scale_approx: Approximate implementation of scale algorithm
In mpoll/scale: The Scaleable Langevin Exact Algorithm -- Reference Implementation
Usage
1
scale_approx(p.num, t.inc, T.fin, ss.phi, ss.phiC, dimen, scale_transform, un_scale_transform, T.start = 0, x.init = NULL, ss.size = 1, ess.thresh = 0.5, resamp.method = resid.resamp, neg.wei.mech = scale_zero.wei, prev.simn = NULL, progress.check = FALSE, phi.record = FALSE, resamp.freq = p.num - 1, theta = NULL, p.path.renew = p.path.renew)
Arguments
p.num
t.inc
T.fin
ss.phi
ss.phiC
dimen
scale_transform
un_scale_transform
T.start
x.init
ss.size
ess.thresh
resamp.method
neg.wei.mech
prev.simn
progress.check
phi.record
resamp.freq
theta
p.path.renew
Examples
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (p.num, t.inc, T.fin, ss.phi, ss.phiC, dimen, scale.transform,
un.scale.transform, T.start = 0, x.init = NULL, ss.size = 1,
ess.thresh = 0.5, resamp.method = resid.resamp, neg.wei.mech = scale_zero.wei,
prev.simn = NULL, progress.check = FALSE, phi.record = FALSE,
resamp.freq = p.num - 1, theta = NULL, p.path.renew = p.path.renew)
{
p.mu <- matrix(c(rep(0, dimen)), ncol = 1, dimnames = list(sprintf("dim.%i",
1:dimen), "mean"))
p.covar <- diag(dimen)
dimnames(p.covar) <- list(sprintf("dim.%i", 1:dimen), sprintf("dim.%i",
1:dimen))
if (is.null(prev.simn) == TRUE) {
if (is.null(x.init) == TRUE) {
p.mat <- matrix(0, dimen, p.num, dimnames = list(sprintf("dim.%i",
1:dimen), NULL))
}
else {
if (is.vector(x.init)) {
x.init <- matrix(x.init, length(x.init), 1)
}
if (dim(x.init)[2] == 1) {
x.init <- matrix(rep(x.init, p.num), dimen, p.num)
}
p.mat <- apply(x.init, 2, scale.transform)
dimnames(p.mat) <- list(sprintf("dim.%i", 1:dimen),
NULL)
}
p.idx <- 1:p.num
log.p.wei <- rep(log(1/p.num), p.num)
p.layer <- matrix(0, 7, p.num, dimnames = list(c("c.idx",
"s", "deg.s", "t", "PhiL", "PhiU", "Delta"), NULL))
p.layer["c.idx", ] <- p.idx
p.dapts <- array(sample(dsz, 3 * p.num * ss.size, replace = TRUE),
c(p.num, ss.size, 3), dimnames = list(sprintf("part.%i",
1:p.num), sprintf("ss.%i", 1:ss.size), sprintf("dapt.%i",
1:3)))
}
else {
T.start <- prev.simn$T.fin
p.idx <- prev.simn$p.idx
log.p.wei <- prev.simn$log.p.wei
p.mat <- prev.simn$p.mat
p.layer <- prev.simn$p.layer
p.dapts <- prev.simn$p.dapts
}
p.layer[c("PhiL", "PhiU"), ] <- apply(p.mat, 2, approx.intensity)
p.layer[c("Delta"), ] <- p.layer[c("PhiU"), ] - p.layer[c("PhiL"),
]
p.bet.degrad.r <- p.bet.degrad <- numeric(p.num)
p.layer["s", ] <- p.layer["deg.s", ] <- T.start
p.layer["t", ] <- p.layer["deg.s", ] + rexp(p.num, rate = p.layer["Delta",
])
p.anc.idx <- matrix(p.idx, 1, p.num, dimnames = list(NULL,
sprintf("pos.%i", 1:p.num)))
p.anc.wei <- matrix(part.normal(log.p.wei)$p.wei, 1, p.num,
dimnames = list(NULL, sprintf("part.%i", 1:p.num)))
p.anc.mat <- array(p.mat, c(dimen, p.num, 1), dimnames = list(sprintf("dim.%i",
1:dimen), sprintf("part.%i", 1:p.num), NULL))
p.phi.hist <- matrix(0, 0, dimen + 1, dimnames = list(NULL,
c("ss.phi", sprintf("dim.%i", 1:dimen))))
