot301.png', 'images/Rplot302.png', 'images/Rplot303.png', 'images/Rplot304.png', 'images/Rplot305.png', 'images/Rplot306.png', 'images/Rplot307.png', 'images/Rplot308.png', 'images/Rplot309.png', 'images/Rplot310.png', 'images/Rplot311.png', 'images/Rplot312.png', 'images/Rplot313.png', 'images/Rplot314.png', 'images/Rplot315.png', 'images/Rplot316.png', 'images/Rplot317.png', 'images/Rplot318.png', 'images/Rplot319.png', 'images/Rplot320.png', 'images/Rplot321.png', 'images/Rplot322.png', 'images/Rplot323.png', 'images/Rplot324.png', 'images/Rplot325.png', 'images/Rplot326.png', 'images/Rplot327.png', 'images/Rplot328.png', 'images/Rplot329.png', 'images/Rplot330.png', 'images/Rplot331.png', 'images/Rplot332.png', 'images/Rplot333.png', 'images/Rplot334.png', 'images/Rplot335.png', 'images/Rplot336.png', 'images/Rplot337.png', 'images/Rplot338.png', 'images/Rplot339.png', 'images/Rplot340.png', 'images/Rplot341.png', 'images/Rplot342.png', 'images/Rplot343.png', 'images/Rplot344.png', 'images/Rplot345.png', 'images/Rplot346.png', 'images/Rplot347.png', 'images/Rplot348.png', 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'images/Rplot607.png', 'images/Rplot608.png', 'images/Rplot609.png', 'images/Rplot610.png', 'images/Rplot611.png', 'images/Rplot612.png', 'images/Rplot613.png', 'images/Rplot614.png', 'images/Rplot615.png', 'images/Rplot616.png', 'images/Rplot617.png', 'images/Rplot618.png', 'images/Rplot619.png', 'images/Rplot620.png', 'images/Rplot621.png', 'images/Rplot622.png', 'images/Rplot623.png', 'images/Rplot624.png', 'images/Rplot625.png', 'images/Rplot626.png', 'images/Rplot627.png', 'images/Rplot628.png', 'images/Rplot629.png', 'images/Rplot630.png', 'images/Rplot631.png', 'images/Rplot632.png', 'images/Rplot633.png', 'images/Rplot634.png', 'images/Rplot635.png', 'images/Rplot636.png', 'images/Rplot637.png', 'images/Rplot638.png', 'images/Rplot639.png', 'images/Rplot640.png', 'images/Rplot641.png', 'images/Rplot642.png', 'images/Rplot643.png', 'images/Rplot644.png', 'images/Rplot645.png', 'images/Rplot646.png', 'images/Rplot647.png', 'images/Rplot648.png', 'images/Rplot649.png', 'images/Rplot650.png', 'images/Rplot651.png', 'images/Rplot652.png', 'images/Rplot653.png', 'images/Rplot654.png', 'images/Rplot655.png', 'images/Rplot656.png', 'images/Rplot657.png', 'images/Rplot658.png', 'images/Rplot659.png', 'images/Rplot660.png', 'images/Rplot661.png', 'images/Rplot662.png', 'images/Rplot663.png', 'images/Rplot664.png', 'images/Rplot665.png', 'images/Rplot666.png', 'images/Rplot667.png', 'images/Rplot668.png', 'images/Rplot669.png', 'images/Rplot670.png', 'images/Rplot671.png', 'images/Rplot672.png', 'images/Rplot673.png', 'images/Rplot674.png'],
'width': 1600,
'delay': 1000,
'loopMode': 'loop',
'controls': ['first', 'previous', 'play', 'next', 'last', 'loop', 'speed']
});
$('#Rplot').scianimator('play');
});
})(jQuery);
misc-R/quantile/js/jquery-1.4.4.min.js 0 → 100755
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misc-R/quantile/js/jquery.scianimator.min.js 0 → 100755
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/**
* SciAnimator - Scientific Image Animator Plugin for jQuery
*
* Copyright (c) 2010 Brent Ertz
* Released under the MIT license.
