# Comparison with vanilla PELT

# Setup

## Logistic regression

    #' Cost function for Logistic regression, i.e. binomial family in GLM.
    #'
    #' @param data Data to be used to calculate the cost values. The last column is
    #'     the response variable.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Cost value for the corresponding segment of data.
    cost_glm_binomial <- function(data, family = "binomial") {
      data <- as.matrix(data)
      p <- dim(data)[2] - 1
      out <- fastglm::fastglm(
        as.matrix(data[, 1:p]), data[, p + 1],
        family = family
      )
      return(out$deviance / 2)
    }

    #' Implementation of vanilla PELT for logistic regression type data.
    #'
    #' @param data Data to be used for change point detection.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param cost Cost function to be used to calculate cost values.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list consisting of two: change point locations and negative log
    #'     likelihood values for each segment.
    pelt_vanilla_binomial <- function(data, beta, cost = cost_glm_binomial) {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)
      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)
        for (i in 1:m)
        {
          k <- set[i] + 1
          cval[i] <- 0
          if (t - k >= p - 1) cval[i] <- suppressWarnings(cost(data[k:t, ]))
        }
        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        Fobj <- c(Fobj, min_val)
      }
      cp <- cp_set[[n + 1]]
      nLL <- 0
      cp_loc <- unique(c(0, cp, n))
      for (i in 1:(length(cp_loc) - 1))
      {
        seg <- (cp_loc[i] + 1):cp_loc[i + 1]
        data_seg <- data[seg, ]
        out <- fastglm::fastglm(
          as.matrix(data_seg[, 1:p]), data_seg[, p + 1],
          family = "binomial"
        )
        nLL <- out$deviance / 2 + nLL
      }

      output <- list(cp, nLL)
      names(output) <- c("cp", "nLL")
      return(output)
    }

    #' Function to update the coefficients using gradient descent.
    #'
    #' @param data_new New data point used to update the coeffient.
    #' @param coef Previous coeffient to be updated.
    #' @param cum_coef Summation of all the past coefficients to be used in
    #'     averaging.
    #' @param cmatrix Hessian matrix in gradient descent.
    #' @param epsilon Small adjustment to avoid singularity when doing inverse on
    #'     the Hessian matrix.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list of values containing the new coefficients, summation of
    #'     coefficients so far and all the Hessian matrices.
    cost_logistic_update <- function(
        data_new, coef, cum_coef, cmatrix, epsilon = 1e-10) {
      p <- length(data_new) - 1
      X_new <- data_new[1:p]
      Y_new <- data_new[p + 1]
      eta <- X_new %*% coef
      mu <- 1 / (1 + exp(-eta))
      cmatrix <- cmatrix + (X_new %o% X_new) * as.numeric((1 - mu) * mu)
      lik_dev <- as.numeric(-(Y_new - mu)) * X_new
      coef <- coef - solve(cmatrix + epsilon * diag(1, p), lik_dev)
      cum_coef <- cum_coef + coef
      return(list(coef, cum_coef, cmatrix))
    }

    #' Calculate negative log likelihood given data segment and guess of
    #' coefficient.
    #'
    #' @param data Data to be used to calculate the negative log likelihood.
    #' @param b Guess of the coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Negative log likelihood.
    neg_log_lik_binomial <- function(data, b) {
      p <- dim(data)[2] - 1
      X <- data[, 1:p, drop = FALSE]
      Y <- data[, p + 1, drop = FALSE]
      u <- as.numeric(X %*% b)
      L <- -Y * u + log(1 + exp(u))
      return(sum(L))
    }

    #' Find change points using dynamic programming with pruning and SeGD.
    #'
    #' @param data Data used to find change points.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param B Initial guess on the number of change points.
    #' @param trim Propotion of the data to ignore the change points at the
    #'     beginning, ending and between change points.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing potential change point locations and negative log
    #'     likelihood for each segment based on the change points guess.
    segd_binomial <- function(data, beta, B = 10, trim = 0.025) {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)

      # choose the initial values based on pre-segmentation

      index <- rep(1:B, rep(n / B, B))
      coef.int <- matrix(NA, B, p)
      for (i in 1:B)
      {
        out <- fastglm::fastglm(
          as.matrix(data[index == i, 1:p]),
          data[index == i, p + 1],
          family = "binomial"
        )
        coef.int[i, ] <- coef(out)
      }
      X1 <- data[1, 1:p]
      cum_coef <- coef <- matrix(coef.int[1, ], p, 1)
      e_eta <- exp(coef %*% X1)
      const <- e_eta / (1 + e_eta)^2
      cmatrix <- array((X1 %o% X1) * as.numeric(const), c(p, p, 1))

      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)

        for (i in 1:(m - 1))
        {
          coef_c <- coef[, i]
          cum_coef_c <- cum_coef[, i]
          cmatrix_c <- cmatrix[, , i]
          out <- cost_logistic_update(data[t, ], coef_c, cum_coef_c, cmatrix_c)
          coef[, i] <- out[[1]]
          cum_coef[, i] <- out[[2]]
          cmatrix[, , i] <- out[[3]]
          k <- set[i] + 1
          cval[i] <- 0
          if (t - k >= p - 1) {
            cval[i] <-
              neg_log_lik_binomial(data[k:t, ], cum_coef[, i] / (t - k + 1))
          }
        }

        # the choice of initial values requires further investigation

        cval[m] <- 0
        Xt <- data[t, 1:p]
        cum_coef_add <- coef_add <- coef.int[index[t], ]
        e_eta_t <- exp(coef_add %*% Xt)
        const <- e_eta_t / (1 + e_eta_t)^2
        cmatrix_add <- (Xt %o% Xt) * as.numeric(const)

        coef <- cbind(coef, coef_add)
        cum_coef <- cbind(cum_coef, cum_coef_add)
        cmatrix <- abind::abind(cmatrix, cmatrix_add, along = 3)