p.anc.resamp <- numeric(0)
p.anc.ess <- numeric(0)
p.anc.neg <- matrix(0, 0, 5, dimnames = list(NULL, c("event.ch",
"wt-", "t", "PhiL", "PhiU")))
p.layer <- mat.sort.r(p.layer, p.num, "t")
anc.times <- curr.deg.time <- T.start
t.inc.next <- T.start + t.inc
resamp.counter <- 1
while (curr.deg.time < T.fin) {
if (p.layer["t", 1] >= t.inc.next) {
p.layer <- mat.sort.r(p.layer, p.num, "c.idx")
p.mat <- p.mat + t(sqrt(t.inc.next - p.layer["s",
]) * mvrnorm(p.num, p.mu, p.covar))
p.bet.degrad <- cbind(p.bet.degrad, rbind(p.layer[c("c.idx"),
], (t.inc.next - p.layer["deg.s", ]) * p.layer["PhiL",
]))
p.wei.agg <- aggregate(x = p.bet.degrad["degrad",
], by = list(p.bet.degrad["c.idx", ]), FUN = "sum")
log.p.wei <- log.p.wei - p.wei.agg[, "x"]
anc.times <- c(anc.times, t.inc.next)
p.anc.idx <- rbind(p.anc.idx, p.idx)
p.idx <- 1:p.num
p.normalise <- part.normal(log.p.wei)
log.p.wei <- p.normalise$log.p.wei
p.anc.wei <- rbind(p.anc.wei, p.normalise$p.wei)
p.anc.mat <- abind(p.anc.mat, array(p.mat, c(dimen,
p.num, 1)), along = 3)
curr.deg.time <- t.inc.next
p.layer["deg.s", ] <- p.layer["s", ] <- curr.deg.time
t.inc.next <- min(curr.deg.time + t.inc, T.fin)
p.resamp <- scale_resamp(p.num, p.idx, log.p.wei,
p.mat, p.layer, p.layer.sor.I = 1, p.dapts, ss.size,
resamp.method, ess.thresh)
p.anc.ess <- c(p.anc.ess, p.resamp$ess)
p.idx <- p.resamp$p.idx
log.p.wei <- p.resamp$log.p.wei
if (p.resamp$resamp.I == 1) {
p.anc.resamp <- c(p.anc.resamp, curr.deg.time)
p.mat <- p.resamp$p.mat
p.layer <- p.resamp$p.layer
p.dapts <- p.resamp$p.dapts
}
p.layer <- mat.sort.r(p.layer, p.num, "t")
resamp.counter <- 1
if (progress.check == TRUE) {
print(curr.deg.time)
}
}
else {
p.curr <- p.layer[, 1]
p.curr.idx <- p.curr["c.idx"]
p.curr.dapts <- p.dapts[p.curr.idx, , , drop = FALSE]
p.loc.next <- p.mat[, p.curr.idx] + sqrt(p.curr["t"] -
p.curr["s"]) * mvrnorm(1, p.mu, p.covar)
p.phi.eval <- ss.phi(p.loc.next, p.curr.dapts, ss.size)
p.event.ch <- (p.curr["PhiU"] - p.phi.eval)/p.curr["Delta"]
if (phi.record == TRUE) {
p.phi.hist <- rbind(p.phi.hist, c(p.phi.eval,
p.loc.next))
}
if (p.event.ch < 0) {
p.neg <- neg.wei.mech(p.event.ch, p.curr.idx,
p.curr, p.anc.neg, log.p.wei, ss.phiC, ss.phiC.up)
log.p.wei[p.curr.idx] <- -Inf
p.anc.neg <- rbind(p.anc.neg, p.neg$p.anc.neg.append)
ss.phiC <- p.neg$ss.phiC
}
else {
log.p.wei[p.curr.idx] <- log.p.wei[p.curr.idx] +
log(p.event.ch)
}
p.bet.degrad <- cbind(p.bet.degrad, c(p.curr.idx,
(p.curr["t"] - p.curr["deg.s"]) * p.curr[c("PhiL")]))
p.mat[, p.curr.idx] <- p.loc.next
p.dapts[p.curr.idx, , ] <- sample(dsz, 3 * ss.size,
replace = TRUE)
p.phi.bds <- ss.phiC(p.loc.next)
p.curr[c("PhiL", "PhiU")] <- c(p.phi.bds$phiL, p.phi.bds$phiU)
p.curr["Delta"] <- p.curr["PhiU"] - p.curr["PhiL"]
p.curr["s"] <- p.curr["deg.s"] <- p.curr["t"]
p.curr["t"] <- p.curr["deg.s"] + rexp(1, rate = p.curr["Delta"])
p.layer <- ins.sort.r(p.curr, p.layer[, -1], p.num -
1, "t")
if (resamp.counter%%(resamp.freq) == 0) {
p.bet.degrad <- cbind(p.bet.degrad, rbind(p.layer[c("c.idx"),
], (p.curr["deg.s"] - p.layer["deg.s", ]) *
