R/segTraj_neighborsbis.R

Defines functions neighborsbis

Documented in neighborsbis

# neighbors
#' neighbors tests whether neighbors of point k,P can  be used to re-initialize
#' the EM algorithm and to improve the log-likelihood.
#' @param x the initial dataset
#' @param L the likelihood
#' @param k the points of interest
#' @param P the number of class
#' @param lmin minimal size of the segment to be implemented
#' @param kv.hull convex hull of likelihood
#' @param param param outputs of segmentation
#' @param eps eps
#' @param sameSigma should segments have same variance ?
#' @param pureR should algorithm use only R functions or benefit from Rcpp
#'   faster algorithm
#' @return smoothing likelihood
#'
neighborsbis <- function(kv.hull,
                         x, L, k,
                         param, P, lmin, eps,
                         sameSigma = TRUE, pureR = FALSE) {
  for (j in seq_along(kv.hull)) {
    K1 <- kv.hull[j]
    a <- L[K1]
    if (a == -Inf) {
      K1 <- -Inf
      phi1 <- initialisePhi(P = P)
      out.EM1 <- list(lvinc = -Inf)
    } else {
      phi1 <- param[[K1]]$phi
      if (pureR) {
        ## computing the cost matrix
        G <- Gmixt_simultanee(x, lmin, phi1)

        ## producing the best segmentation
        ## with the given cost matrix in k segment
        out.DP <- DynProg(G, k)
      } else {
        G <- Gmixt_simultanee_fullcpp(x,
          lmin = lmin,
          phi1$prop,
          phi1$mu,
          phi1$sigma
        )
        out.DP <- wrap_dynprog_cpp(G, k)
      }
      t.est <- out.DP$t.est
      J.est <- out.DP$J.est
      rupt1 <- matrix(ncol = 2, c(c(1, t.est[k, 1:(k - 1)] + 1), t.est[k, ]))
      if (pureR) {
        out.EM1 <- EM.algo_simultanee(
          x = x,
          rupt = rupt1,
          P = P,
          phi = phi1,
          eps, sameSigma
        )
      } else {
        out.EM1 <- EM.algo_simultanee_Cpp(
          x = x,
          rupt = rupt1,
          P = P,
          phi = phi1,
          eps, sameSigma
        )
      }
    } # end else
    if (out.EM1$lvinc > L[k]) {
      param[[k]] <- list(
        phi = out.EM1$phi,
        rupt = rupt1,
        tau = out.EM1$tau,
        cluster = apply(out.EM1$tau, 1, which.max)
      )
      L[k] <- out.EM1$lvinc
    }
  }

  invisible(list(L = L, param = param))
} # end function

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