        # Adding a momentum term (TBD)

        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        coef <- coef[, ind2, drop = FALSE]
        cum_coef <- cum_coef[, ind2, drop = FALSE]
        cmatrix <- cmatrix[, , ind2, drop = FALSE]
        Fobj <- c(Fobj, min_val)
      }

      # Remove change-points close to the boundaries

      cp <- cp_set[[n + 1]]
      if (length(cp) > 0) {
        ind3 <- (seq_len(length(cp)))[(cp < trim * n) | (cp > (1 - trim) * n)]
        cp <- cp[-ind3]
      }

      nLL <- 0
      cp_loc <- unique(c(0, cp, n))
      for (i in 1:(length(cp_loc) - 1))
      {
        seg <- (cp_loc[i] + 1):cp_loc[i + 1]
        data_seg <- data[seg, ]
        out <- fastglm::fastglm(
          as.matrix(data_seg[, 1:p]), data_seg[, p + 1],
          family = "binomial"
        )
        nLL <- out$deviance / 2 + nLL
      }

      output <- list(cp, nLL)
      names(output) <- c("cp", "nLL")
      return(output)
    }

## Poisson regression

    #' Cost function for Poisson regression.
    #'
    #' @param data Data to be used to calculate the cost values. The last column is
    #'     the response variable.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Cost value for the corresponding segment of data.
    cost_glm_poisson <- function(data, family = "poisson") {
      data <- as.matrix(data)
      p <- dim(data)[2] - 1
      out <- fastglm::fastglm(as.matrix(data[, 1:p]), data[, p + 1], family = family)
      return(out$deviance / 2)
    }

    #' Implementation of vanilla PELT for poisson regression type data.
    #'
    #' @param data Data to be used for change point detection.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param cost Cost function to be used to calculate cost values.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list consisting of two: change point locations and negative log
    #'     likelihood values for each segment.
    pelt_vanilla_poisson <- function(data, beta, cost = cost_glm_poisson) {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)
      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)
        for (i in 1:m)
        {
          k <- set[i] + 1
          if (t - k >= p - 1) cval[i] <- suppressWarnings(cost(data[k:t, ])) else cval[i] <- 0
        }
        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        Fobj <- c(Fobj, min_val)
        # if (t %% 100 == 0) print(t)
      }
      cp <- cp_set[[n + 1]]
      output <- list(cp)
      names(output) <- c("cp")

      return(output)
    }

    #' Function to update the coefficients using gradient descent.
    #'
    #' @param data_new New data point used to update the coeffient.
    #' @param coef Previous coeffient to be updated.
    #' @param cum_coef Summation of all the past coefficients to be used in
    #'     averaging.
    #' @param cmatrix Hessian matrix in gradient descent.
    #' @param epsilon Small adjustment to avoid singularity when doing inverse on
    #'     the Hessian matrix.
    #' @param G Upper bound for the coefficient.
    #' @param L Winsorization lower bound.
    #' @param H Winsorization upper bound.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list of values containing the new coefficients, summation of
    #'     coefficients so far and all the Hessian matrices.
    cost_poisson_update <- function(data_new, coef, cum_coef, cmatrix, epsilon = 0.001, G = 10^10, L = -20, H = 20) {
      p <- length(data_new) - 1
      X_new <- data_new[1:p]
      Y_new <- data_new[p + 1]
      eta <- X_new %*% coef
      mu <- exp(eta)
      cmatrix <- cmatrix + (X_new %o% X_new) * min(as.numeric(mu), G)
      lik_dev <- as.numeric(-(Y_new - mu)) * X_new
      coef <- coef - solve(cmatrix + epsilon * diag(1, p), lik_dev)
      coef <- pmin(pmax(coef, L), H)
      cum_coef <- cum_coef + coef
      return(list(coef, cum_coef, cmatrix))
    }

    #' Calculate negative log likelihood given data segment and guess of
    #' coefficient.
    #'
    #' @param data Data to be used to calculate the negative log likelihood.
    #' @param b Guess of the coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Negative log likelihood.
    neg_log_lik_poisson <- function(data, b) {
      p <- dim(data)[2] - 1
      X <- data[, 1:p, drop = FALSE]
      Y <- data[, p + 1, drop = FALSE]
      u <- as.numeric(X %*% b)
      L <- -Y * u + exp(u) + lfactorial(Y)
      return(sum(L))
    }

    #' Find change points using dynamic programming with pruning and SeGD.
    #'
    #' @param data Data used to find change points.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param B Initial guess on the number of change points.
    #' @param trim Propotion of the data to ignore the change points at the
    #'     beginning, ending and between change points.
    #' @param epsilon Small adjustment to avoid singularity when doing inverse on
    #'    the Hessian matrix.
    #' @param G Upper bound for the coefficient.
    #' @param L Winsorization lower bound.
    #' @param H Winsorization upper bound.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing potential change point locations and negative log
    #'     likelihood for each segment based on the change points guess.
    segd_poisson <- function(data, beta, B = 10, trim = 0.03, epsilon = 0.001, G = 10^10, L = -20, H = 20) {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)

      # choose the initial values based on pre-segmentation

      index <- rep(1:B, rep(n / B, B))
      coef.int <- matrix(NA, B, p)
      for (i in 1:B)
      {
        out <- fastglm::fastglm(x = as.matrix(data[index == i, 1:p]), y = data[index == i, p + 1], family = "poisson")
        coef.int[i, ] <- coef(out)
      }
      X1 <- data[1, 1:p]
      cum_coef <- coef <- pmin(pmax(matrix(coef.int[1, ], p, 1), L), H)
      e_eta <- exp(coef %*% X1)
      const <- e_eta
      cmatrix <- array((X1 %o% X1) * as.numeric(const), c(p, p, 1))

      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)
        for (i in 1:(m - 1))
        {
          coef_c <- coef[, i]
          cum_coef_c <- cum_coef[, i]
          cmatrix_c <- cmatrix[, , i]
          out <- cost_poisson_update(data[t, ], coef_c, cum_coef_c, cmatrix_c, epsilon = epsilon, G = G, L = L, H = H)
          coef[, i] <- out[[1]]
          cum_coef[, i] <- out[[2]]
          cmatrix[, , i] <- out[[3]]
          k <- set[i] + 1
          cum_coef_win <- pmin(pmax(cum_coef[, i] / (t - k + 1), L), H)
          if (t - k >= p - 1) cval[i] <- neg_log_lik_poisson(data[k:t, ], cum_coef_win) else cval[i] <- 0
        }

