# R/SegTraj_EM_cpp.R In segclust2d: Bivariate Segmentation/Clustering Methods and Tools

#### Documented in EM.algo_simultanee_CppMstep_simultanee_cpp

```# EM.algo_simultanee C++
#' EM.algo_simultanee calculates the MLE of phi for given change-point instants
#'  and for a fixed number of clusters
#' @param rupt the sequence of change points
#' @param P   number of clusters
#' @param phi starting value for the  parameter
#' @param x bivariate signal
#' @param eps eps
#' @param sameSigma TRUE if segments have the same variance
#' @return a list with  phi, the MLE, tau =(taukj) the probability for segment k
#'   to belong to class,lvinc = lvinc,empty = empty,dv = dv

EM.algo_simultanee_Cpp <- function(x, rupt, P, phi,
eps = 1e-6, sameSigma = FALSE) {
K <- nrow(rupt)
delta <- 1
empty <- 0
dv <- 0
tau <- matrix(1, nrow = K, ncol = P)
iter <- 0
np <- apply(tau, 2, sum)

while ((delta >= 1e-4) & (min(np) > eps) & (iter <= 500)) {
iter <- iter + 1
phi_temp <- phi
# logdensity = t( apply(rupt,1,FUN=function(y) logdens_simultanee(   x[,
# y[1]:y[2] ],phi)))
logdensity <- t(
apply(rupt, 1,
FUN = function(y) logdens_simultanee_cpp(x[, y[1]:y[2]],
phi\$mu,
phi\$sigma,
phi\$prop)
)
)

Estepout <- Estep_simultanee(logdensity, phi)
tau <- Estepout[[1]]

lvinc <- Estepout[[2]]

phi <- Mstep_simultanee_cpp(x, rupt, tau, phi, sameSigma)
np <- apply(tau, 2, sum)

delta <- max(unlist(lapply(names(phi), function(d) {
max(abs(phi_temp[[d]] - phi[[d]]) / phi[[d]])
})))
}

if (min(np) < eps) {
empty <- 1
lvinc <- -Inf
}

if (iter > 5000) {
dv <- 2
lvinc <- -Inf
}

rm(delta, logdensity)

invisible(list(phi = phi, tau = tau, lvinc = lvinc, empty = empty, dv = dv))
}

# Mstep_simultanee C++
#' Mstep_simultanee computes the MLE within the EM framework

#' @param x the bivariate signal
#' @param rupt the rupture dataframe
#' @param phi the parameters of the mixture
#' @param tau the K*P matrix containing posterior probabilities of membership to
#'   clusters
#' @param sameSigma whether segments have the same variance
#' @return phi the updated value of the parameters

Mstep_simultanee_cpp <- function(x, rupt, tau, phi, sameSigma = TRUE) {
K <- nrow(tau)
P <- ncol(tau)
m <- matrix(nrow = 2, ncol = P)
s <- matrix(nrow = 2, ncol = P)
prop <- matrix(nrow = 1, ncol = P)
# Yk = apply(rupt,1,FUN=function(y) rowSums(x[,y[1]:y[2]]))
Yk <- apply_rowSums(rupt, x)
rownames(Yk) <- rownames(x)

nk <- rupt[, 2] - rupt[, 1] + 1
n <- sum(nk)

#
np <- nk %*% tau
m <- Yk %*% tau / rep(np, each = 2)
if (!sameSigma) {
for (i in 1:2) {
# s[i,]=  colSums( tau*(sapply(1:P, function(p)
# {apply(rupt,1,FUN=function(y) sum((x[i,y[1]:y[2]]-m[i,p])^2   ))})))
s[i, ] <- colsums_sapply(i, rupt, x, m, tau)
}
s <- sqrt(s / rep(np, each = 2))
} else {
for (i in 1:2) {
s[i, ] <- rep(sum(tau * (vapply(1:P, function(p) {
apply(rupt, 1, FUN = function(y) sum((x[i, y[1]:y[2]] - m[i, p])^2))
}))), P)
}
s <- sqrt(s / n)
}

# prop = apply(tau,2,sum)/K
# emptyCluster = which(prop==0)
# if(length(emptyCluster)>0){
#   prop = pmax(prop, eps)
#   prop = prop /sum(prop)
#   for (d in emptyCluster){
#     m[,d]=rep(0,2)
#     s[,d]=rep(1e9,2)
#   }
# }

prop <- apply(tau, 2, sum) / K
b <- order(m[1, ])
m <- m[, b]
s <- s[, b]
prop <- prop[b]
phi <- list(mu = m, sigma = s, prop = prop)

invisible(phi)
}
```

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segclust2d documentation built on Oct. 11, 2021, 9:10 a.m.