p.layer["PhiL", ]))
p.wei.agg <- aggregate(x = p.bet.degrad["degrad",
], by = list(p.bet.degrad["c.idx", ]), FUN = "sum")
log.p.wei <- log.p.wei - p.wei.agg[, "x"]
p.layer["deg.s", ] <- curr.deg.time <- p.curr["deg.s"]
p.bet.degrad <- p.bet.degrad.r
p.resamp <- scale_resamp(p.num, p.idx, log.p.wei,
p.mat, p.layer, p.layer.sor.I = 0, p.dapts,
ss.size, resamp.method, ess.thresh)
p.idx <- p.resamp$p.idx
log.p.wei <- p.resamp$log.p.wei
if (p.resamp$resamp.I == 1) {
p.anc.resamp <- c(p.anc.resamp, curr.deg.time)
p.mat <- p.resamp$p.mat
p.layer <- p.resamp$p.layer
p.dapts <- p.resamp$p.dapts
}
}
resamp.counter <- resamp.counter + 1
}
}
list(p.num = p.num, p.idx = p.idx, log.p.wei = log.p.wei,
p.mat = p.mat, p.layer = p.layer, theta = theta, p.dapts = p.dapts,
p.anc.idx = p.anc.idx, p.anc.wei = p.anc.wei, p.anc.mat = p.anc.mat,
resamp.freq = resamp.freq, resamp.method = resamp.method,
neg.wei.mech = neg.wei.mech, p.anc.resamp = p.anc.resamp,
p.anc.ess = p.anc.ess, p.anc.neg = p.anc.neg, p.phi.hist = p.phi.hist,
anc.times = anc.times, T.start = T.start, t.inc = t.inc,
T.fin = T.fin, dimen = dimen, p.mu = p.mu, ss.size = ss.size,
ess.thresh = ess.thresh, ss.phi = ss.phi, ss.phiC = ss.phiC,
p.cyc.arr = NULL, p.pass.arr = NULL, scale.transform = scale.transform,
un.scale.transform = un.scale.transform, progress.check = progress.check,
phi.record = phi.record, p.path.renew = p.path.renew)
}
mpoll/scale documentation built on Dec. 9, 2019, 7:15 a.m.
R Package Documentation
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Usage
1 | scale_approx(p.num, t.inc, T.fin, ss.phi, ss.phiC, dimen, scale_transform, un_scale_transform, T.start = 0, x.init = NULL, ss.size = 1, ess.thresh = 0.5, resamp.method = resid.resamp, neg.wei.mech = scale_zero.wei, prev.simn = NULL, progress.check = FALSE, phi.record = FALSE, resamp.freq = p.num - 1, theta = NULL, p.path.renew = p.path.renew)
|
Arguments
p.num |
|
t.inc |
|
T.fin |
|
ss.phi |
|
ss.phiC |
|
dimen |
|
scale_transform |
|
un_scale_transform |
|
T.start |
|
x.init |
|
ss.size |
|
ess.thresh |
|
resamp.method |
|
neg.wei.mech |
|
prev.simn |
|
progress.check |
|
phi.record |
|
resamp.freq |
|
theta |
|
p.path.renew |
Examples
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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (p.num, t.inc, T.fin, ss.phi, ss.phiC, dimen, scale.transform,
un.scale.transform, T.start = 0, x.init = NULL, ss.size = 1,
ess.thresh = 0.5, resamp.method = resid.resamp, neg.wei.mech = scale_zero.wei,
prev.simn = NULL, progress.check = FALSE, phi.record = FALSE,
resamp.freq = p.num - 1, theta = NULL, p.path.renew = p.path.renew)
{
p.mu <- matrix(c(rep(0, dimen)), ncol = 1, dimnames = list(sprintf("dim.%i",
1:dimen), "mean"))
p.covar <- diag(dimen)
dimnames(p.covar) <- list(sprintf("dim.%i", 1:dimen), sprintf("dim.%i",
1:dimen))
if (is.null(prev.simn) == TRUE) {
if (is.null(x.init) == TRUE) {
p.mat <- matrix(0, dimen, p.num, dimnames = list(sprintf("dim.%i",
1:dimen), NULL))
}
else {
if (is.vector(x.init)) {