        # the choice of initial values requires further investigation

        cval[m] <- 0
        Xt <- data[t, 1:p]
        cum_coef_add <- coef_add <- pmin(pmax(coef.int[index[t], ], L), H)
        e_eta_t <- exp(coef_add %*% Xt)
        const <- e_eta_t
        cmatrix_add <- (Xt %o% Xt) * as.numeric(const)
        coef <- cbind(coef, coef_add)
        cum_coef <- cbind(cum_coef, cum_coef_add)
        cmatrix <- abind::abind(cmatrix, cmatrix_add, along = 3)

        # Adding a momentum term (TBD)

        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        coef <- coef[, ind2, drop = FALSE]
        cum_coef <- cum_coef[, ind2, drop = FALSE]
        cmatrix <- cmatrix[, , ind2, drop = FALSE]
        Fobj <- c(Fobj, min_val)
      }

      # Remove change-points close to the boundaries

      cp <- cp_set[[n + 1]]
      if (length(cp) > 0) {
        ind3 <- (1:length(cp))[(cp < trim * n) | (cp > (1 - trim) * n)]
        if (length(ind3) > 0) cp <- cp[-ind3]
      }

      cp <- sort(unique(c(0, cp)))
      index <- which((diff(cp) < trim * n) == TRUE)
      if (length(index) > 0) cp <- floor((cp[-(index + 1)] + cp[-index]) / 2)
      cp <- cp[cp > 0]

      # nLL <- 0
      # cp_loc <- unique(c(0,cp,n))
      # for(i in 1:(length(cp_loc)-1))
      # {
      #   seg <- (cp_loc[i]+1):cp_loc[i+1]
      #   data_seg <- data[seg,]
      #   out <- fastglm(as.matrix(data_seg[, 1:p]), data_seg[, p+1], family="Poisson")
      #   nLL <- out$deviance/2 + nLL
      # }

      # output <- list(cp, nLL)
      # names(output) <- c("cp", "nLL")

      output <- list(cp)
      names(output) <- c("cp")

      return(output)
    }

    # Generate data from poisson regression models with change-points
    #' @param n Number of observations.
    #' @param d Dimension of the covariates.
    #' @param true.coef True regression coefficients.
    #' @param true.cp.loc True change-point locations.
    #' @param Sigma Covariance matrix of the covariates.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing the generated data and the true cluster
    #'    assignments.
    data_gen_poisson <- function(n, d, true.coef, true.cp.loc, Sigma) {
      loc <- unique(c(0, true.cp.loc, n))
      if (dim(true.coef)[2] != length(loc) - 1) stop("true.coef and true.cp.loc do not match")
      x <- mvtnorm::rmvnorm(n, mean = rep(0, d), sigma = Sigma)
      y <- NULL
      for (i in 1:(length(loc) - 1))
      {
        mu <- exp(x[(loc[i] + 1):loc[i + 1], , drop = FALSE] %*% true.coef[, i, drop = FALSE])
        group <- rpois(length(mu), mu)
        y <- c(y, group)
      }
      data <- cbind(x, y)
      true_cluster <- rep(1:(length(loc) - 1), diff(loc))
      result <- list(data, true_cluster)
      return(result)
    }

## Penalized linear regression

    #' Cost function for penalized linear regression.
    #'
    #' @param data Data to be used to calculate the cost values. The last column is
    #'     the response variable.
    #' @param lambda Penalty coefficient.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Cost value for the corresponding segment of data.
    cost_lasso <- function(data, lambda, family = "gaussian") {
      data <- as.matrix(data)
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      out <- glmnet::glmnet(as.matrix(data[, 1:p]), data[, p + 1], family = family, lambda = lambda)
      return(deviance(out) / 2)
    }

    #' Implementation of vanilla PELT for penalized linear regression type data.
    #'
    #' @param data Data to be used for change point detection.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param B Initial guess on the number of change points.
    #' @param cost Cost function to be used to calculate cost values.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list consisting of two: change point locations and negative log
    #'     likelihood values for each segment.
    pelt_vanilla_lasso <- function(data, beta, B = 10, cost = cost_lasso, family = "gaussian") {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      index <- rep(1:B, rep(n / B, B))
      err_sd <- act_num <- rep(NA, B)
      for (i in 1:B)
      {
        cvfit <- glmnet::cv.glmnet(as.matrix(data[index == i, 1:p]), data[index == i, p + 1], family = family)
        coef <- coef(cvfit, s = "lambda.1se")[-1]
        resi <- data[index == i, p + 1] - as.matrix(data[index == i, 1:p]) %*% as.numeric(coef)
        err_sd[i] <- sqrt(mean(resi^2))
        act_num[i] <- sum(abs(coef) > 0)
      }
      err_sd_mean <- mean(err_sd) # only works if error sd is unchanged.
      act_num_mean <- mean(act_num)
      beta <- (act_num_mean + 1) * beta # seems to work but there might be better choices

      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)
      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)
        for (i in 1:m)
        {
          k <- set[i] + 1
          if (t - k >= 1) cval[i] <- suppressWarnings(cost(data[k:t, ], lambda = err_sd_mean * sqrt(2 * log(p) / (t - k + 1)))) else cval[i] <- 0
        }
        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        Fobj <- c(Fobj, min_val)
        if (t %% 100 == 0) print(t)
      }
      cp <- cp_set[[n + 1]]
      # nLL <- 0
      # cp_loc <- unique(c(0,cp,n))
      # for(i in 1:(length(cp_loc)-1))
      # {
      #  seg <- (cp_loc[i]+1):cp_loc[i+1]
      #  data_seg <- data[seg,]
      #  out <- glmnet(as.matrix(data_seg[, 1:p]), data_seg[, p+1], lambda=lambda, family=family)
      #  nLL <- deviance(out)/2 + nLL
      # }
      # output <- list(cp, nLL)
      output <- list(cp)
      names(output) <- c("cp")
      return(output)
    }