* http://github.com/brentertz/scianimator
*/
(function(e){var c={CONTROLS_ALL:"all",CONTROLS_NONE:"none",DIRECTION_FORWARD:0,DIRECTION_REVERSE:1,LOOP_MODE_NONE:"none",LOOP_MODE_LOOP:"loop",LOOP_MODE_SWEEP:"sweep",PLAY_MODE_STOPPED:0,PLAY_MODE_PLAYING:1,POSITION_TOP:0,POSITION_BOTTOM:1};e.fn.scianimator=function(g){if(b[g]){return b[g].apply(this,Array.prototype.slice.call(arguments,1))}else{if(typeof g==="object"||!g){return b.init.apply(this,arguments)}else{e.error("Method "+g+" does not exist on jQuery.scianimator")}}};e.fn.scianimator.defaults={autoRefresh:false,keyboard:true,debug:false,images:[],controlContainer:null,controlPosition:c.POSITION_BOTTOM,controls:c.CONTROLS_ALL,defaultFrame:0,delay:250,delayStep:50,delayMin:25,delayMax:5000,dwellMultiplier:2,theme:"light",width:null,utf8:true,loopMode:c.LOOP_MODE_LOOP,labels:{text:{first:"First",previous:"Previous",play:"Play",stop:"Stop",next:"Next",last:"Last",loop:{tip:"Click to toggle loop mode",loop:"Loop",sweep:"Sweep",none:"None"},speed:{speed:"Speed",down:"-",up:"+"},navigator:{tip:"Click to go to frame; &lt;ctrl&gt;+click to enable/disable frame."},status:{preload:"Preloading images...",refresh:"Refreshing images from source..."}},utf8:{first:"|&#8592;",previous:"&#8592;",play:"&#9658;",stop:"&#9632;",next:"&#8594;",last:"&#8594;|"}}};var d={};var b={init:function(g){d=e.extend(true,{},e.fn.scianimator.defaults,g);a("init");return this.each(function(){var i=e(this);var h=i.data("scianimator");e(this).data("scianimator",{id:i[0].id,target:i,image:null,animationTimer:null,autoRefreshTimer:null,playMode:c.PLAY_MODE_STOPPED,frames:[],currentFrame:d.defaultFrame,firstFrame:0,lastFrame:null,disabledFrames:[],direction:c.DIRECTION_FORWARD,dwell:0,settings:d,controls:{}});i.scianimator("loadImages","preload").scianimator("container").scianimator("image").scianimator("controls").scianimator("calculateDwell")})},destroy:function(){a("destroy");return this.each(function(){var 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\ No newline at end of file
misc-R/quantile/model.html 0 → 100644
+ 91
− 0
View file @ 2a65e98e
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="R package animation 2.7">
<title>Animations Using the R Language</title>
<link rel="stylesheet" href="css/reset.css" />
<link rel="stylesheet" href="css/styles.css" />
<link rel="stylesheet" href="css/scianimator.css" />
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css">
<script src="js/jquery-1.4.4.min.js"></script>
<script src="js/jquery.scianimator.min.js"></script>
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<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script>
<script>hljs.initHighlightingOnLoad();</script>
</head>
<body>
<div class="scianimator"><div id="Rplot" style="display: inline-block;"></div></div>
<div class="scianimator" style="width: 1600px; text-align: left"><pre><code class="r">## Animations generated in R version 4.4.1 (2024-06-14) using the package animation
library(animation)