x.init <- matrix(x.init, length(x.init), 1)
}
if (dim(x.init)[2] == 1) {
x.init <- matrix(rep(x.init, p.num), dimen, p.num)
}
p.mat <- apply(x.init, 2, scale.transform)
dimnames(p.mat) <- list(sprintf("dim.%i", 1:dimen),
NULL)
}
p.idx <- 1:p.num
log.p.wei <- rep(log(1/p.num), p.num)
p.layer <- matrix(0, 7, p.num, dimnames = list(c("c.idx",
"s", "deg.s", "t", "PhiL", "PhiU", "Delta"), NULL))
p.layer["c.idx", ] <- p.idx
p.dapts <- array(sample(dsz, 3 * p.num * ss.size, replace = TRUE),
c(p.num, ss.size, 3), dimnames = list(sprintf("part.%i",
1:p.num), sprintf("ss.%i", 1:ss.size), sprintf("dapt.%i",
1:3)))
}
else {
T.start <- prev.simn$T.fin
p.idx <- prev.simn$p.idx
log.p.wei <- prev.simn$log.p.wei
p.mat <- prev.simn$p.mat
p.layer <- prev.simn$p.layer
p.dapts <- prev.simn$p.dapts
}
p.layer[c("PhiL", "PhiU"), ] <- apply(p.mat, 2, approx.intensity)
p.layer[c("Delta"), ] <- p.layer[c("PhiU"), ] - p.layer[c("PhiL"),
]
p.bet.degrad.r <- p.bet.degrad <- numeric(p.num)
p.layer["s", ] <- p.layer["deg.s", ] <- T.start
p.layer["t", ] <- p.layer["deg.s", ] + rexp(p.num, rate = p.layer["Delta",
])
p.anc.idx <- matrix(p.idx, 1, p.num, dimnames = list(NULL,
sprintf("pos.%i", 1:p.num)))
p.anc.wei <- matrix(part.normal(log.p.wei)$p.wei, 1, p.num,
dimnames = list(NULL, sprintf("part.%i", 1:p.num)))
p.anc.mat <- array(p.mat, c(dimen, p.num, 1), dimnames = list(sprintf("dim.%i",
1:dimen), sprintf("part.%i", 1:p.num), NULL))
p.phi.hist <- matrix(0, 0, dimen + 1, dimnames = list(NULL,
c("ss.phi", sprintf("dim.%i", 1:dimen))))
p.anc.resamp <- numeric(0)
p.anc.ess <- numeric(0)
p.anc.neg <- matrix(0, 0, 5, dimnames = list(NULL, c("event.ch",
"wt-", "t", "PhiL", "PhiU")))
p.layer <- mat.sort.r(p.layer, p.num, "t")
anc.times <- curr.deg.time <- T.start
t.inc.next <- T.start + t.inc
resamp.counter <- 1
while (curr.deg.time < T.fin) {
if (p.layer["t", 1] >= t.inc.next) {
p.layer <- mat.sort.r(p.layer, p.num, "c.idx")
p.mat <- p.mat + t(sqrt(t.inc.next - p.layer["s",
]) * mvrnorm(p.num, p.mu, p.covar))
p.bet.degrad <- cbind(p.bet.degrad, rbind(p.layer[c("c.idx"),
], (t.inc.next - p.layer["deg.s", ]) * p.layer["PhiL",
]))
p.wei.agg <- aggregate(x = p.bet.degrad["degrad",
], by = list(p.bet.degrad["c.idx", ]), FUN = "sum")
log.p.wei <- log.p.wei - p.wei.agg[, "x"]
anc.times <- c(anc.times, t.inc.next)
p.anc.idx <- rbind(p.anc.idx, p.idx)
p.idx <- 1:p.num
p.normalise <- part.normal(log.p.wei)
log.p.wei <- p.normalise$log.p.wei
p.anc.wei <- rbind(p.anc.wei, p.normalise$p.wei)
p.anc.mat <- abind(p.anc.mat, array(p.mat, c(dimen,
p.num, 1)), along = 3)
curr.deg.time <- t.inc.next
p.layer["deg.s", ] <- p.layer["s", ] <- curr.deg.time
t.inc.next <- min(curr.deg.time + t.inc, T.fin)
p.resamp <- scale_resamp(p.num, p.idx, log.p.wei,
p.mat, p.layer, p.layer.sor.I = 1, p.dapts, ss.size,
resamp.method, ess.thresh)
p.anc.ess <- c(p.anc.ess, p.resamp$ess)
p.idx <- p.resamp$p.idx
log.p.wei <- p.resamp$log.p.wei
if (p.resamp$resamp.I == 1) {
p.anc.resamp <- c(p.anc.resamp, curr.deg.time)
p.mat <- p.resamp$p.mat