    #' Function to update the coefficients using gradient descent.
    #' @param a Coefficient to be updated.
    #' @param lambda Penalty coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Updated coefficient.
    soft_threshold <- function(a, lambda) {
      sign(a) * pmax(abs(a) - lambda, 0)
    }

    #' Function to update the coefficients using gradient descent.
    #'
    #' @param data_new New data point used to update the coeffient.
    #' @param coef Previous coeffient to be updated.
    #' @param cum_coef Summation of all the past coefficients to be used in
    #'     averaging.
    #' @param cmatrix Hessian matrix in gradient descent.
    #' @param lambda Penalty coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list of values containing the new coefficients, summation of
    #'     coefficients so far and all the Hessian matrices.
    cost_lasso_update <- function(data_new, coef, cum_coef, cmatrix, lambda) {
      p <- length(data_new) - 1
      X_new <- data_new[1:p]
      Y_new <- data_new[p + 1]
      mu <- X_new %*% coef
      cmatrix <- cmatrix + X_new %o% X_new
      # B <- as.vector(cmatrix_inv%*%X_new)
      # cmatrix_inv <- cmatrix_inv - B%o%B/(1+sum(X_new*B))
      lik_dev <- as.numeric(-(Y_new - mu)) * X_new
      coef <- coef - solve(cmatrix, lik_dev)
      nc <- norm(cmatrix, type = "F") # the choice of norm affects the speed. Spectral norm is more accurate but slower than F norm.
      coef <- soft_threshold(coef, lambda / nc)
      cum_coef <- cum_coef + coef
      return(list(coef, cum_coef, cmatrix))
    }

    #' Calculate negative log likelihood given data segment and guess of
    #' coefficient.
    #'
    #' @param data Data to be used to calculate the negative log likelihood.
    #' @param b Guess of the coefficient.
    #' @param lambda Penalty coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Negative log likelihood.
    neg_log_lik_lasso <- function(data, b, lambda) {
      p <- dim(data)[2] - 1
      X <- data[, 1:p, drop = FALSE]
      Y <- data[, p + 1, drop = FALSE]
      resi <- Y - X %*% b
      L <- sum(resi^2) / 2 + lambda * sum(abs(b))
      return(L)
    }

    #' Find change points using dynamic programming with pruning and SeGD.
    #'
    #' @param data Data used to find change points.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param B Initial guess on the number of change points.
    #' @param trim Propotion of the data to ignore the change points at the
    #'     beginning, ending and between change points.
    #' @param epsilon Small adjustment to avoid singularity when doing inverse on
    #'    the Hessian matrix.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing potential change point locations and negative log
    #'     likelihood for each segment based on the change points guess.
    segd_lasso <- function(data, beta, B = 10, trim = 0.025, epsilon = 1e-5, family = "gaussian") {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)

      # choose the initial values based on pre-segmentation

      index <- rep(1:B, rep(n / B, B))
      coef.int <- matrix(NA, B, p)
      err_sd <- act_num <- rep(NA, B)
      for (i in 1:B)
      {
        cvfit <- glmnet::cv.glmnet(as.matrix(data[index == i, 1:p]), data[index == i, p + 1], family = family)
        coef.int[i, ] <- coef(cvfit, s = "lambda.1se")[-1]
        resi <- data[index == i, p + 1] - as.matrix(data[index == i, 1:p]) %*% as.numeric(coef.int[i, ])
        err_sd[i] <- sqrt(mean(resi^2))
        act_num[i] <- sum(abs(coef.int[i, ]) > 0)
      }
      err_sd_mean <- mean(err_sd) # only works if error sd is unchanged.
      act_num_mean <- mean(act_num)
      beta <- (act_num_mean + 1) * beta # seems to work but there might be better choices

      X1 <- data[1, 1:p]
      cum_coef <- coef <- matrix(coef.int[1, ], p, 1)
      eta <- coef %*% X1
      # c_int <- diag(1/epsilon,p) - X1%o%X1/epsilon^2/(1+sum(X1^2)/epsilon)
      # cmatrix_inv <- array(c_int, c(p,p,1))
      cmatrix <- array(X1 %o% X1 + epsilon * diag(1, p), c(p, p, 1))

      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)

        for (i in 1:(m - 1))
        {
          coef_c <- coef[, i]
          cum_coef_c <- cum_coef[, i]
          # cmatrix_inv_c <- cmatrix_inv[,,i]
          cmatrix_c <- cmatrix[, , i]
          k <- set[i] + 1
          out <- cost_lasso_update(data[t, ], coef_c, cum_coef_c, cmatrix_c, lambda = err_sd_mean * sqrt(2 * log(p) / (t - k + 1)))
          coef[, i] <- out[[1]]
          cum_coef[, i] <- out[[2]]
          # cmatrix_inv[,,i] <- out[[3]]
          cmatrix[, , i] <- out[[3]]
          if (t - k >= 2) cval[i] <- neg_log_lik_lasso(data[k:t, ], cum_coef[, i] / (t - k + 1), lambda = err_sd_mean * sqrt(2 * log(p) / (t - k + 1))) else cval[i] <- 0
        }

        # the choice of initial values requires further investigation

        cval[m] <- 0
        Xt <- data[t, 1:p]
        cum_coef_add <- coef_add <- coef.int[index[t], ]
        # cmatrix_inv_add <- diag(1/epsilon,p) - Xt%o%Xt/epsilon^2/(1+sum(Xt^2)/epsilon)

        coef <- cbind(coef, coef_add)
        cum_coef <- cbind(cum_coef, cum_coef_add)
        # cmatrix_inv <- abind::abind(cmatrix_inv, cmatrix_inv_add, along=3)
        cmatrix <- abind::abind(cmatrix, Xt %o% Xt + epsilon * diag(1, p), along = 3)