library(RColorBrewer)
library(onlineforecast)
library(quantreg)
library(SparseM)
library(testthat)
library(devtools)
library(usethis)
for (i in k:(60)) {
par(mfrow = c(2, 1))
par(mar = c(2, 4, 2, 2))
with(Pred_model, {
plot(Dtrain$t[(i - 10):(i + k)], Dtrain$Ps[(i - 10):(i +
k)], bty = "l", lwd = 2, col = "black", pch = 19,
cex = 0.5, axes = FALSE, xaxt = "n", type = "n",
ylim = range(Dtrain$Ps[(i - 10):(i + k)], q0.5[i,
], q0.95[i, ], q0.05[i, ], na.rm = T), main = "TimeAdaptive with Weights",
xlab = "Time", ylab = "Ps")
axis(2)
polygon(c(Dtrain$t[(i + 1):(i + k)], rev(Dtrain$t[(i +
1):(i + k)])), c(Pred_model$q0.05[i, ], rev(Pred_model$q0.2[i,
])), col = colSeq[3], border = NA)
polygon(c(Dtrain$t[(i + 1):(i + k)], rev(Dtrain$t[(i +
1):(i + k)])), c(Pred_model$q0.8[i, ], rev(Pred_model$q0.95[i,
])), col = colSeq[3], border = NA)
polygon(c(Dtrain$t[(i + 1):(i + k)], rev(Dtrain$t[(i +
1):(i + k)])), c(Pred_model$q0.2[i, ], rev(Pred_model$q0.5[i,
])), col = colSeq[4], border = NA)
polygon(c(Dtrain$t[(i + 1):(i + k)], rev(Dtrain$t[(i +
1):(i + k)])), c(Pred_model$q0.5[i, ], rev(Pred_model$q0.8[i,
])), col = colSeq[4], border = NA)
lines(Dtrain$t[(i - 10):(i + k)], Dtrain$Ps[(i -
10):(i + k)], col = "black", type = "b", lwd = 2)
lines(Dtrain$t[(i + 1):(i + k)], q0.5[i, ], type = "b",
col = "grey", lwd = 2)
axis.POSIXct(side = 1, x = Dtrain$t[(i - 10):(i +
k)], at = seq(from = Dtrain$t[(i - 10):(i + k)][1],
to = Dtrain$t[(i - 10):(i + k)][length(Dtrain$t[(i -
10):(i + k)])], by = "1 hour"), format = "%Y/%m/%d %H:%M \n %a",
las = 1, cex.axis = 1, srt = 45)
plot(Dtrain$t[(i - 10):(i + k)], c(rep(NA, 11), as.numeric(Dtrain$I[i,
1:k])), type = "b", col = "steelblue", axes = FALSE,
xlab = "Time", ylab = "Temp", lwd = 2, ylim = range(Dtrain$I[i,
1:k], Dtrain$Iobs[(i + 1):(i + k)]))
lines(Dtrain$t[(i + 1):(i + k)], Dtrain$Iobs[(i +
1):(i + k)], type = "b", col = "red", lwd = 2)
axis(2)
abline(h = 0, v = Dtrain$t[i], lty = 2, col = "lightgrey",
lwd = 2)
legend("topleft", legend = c("Ta Obs", "Ta Pred"),
col = c("red", "steelblue"), lty = 1, cex = 1,
lwd = 2)
i
axis.POSIXct(side = 1, x = Dtrain$t[(i - 10):(i +
k)], at = seq(from = Dtrain$t[(i - 10):(i + k)][1],
to = Dtrain$t[(i - 10):(i + k)][length(Dtrain$t[(i -
10):(i + k)])], by = "1 hour"), format = "%Y/%m/%d %H:%M \n %a",
las = 1, cex.axis = 1, srt = 45)
})
}
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Other packages: animation 2.7, RColorBrewer 1.1-3, onlineforecast
## 1.0.2, quantreg 5.99, SparseM 1.84-2, testthat 3.2.1.1, devtools 2.4.5,
## usethis 3.0.0</code></pre></div>
<script src="js/Rplot.js"></script>
<!-- highlight R code -->
</body>
</html>
misc-R/quantile/quantile_Solar.R
+ 106
− 11
View file @ 2a65e98e
......@@ -47,29 +47,32 @@ D$scoreperiod <- in_range("2009-06-10", D$t)
Dtrain <- subset(D, in_range(tstart,D$t,tend))
## Create the model, where N1 is the initial number of data points - for the cold start
model <- qmodel$new(N1 = 50)
## The output is the Ps from the Dsolar data
model$output = "Ps"