p.layer <- p.resamp$p.layer
p.dapts <- p.resamp$p.dapts
}
p.layer <- mat.sort.r(p.layer, p.num, "t")
resamp.counter <- 1
if (progress.check == TRUE) {
print(curr.deg.time)
}
}
else {
p.curr <- p.layer[, 1]
p.curr.idx <- p.curr["c.idx"]
p.curr.dapts <- p.dapts[p.curr.idx, , , drop = FALSE]
p.loc.next <- p.mat[, p.curr.idx] + sqrt(p.curr["t"] -
p.curr["s"]) * mvrnorm(1, p.mu, p.covar)
p.phi.eval <- ss.phi(p.loc.next, p.curr.dapts, ss.size)
p.event.ch <- (p.curr["PhiU"] - p.phi.eval)/p.curr["Delta"]
if (phi.record == TRUE) {
p.phi.hist <- rbind(p.phi.hist, c(p.phi.eval,
p.loc.next))
}
if (p.event.ch < 0) {
p.neg <- neg.wei.mech(p.event.ch, p.curr.idx,
p.curr, p.anc.neg, log.p.wei, ss.phiC, ss.phiC.up)
log.p.wei[p.curr.idx] <- -Inf
p.anc.neg <- rbind(p.anc.neg, p.neg$p.anc.neg.append)
ss.phiC <- p.neg$ss.phiC
}
else {
log.p.wei[p.curr.idx] <- log.p.wei[p.curr.idx] +
log(p.event.ch)
}
p.bet.degrad <- cbind(p.bet.degrad, c(p.curr.idx,
(p.curr["t"] - p.curr["deg.s"]) * p.curr[c("PhiL")]))
p.mat[, p.curr.idx] <- p.loc.next
p.dapts[p.curr.idx, , ] <- sample(dsz, 3 * ss.size,
replace = TRUE)
p.phi.bds <- ss.phiC(p.loc.next)
p.curr[c("PhiL", "PhiU")] <- c(p.phi.bds$phiL, p.phi.bds$phiU)
p.curr["Delta"] <- p.curr["PhiU"] - p.curr["PhiL"]
p.curr["s"] <- p.curr["deg.s"] <- p.curr["t"]
p.curr["t"] <- p.curr["deg.s"] + rexp(1, rate = p.curr["Delta"])
p.layer <- ins.sort.r(p.curr, p.layer[, -1], p.num -
1, "t")
if (resamp.counter%%(resamp.freq) == 0) {
p.bet.degrad <- cbind(p.bet.degrad, rbind(p.layer[c("c.idx"),
], (p.curr["deg.s"] - p.layer["deg.s", ]) *
p.layer["PhiL", ]))
p.wei.agg <- aggregate(x = p.bet.degrad["degrad",
], by = list(p.bet.degrad["c.idx", ]), FUN = "sum")
log.p.wei <- log.p.wei - p.wei.agg[, "x"]
p.layer["deg.s", ] <- curr.deg.time <- p.curr["deg.s"]
p.bet.degrad <- p.bet.degrad.r
p.resamp <- scale_resamp(p.num, p.idx, log.p.wei,
p.mat, p.layer, p.layer.sor.I = 0, p.dapts,
ss.size, resamp.method, ess.thresh)
p.idx <- p.resamp$p.idx
log.p.wei <- p.resamp$log.p.wei
if (p.resamp$resamp.I == 1) {
p.anc.resamp <- c(p.anc.resamp, curr.deg.time)
p.mat <- p.resamp$p.mat
p.layer <- p.resamp$p.layer
p.dapts <- p.resamp$p.dapts
}
}
resamp.counter <- resamp.counter + 1
}
}
list(p.num = p.num, p.idx = p.idx, log.p.wei = log.p.wei,
p.mat = p.mat, p.layer = p.layer, theta = theta, p.dapts = p.dapts,
p.anc.idx = p.anc.idx, p.anc.wei = p.anc.wei, p.anc.mat = p.anc.mat,
resamp.freq = resamp.freq, resamp.method = resamp.method,
neg.wei.mech = neg.wei.mech, p.anc.resamp = p.anc.resamp,
p.anc.ess = p.anc.ess, p.anc.neg = p.anc.neg, p.phi.hist = p.phi.hist,
anc.times = anc.times, T.start = T.start, t.inc = t.inc,
T.fin = T.fin, dimen = dimen, p.mu = p.mu, ss.size = ss.size,
ess.thresh = ess.thresh, ss.phi = ss.phi, ss.phiC = ss.phiC,
p.cyc.arr = NULL, p.pass.arr = NULL, scale.transform = scale.transform,
un.scale.transform = un.scale.transform, progress.check = progress.check,
phi.record = phi.record, p.path.renew = p.path.renew)
}
|
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