        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        coef <- coef[, ind2, drop = FALSE]
        cum_coef <- cum_coef[, ind2, drop = FALSE]
        cmatrix <- cmatrix[, , ind2, drop = FALSE]
        # cmatrix_inv <- cmatrix_inv[,,ind2,drop=FALSE]
        Fobj <- c(Fobj, min_val)
      }

      # Remove change-points close to the boundaries and merge change-points

      cp <- cp_set[[n + 1]]
      if (length(cp) > 0) {
        ind3 <- (1:length(cp))[(cp < trim * n) | (cp > (1 - trim) * n)]
        if (length(ind3) > 0) cp <- cp[-ind3]
      }

      cp <- sort(unique(c(0, cp)))
      index <- which((diff(cp) < trim * n) == TRUE)
      if (length(index) > 0) cp <- floor((cp[-(index + 1)] + cp[-index]) / 2)
      cp <- cp[cp > 0]

      # nLL <- 0
      # cp_loc <- unique(c(0,cp,n))
      # for(i in 1:(length(cp_loc)-1))
      # {
      #  seg <- (cp_loc[i]+1):cp_loc[i+1]
      #  data_seg <- data[seg,]
      #  out <- fastglm(as.matrix(data_seg[, 1:p]), data_seg[, p+1], family="binomial")
      #  nLL <- out$deviance/2 + nLL
      # }

      output <- list(cp)
      names(output) <- c("cp")
      return(output)
    }

    # Generate data from penalized linear regression models with change-points
    #' @param n Number of observations.
    #' @param d Dimension of the covariates.
    #' @param true.coef True regression coefficients.
    #' @param true.cp.loc True change-point locations.
    #' @param Sigma Covariance matrix of the covariates.
    #' @param evar Error variance.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing the generated data and the true cluster
    #'    assignments.
    data_gen_lasso <- function(n, d, true.coef, true.cp.loc, Sigma, evar) {
      loc <- unique(c(0, true.cp.loc, n))
      if (dim(true.coef)[2] != length(loc) - 1) stop("true.coef and true.cp.loc do not match")
      x <- mvtnorm::rmvnorm(n, mean = rep(0, d), sigma = Sigma)
      y <- NULL
      for (i in 1:(length(loc) - 1))
      {
        Xb <- x[(loc[i] + 1):loc[i + 1], , drop = FALSE] %*% true.coef[, i, drop = FALSE]
        add <- Xb + rnorm(length(Xb), sd = sqrt(evar))
        y <- c(y, add)
      }
      data <- cbind(x, y)
      true_cluster <- rep(1:(length(loc) - 1), diff(loc))
      result <- list(data, true_cluster)
      return(result)
    }

# Logistic regression

    set.seed(1)
    p <- 5
    x <- matrix(rnorm(300 * p, 0, 1), ncol = p)

    # Randomly generate coefficients with different means.
    theta <- rbind(rnorm(p, 0, 1), rnorm(p, 2, 1))

    # Randomly generate response variables based on the segmented data and
    # corresponding coefficients
    y <- c(
      rbinom(125, 1, 1 / (1 + exp(-x[1:125, ] %*% theta[1, ]))),
      rbinom(300 - 125, 1, 1 / (1 + exp(-x[(125 + 1):300, ] %*% theta[2, ])))
    )

    segd_binomial(cbind(x, y), (p + 1) * log(300) / 2, B = 5)$cp
    #> [1] 125

    fastcpd.binomial(
      cbind(y, x),
      segment_count = 5,
      beta = "BIC",
      cost_adjustment = "BIC",
      r.progress = FALSE
    )@cp_set
    #> [1] 125

    pelt_vanilla_binomial(cbind(x, y), (p + 1) * log(300) / 2)$cp
    #> [1]   0 125

    fastcpd.binomial(
      cbind(y, x),
      segment_count = 5,
      vanilla_percentage = 1,
      beta = "BIC",
      cost_adjustment = "BIC",
      r.progress = FALSE
    )@cp_set
    #> [1] 125

# Poisson regression

    set.seed(1)
    n <- 1500
    d <- 5
    rho <- 0.9
    Sigma <- array(0, c(d, d))
    for (i in 1:d) {
      Sigma[i, ] <- rho^(abs(i - (1:d)))
    }
    delta <- c(5, 7, 9, 11, 13)
    a.sq <- 1
    delta.new <-
      delta * sqrt(a.sq) / sqrt(as.numeric(t(delta) %*% Sigma %*% delta))
    true.cp.loc <- c(375, 750, 1125)

    # regression coefficients
    true.coef <- matrix(0, nrow = d, ncol = length(true.cp.loc) + 1)
    true.coef[, 1] <- c(1, 1.2, -1, 0.5, -2)
    true.coef[, 2] <- true.coef[, 1] + delta.new
    true.coef[, 3] <- true.coef[, 1]
    true.coef[, 4] <- true.coef[, 3] - delta.new

    out <- data_gen_poisson(n, d, true.coef, true.cp.loc, Sigma)
    data <- out[[1]]
    g_tr <- out[[2]]
    beta <- log(n) * (d + 1) / 2

    segd_poisson(
      data, beta, trim = 0.03, B = 10, epsilon = 0.001, G = 10^10, L = -20, H = 20
    )$cp
    #> [1]  380  751 1136 1251

    fastcpd.poisson(
      cbind(data[, d + 1], data[, 1:d]),
      beta = beta,
      cost_adjustment = "BIC",
      epsilon = 0.001,
      segment_count = 10,
      r.progress = FALSE
    )@cp_set
    #> [1]  380  751 1136 1251

    pelt_vanilla_poisson(data, beta)$cp
    #> [1]    0  374  752 1133

    fastcpd.poisson(
      cbind(data[, d + 1], data[, 1:d]),
      segment_count = 10,
      vanilla_percentage = 1,
      beta = beta,
      cost_adjustment = "BIC",
      r.progress = FALSE
    )@cp_set
    #> [1]  374  752 1133