## The design matrix, here we are using 5 bsplines fixed at 5 and 18 hour of the day - ie when sun is up and down.
model$add_inputs(I = "bspline(tday, df=5, Boundary.knots=c(5,18)) %**% I")
## Add the regularization parameter, lambda, and the bounds
model$add_regprm("rls_prm(lambda=0.9)")
model$add_prmbounds(lambda = c(0.99, 0.999, 0.9999))
### Setup of the booking keeping matrix -
##############################################################
####### THIS SHOULD BE DONE IN quantile_fit.R ################
##############################################################
## First 5 columns are the input Matrix
model$IX <- 0:4
## The number of predictors, we could just use the length of the input matrix or the IX
model$K <- 5
## The output column is the 6th one
model$Iy <- 5
model$K <- 5
## Just quick fun to see the transformation
## maybe have splines that sum to one always..
model$datatr <- model$transform_data(Dtrain)
model$tau <- c(0.05,0.2, 0.5, 0.8,0.95)
model$kseq <- 1:24
#opt_model <- quantile_optim(model = model, data = Dnew)
plot(Dtrain$I$k1[1:100], type = "l")
lines(model$datatr$I.bs1$k1, col = "red")
lines(model$datatr$I.bs2$k1, col = "red")
......@@ -78,6 +81,15 @@ lines(model$datatr$I.bs4$k1, col = "red")
lines(model$datatr$I.bs5$k1, col = "red")
## Select which quantiles we want to optimise and fit
model$tau <- c(0.05,0.2, 0.5, 0.8,0.95)
## Select which prediction horizon
model$kseq <- c(1,4,8,12,24)
model$kseq <- 1:24
#opt_model <- quantile_optim(model = model, data = Dtrain)
PAR <- c("lambda" = 0.999)
quantile_fit(prm = PAR, model = model, data = Dtrain, q = model$tau[1])
PAR <- c("lambda" = 0.999)
......@@ -93,7 +105,11 @@ quantile_fit(prm = PAR, model = model, data = Dtrain, q = model$tau[5])
Pred_model <- quantile_predict(model = model, datatr = model$datatr)
plot(Dtrain$Ps, type = "l")
lines(Pred_model$q0.05[,"k24"], type = "l", col = "green")
lines(Pred_model$q0.5[,"k24"], type = "l", col = "blue")
#lines(Pred_model$q0.5[,"k1"], type = "l", col = "green")
lines(Pred_model$q0.95[,"k24"], type = "l", col = "red")
......@@ -164,3 +180,82 @@ i
}
library(animation)
k <- 24
colSeq <- grey(seq(0.9,0.1,len=3))
#colSeq <- colorJet(nPolygons)
require( RColorBrewer )
colSeq <- brewer.pal(11 , "Spectral" )
saveHTML({
for(i in k:(60)){
par(mfrow = c(2,1))
par(mar = c(2,4,2,2)) # bottom left top right
with(Pred_model, {
plot(Dtrain$t[(i-10):(i+k)], Dtrain$Ps[(i-10):(i+k)], bty = "l", lwd = 2, col = "black", pch=19, cex=0.5, axes = FALSE, xaxt = "n",
type="n", ylim = range(Dtrain$Ps[(i-10):(i+k)], q0.5[i,], q0.95[i,], q0.05[i,], na.rm = T), main = "TimeAdaptive with Weights", xlab = "Time", ylab = "Ps")
axis(2)
polygon(c(Dtrain$t[(i+1):(i+k)], rev(Dtrain$t[(i+1):(i+k)])),
c(Pred_model$q0.05[i,],rev(Pred_model$q0.2[i,])), col= colSeq[3], border = NA)
polygon(c(Dtrain$t[(i+1):(i+k)], rev(Dtrain$t[(i+1):(i+k)])),
c(Pred_model$q0.8[i,],rev(Pred_model$q0.95[i,])), col= colSeq[3], border = NA)
polygon(c(Dtrain$t[(i+1):(i+k)], rev(Dtrain$t[(i+1):(i+k)])),
c(Pred_model$q0.2[i,],rev(Pred_model$q0.5[i,])), col= colSeq[4], border = NA)