# Penalized linear regression

    set.seed(1)
    n <- 1000
    s <- 3
    d <- 50
    evar <- 0.5
    Sigma <- diag(1, d)
    true.cp.loc <- c(100, 300, 500, 800, 900)
    seg <- length(true.cp.loc) + 1
    true.coef <- matrix(rnorm(seg * s), s, seg)
    true.coef <- rbind(true.coef, matrix(0, d - s, seg))
    out <- data_gen_lasso(n, d, true.coef, true.cp.loc, Sigma, evar)
    data <- out[[1]]
    beta <- log(n) / 2 # beta here has different meaning

    segd_lasso(data, beta, B = 10, trim = 0.025)$cp
    #> [1] 100 300 520 800 901

    fastcpd.lasso(
      cbind(data[, d + 1], data[, 1:d]),
      epsilon = 1e-5,
      beta = beta,
      cost_adjustment = "BIC",
      pruning_coef = 0,
      r.progress = FALSE
    )@cp_set
    #> [1] 100 300 520 800 901

    pelt_vanilla_lasso(data, beta, cost = cost_lasso)$cp
    #> [1] 100
    #> [1] 200
    #> [1] 300
    #> [1] 400
    #> [1] 500
    #> [1] 600
    #> [1] 700
    #> [1] 800
    #> [1] 900
    #> [1] 1000
    #> [1]   0 103 299 510 800 900

    fastcpd.lasso(
      cbind(data[, d + 1], data[, 1:d]),
      vanilla_percentage = 1,
      epsilon = 1e-5,
      beta = beta,
      cost_adjustment = "BIC",
      pruning_coef = 0,
      r.progress = FALSE
    )@cp_set
    #> [1] 103 299 510 800 900

# Notes

The evaluation of this vignette is set to be `FALSE`.

# Appendix: all code snippets

    knitr::opts_chunk$set(
      collapse = TRUE, comment = "#>", eval = FALSE, cache = FALSE,
      warning = FALSE, fig.width = 8, fig.height = 5
    )
    library(fastcpd)
    #' Cost function for Logistic regression, i.e. binomial family in GLM.
    #'
    #' @param data Data to be used to calculate the cost values. The last column is
    #'     the response variable.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Cost value for the corresponding segment of data.
    cost_glm_binomial <- function(data, family = "binomial") {
      data <- as.matrix(data)
      p <- dim(data)[2] - 1
      out <- fastglm::fastglm(
        as.matrix(data[, 1:p]), data[, p + 1],
        family = family
      )
      return(out$deviance / 2)
    }

    #' Implementation of vanilla PELT for logistic regression type data.
    #'
    #' @param data Data to be used for change point detection.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param cost Cost function to be used to calculate cost values.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list consisting of two: change point locations and negative log
    #'     likelihood values for each segment.
    pelt_vanilla_binomial <- function(data, beta, cost = cost_glm_binomial) {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)
      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)
        for (i in 1:m)
        {
          k <- set[i] + 1
          cval[i] <- 0
          if (t - k >= p - 1) cval[i] <- suppressWarnings(cost(data[k:t, ]))
        }
        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        Fobj <- c(Fobj, min_val)
      }
      cp <- cp_set[[n + 1]]
      nLL <- 0
      cp_loc <- unique(c(0, cp, n))
      for (i in 1:(length(cp_loc) - 1))
      {
        seg <- (cp_loc[i] + 1):cp_loc[i + 1]
        data_seg <- data[seg, ]
        out <- fastglm::fastglm(
          as.matrix(data_seg[, 1:p]), data_seg[, p + 1],
          family = "binomial"
        )
        nLL <- out$deviance / 2 + nLL
      }

      output <- list(cp, nLL)
      names(output) <- c("cp", "nLL")
      return(output)
    }

    #' Function to update the coefficients using gradient descent.
    #'
    #' @param data_new New data point used to update the coeffient.
    #' @param coef Previous coeffient to be updated.
    #' @param cum_coef Summation of all the past coefficients to be used in
    #'     averaging.
    #' @param cmatrix Hessian matrix in gradient descent.
    #' @param epsilon Small adjustment to avoid singularity when doing inverse on
    #'     the Hessian matrix.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list of values containing the new coefficients, summation of
    #'     coefficients so far and all the Hessian matrices.
    cost_logistic_update <- function(
        data_new, coef, cum_coef, cmatrix, epsilon = 1e-10) {
      p <- length(data_new) - 1
      X_new <- data_new[1:p]
      Y_new <- data_new[p + 1]
      eta <- X_new %*% coef
      mu <- 1 / (1 + exp(-eta))
      cmatrix <- cmatrix + (X_new %o% X_new) * as.numeric((1 - mu) * mu)
      lik_dev <- as.numeric(-(Y_new - mu)) * X_new
      coef <- coef - solve(cmatrix + epsilon * diag(1, p), lik_dev)
      cum_coef <- cum_coef + coef
      return(list(coef, cum_coef, cmatrix))
    }

    #' Calculate negative log likelihood given data segment and guess of
    #' coefficient.
    #'
    #' @param data Data to be used to calculate the negative log likelihood.
    #' @param b Guess of the coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Negative log likelihood.
    neg_log_lik_binomial <- function(data, b) {
      p <- dim(data)[2] - 1
      X <- data[, 1:p, drop = FALSE]
      Y <- data[, p + 1, drop = FALSE]
      u <- as.numeric(X %*% b)
      L <- -Y * u + log(1 + exp(u))
      return(sum(L))
    }

    #' Find change points using dynamic programming with pruning and SeGD.
    #'
    #' @param data Data used to find change points.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param B Initial guess on the number of change points.
    #' @param trim Propotion of the data to ignore the change points at the
    #'     beginning, ending and between change points.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing potential change point locations and negative log
    #'     likelihood for each segment based on the change points guess.
    segd_binomial <- function(data, beta, B = 10, trim = 0.025) {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)

      # choose the initial values based on pre-segmentation

      index <- rep(1:B, rep(n / B, B))
      coef.int <- matrix(NA, B, p)
      for (i in 1:B)
      {
        out <- fastglm::fastglm(
          as.matrix(data[index == i, 1:p]),
          data[index == i, p + 1],
          family = "binomial"
        )
        coef.int[i, ] <- coef(out)
      }
      X1 <- data[1, 1:p]
      cum_coef <- coef <- matrix(coef.int[1, ], p, 1)
      e_eta <- exp(coef %*% X1)
      const <- e_eta / (1 + e_eta)^2
      cmatrix <- array((X1 %o% X1) * as.numeric(const), c(p, p, 1))

      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)

        for (i in 1:(m - 1))
        {
          coef_c <- coef[, i]
          cum_coef_c <- cum_coef[, i]
          cmatrix_c <- cmatrix[, , i]
          out <- cost_logistic_update(data[t, ], coef_c, cum_coef_c, cmatrix_c)
          coef[, i] <- out[[1]]
          cum_coef[, i] <- out[[2]]
          cmatrix[, , i] <- out[[3]]
          k <- set[i] + 1
          cval[i] <- 0
          if (t - k >= p - 1) {
            cval[i] <-
              neg_log_lik_binomial(data[k:t, ], cum_coef[, i] / (t - k + 1))
          }
        }

        # the choice of initial values requires further investigation

        cval[m] <- 0
        Xt <- data[t, 1:p]
        cum_coef_add <- coef_add <- coef.int[index[t], ]
        e_eta_t <- exp(coef_add %*% Xt)
        const <- e_eta_t / (1 + e_eta_t)^2
        cmatrix_add <- (Xt %o% Xt) * as.numeric(const)

        coef <- cbind(coef, coef_add)
        cum_coef <- cbind(cum_coef, cum_coef_add)
        cmatrix <- abind::abind(cmatrix, cmatrix_add, along = 3)

        # Adding a momentum term (TBD)

        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        coef <- coef[, ind2, drop = FALSE]
        cum_coef <- cum_coef[, ind2, drop = FALSE]
        cmatrix <- cmatrix[, , ind2, drop = FALSE]
        Fobj <- c(Fobj, min_val)
      }

      # Remove change-points close to the boundaries

      cp <- cp_set[[n + 1]]
      if (length(cp) > 0) {
        ind3 <- (seq_len(length(cp)))[(cp < trim * n) | (cp > (1 - trim) * n)]
        cp <- cp[-ind3]
      }

      nLL <- 0
      cp_loc <- unique(c(0, cp, n))
      for (i in 1:(length(cp_loc) - 1))
      {
        seg <- (cp_loc[i] + 1):cp_loc[i + 1]
        data_seg <- data[seg, ]
        out <- fastglm::fastglm(
          as.matrix(data_seg[, 1:p]), data_seg[, p + 1],
          family = "binomial"
        )
        nLL <- out$deviance / 2 + nLL
      }

      output <- list(cp, nLL)
      names(output) <- c("cp", "nLL")
      return(output)
    }
    #' Cost function for Poisson regression.
    #'
    #' @param data Data to be used to calculate the cost values. The last column is
    #'     the response variable.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Cost value for the corresponding segment of data.
    cost_glm_poisson <- function(data, family = "poisson") {
      data <- as.matrix(data)
      p <- dim(data)[2] - 1
      out <- fastglm::fastglm(as.matrix(data[, 1:p]), data[, p + 1], family = family)
      return(out$deviance / 2)
    }

    #' Implementation of vanilla PELT for poisson regression type data.
    #'
    #' @param data Data to be used for change point detection.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param cost Cost function to be used to calculate cost values.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list consisting of two: change point locations and negative log
    #'     likelihood values for each segment.
    pelt_vanilla_poisson <- function(data, beta, cost = cost_glm_poisson) {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)
      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)
        for (i in 1:m)
        {
          k <- set[i] + 1
          if (t - k >= p - 1) cval[i] <- suppressWarnings(cost(data[k:t, ])) else cval[i] <- 0
        }
        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        Fobj <- c(Fobj, min_val)
        # if (t %% 100 == 0) print(t)
      }
      cp <- cp_set[[n + 1]]
      output <- list(cp)
      names(output) <- c("cp")

      return(output)
    }

    #' Function to update the coefficients using gradient descent.
    #'
    #' @param data_new New data point used to update the coeffient.
    #' @param coef Previous coeffient to be updated.
    #' @param cum_coef Summation of all the past coefficients to be used in
    #'     averaging.
    #' @param cmatrix Hessian matrix in gradient descent.
    #' @param epsilon Small adjustment to avoid singularity when doing inverse on
    #'     the Hessian matrix.
    #' @param G Upper bound for the coefficient.
    #' @param L Winsorization lower bound.
    #' @param H Winsorization upper bound.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list of values containing the new coefficients, summation of
    #'     coefficients so far and all the Hessian matrices.
    cost_poisson_update <- function(data_new, coef, cum_coef, cmatrix, epsilon = 0.001, G = 10^10, L = -20, H = 20) {
      p <- length(data_new) - 1
      X_new <- data_new[1:p]
      Y_new <- data_new[p + 1]
      eta <- X_new %*% coef
      mu <- exp(eta)
      cmatrix <- cmatrix + (X_new %o% X_new) * min(as.numeric(mu), G)
      lik_dev <- as.numeric(-(Y_new - mu)) * X_new
      coef <- coef - solve(cmatrix + epsilon * diag(1, p), lik_dev)
      coef <- pmin(pmax(coef, L), H)
      cum_coef <- cum_coef + coef
      return(list(coef, cum_coef, cmatrix))
    }

    #' Calculate negative log likelihood given data segment and guess of
    #' coefficient.
    #'
    #' @param data Data to be used to calculate the negative log likelihood.
    #' @param b Guess of the coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Negative log likelihood.
    neg_log_lik_poisson <- function(data, b) {
      p <- dim(data)[2] - 1
      X <- data[, 1:p, drop = FALSE]
      Y <- data[, p + 1, drop = FALSE]
      u <- as.numeric(X %*% b)
      L <- -Y * u + exp(u) + lfactorial(Y)
      return(sum(L))
    }