polygon(c(Dtrain$t[(i+1):(i+k)], rev(Dtrain$t[(i+1):(i+k)])),
c(Pred_model$q0.5[i,],rev(Pred_model$q0.8[i,])), col= colSeq[4], border = NA)
lines(Dtrain$t[(i-10):(i+k)], Dtrain$Ps[(i-10):(i+k)], col = "black", type = "b", lwd = 2)
lines(Dtrain$t[(i+1):(i+k)], q0.5[i,], type = "b", col = "grey", lwd = 2)
axis.POSIXct(side = 1, x = Dtrain$t[(i-10):(i+k)],
at = seq(from = Dtrain$t[(i-10):(i+k)][1],
to = Dtrain$t[(i-10):(i+k)][length(Dtrain$t[(i-10):(i+k)])],
by = "1 hour"), format = "%Y/%m/%d %H:%M \n %a",
las = 1, cex.axis = 1, srt = 45)
#lines(Pred_model$q0.05[i,], type = "b", col = "blue")
#lines(Pred_model$q0.95[i,], type = "b", col = "blue")
plot(Dtrain$t[(i-10):(i+k)], c(rep(NA,11), as.numeric(Dtrain$I[i, 1:k])), type = "b", col = "steelblue", axes = FALSE, xlab = "Time", ylab = "Temp", lwd = 2, ylim = range(Dtrain$I[i, 1:k], Dtrain$Iobs[(i+1):(i+k)]))
#lines(Dtrain$t[i], Dtrain$Iobs[i+1,1], type = "b", col = "red", axes = FALSE)
lines(Dtrain$t[(i+1):(i+k)], Dtrain$Iobs[(i+1):(i+k)], type = "b", col = "red", lwd = 2)
axis(2)
abline(h=0,v=Dtrain$t[i],lty=2, col = "lightgrey", lwd = 2)
legend("topleft", legend=c("Ta Obs", "Ta Pred"),
col=c("red", "steelblue"), lty=1, cex=1, lwd = 2)
i
axis.POSIXct(side = 1, x = Dtrain$t[(i-10):(i+k)],
at = seq(from = Dtrain$t[(i-10):(i+k)][1],
to = Dtrain$t[(i-10):(i+k)][length(Dtrain$t[(i-10):(i+k)])],
by = "1 hour"), format = "%Y/%m/%d %H:%M \n %a",
las = 1, cex.axis = 1, srt = 45)
})
}}, htmlfile = "misc-R/quantile/model.html", ani.height = 800, ani.width = 1600)
misc-R/quantile/rls.R 0 → 100644
+ 50
− 0
View file @ 2a65e98e
rls <- function(frml, lambda, data, k) {
# R build-in function for setting up linear regression model
mf <- model.frame(as.formula(frml), data, na.action = na.pass)
# The model output
y <- mf[ ,1]
# The design matrix
X <- model.matrix(as.formula(frml), mf)
# The number of observations
n <- nrow(X)
# The number of parameters
p <- ncol(X)
# Parameter matrix
Theta <- matrix(as.numeric(NA), nrow = n, ncol = p)
# The predictions
yhat <- as.numeric(rep(NA,n))
# The parameter vector
theta <- matrix(rep(0,p), ncol = 1)
# Start value for the parameter covariance P
P <- 10000 * diag(1, p)
# Use the inverse in the RLS
R <- solve(P)
# Iterate through and estimate the parameters
for (i in 1:(n-k)) {
x <- matrix(X[i, ])
# Check for NAs in inputs and output
if(all(!is.na(x)) & !is.na(y[i])){
# Update
R <- lambda * R + x %*% t(x)
theta <- theta + solve(R, x) %*% (y[i] - t(x) %*% theta)
Theta[i, ] <- t(theta)
}
# Predict
x <- matrix(X[i+k, ])
if(all(!is.na(x))){
yhat[i+k] <- t(x) %*% theta
}
}
# Return a list
L <- list()
L$residuals <- y - yhat
L$X <- X
L$y <- y
L$Theta <- Theta
return(L)
}
src/rq_init_cpp.cpp
+ 1
− 1
View file @ 2a65e98e
......@@ -18,7 +18,7 @@ Rcpp::List rq_init_cpp(arma::mat X,
if(Lr0 > beta.n_elem){
Rcout << "Worked" << "\n";
// Rcout << "Worked" << "\n";
arma::uvec Irs = sort_index(abs(R));
Irs = Irs.subvec(0,Lr0-1);
arma::mat Xh = X.rows(Irs);
......
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