    #' Find change points using dynamic programming with pruning and SeGD.
    #'
    #' @param data Data used to find change points.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param B Initial guess on the number of change points.
    #' @param trim Propotion of the data to ignore the change points at the
    #'     beginning, ending and between change points.
    #' @param epsilon Small adjustment to avoid singularity when doing inverse on
    #'    the Hessian matrix.
    #' @param G Upper bound for the coefficient.
    #' @param L Winsorization lower bound.
    #' @param H Winsorization upper bound.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing potential change point locations and negative log
    #'     likelihood for each segment based on the change points guess.
    segd_poisson <- function(data, beta, B = 10, trim = 0.03, epsilon = 0.001, G = 10^10, L = -20, H = 20) {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)

      # choose the initial values based on pre-segmentation

      index <- rep(1:B, rep(n / B, B))
      coef.int <- matrix(NA, B, p)
      for (i in 1:B)
      {
        out <- fastglm::fastglm(x = as.matrix(data[index == i, 1:p]), y = data[index == i, p + 1], family = "poisson")
        coef.int[i, ] <- coef(out)
      }
      X1 <- data[1, 1:p]
      cum_coef <- coef <- pmin(pmax(matrix(coef.int[1, ], p, 1), L), H)
      e_eta <- exp(coef %*% X1)
      const <- e_eta
      cmatrix <- array((X1 %o% X1) * as.numeric(const), c(p, p, 1))

      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)
        for (i in 1:(m - 1))
        {
          coef_c <- coef[, i]
          cum_coef_c <- cum_coef[, i]
          cmatrix_c <- cmatrix[, , i]
          out <- cost_poisson_update(data[t, ], coef_c, cum_coef_c, cmatrix_c, epsilon = epsilon, G = G, L = L, H = H)
          coef[, i] <- out[[1]]
          cum_coef[, i] <- out[[2]]
          cmatrix[, , i] <- out[[3]]
          k <- set[i] + 1
          cum_coef_win <- pmin(pmax(cum_coef[, i] / (t - k + 1), L), H)
          if (t - k >= p - 1) cval[i] <- neg_log_lik_poisson(data[k:t, ], cum_coef_win) else cval[i] <- 0
        }

        # the choice of initial values requires further investigation

        cval[m] <- 0
        Xt <- data[t, 1:p]
        cum_coef_add <- coef_add <- pmin(pmax(coef.int[index[t], ], L), H)
        e_eta_t <- exp(coef_add %*% Xt)
        const <- e_eta_t
        cmatrix_add <- (Xt %o% Xt) * as.numeric(const)
        coef <- cbind(coef, coef_add)
        cum_coef <- cbind(cum_coef, cum_coef_add)
        cmatrix <- abind::abind(cmatrix, cmatrix_add, along = 3)

        # Adding a momentum term (TBD)

        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        coef <- coef[, ind2, drop = FALSE]
        cum_coef <- cum_coef[, ind2, drop = FALSE]
        cmatrix <- cmatrix[, , ind2, drop = FALSE]
        Fobj <- c(Fobj, min_val)
      }

      # Remove change-points close to the boundaries

      cp <- cp_set[[n + 1]]
      if (length(cp) > 0) {
        ind3 <- (1:length(cp))[(cp < trim * n) | (cp > (1 - trim) * n)]
        if (length(ind3) > 0) cp <- cp[-ind3]
      }

      cp <- sort(unique(c(0, cp)))
      index <- which((diff(cp) < trim * n) == TRUE)
      if (length(index) > 0) cp <- floor((cp[-(index + 1)] + cp[-index]) / 2)
      cp <- cp[cp > 0]

      # nLL <- 0
      # cp_loc <- unique(c(0,cp,n))
      # for(i in 1:(length(cp_loc)-1))
      # {
      #   seg <- (cp_loc[i]+1):cp_loc[i+1]
      #   data_seg <- data[seg,]
      #   out <- fastglm(as.matrix(data_seg[, 1:p]), data_seg[, p+1], family="Poisson")
      #   nLL <- out$deviance/2 + nLL
      # }

      # output <- list(cp, nLL)
      # names(output) <- c("cp", "nLL")

      output <- list(cp)
      names(output) <- c("cp")

      return(output)
    }

    # Generate data from poisson regression models with change-points
    #' @param n Number of observations.
    #' @param d Dimension of the covariates.
    #' @param true.coef True regression coefficients.
    #' @param true.cp.loc True change-point locations.
    #' @param Sigma Covariance matrix of the covariates.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing the generated data and the true cluster
    #'    assignments.
    data_gen_poisson <- function(n, d, true.coef, true.cp.loc, Sigma) {
      loc <- unique(c(0, true.cp.loc, n))
      if (dim(true.coef)[2] != length(loc) - 1) stop("true.coef and true.cp.loc do not match")
      x <- mvtnorm::rmvnorm(n, mean = rep(0, d), sigma = Sigma)
      y <- NULL
      for (i in 1:(length(loc) - 1))
      {
        mu <- exp(x[(loc[i] + 1):loc[i + 1], , drop = FALSE] %*% true.coef[, i, drop = FALSE])
        group <- rpois(length(mu), mu)
        y <- c(y, group)
      }
      data <- cbind(x, y)
      true_cluster <- rep(1:(length(loc) - 1), diff(loc))
      result <- list(data, true_cluster)
      return(result)
    }
    #' Cost function for penalized linear regression.
    #'
    #' @param data Data to be used to calculate the cost values. The last column is
    #'     the response variable.
    #' @param lambda Penalty coefficient.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Cost value for the corresponding segment of data.
    cost_lasso <- function(data, lambda, family = "gaussian") {
      data <- as.matrix(data)
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      out <- glmnet::glmnet(as.matrix(data[, 1:p]), data[, p + 1], family = family, lambda = lambda)
      return(deviance(out) / 2)
    }

    #' Implementation of vanilla PELT for penalized linear regression type data.
    #'
    #' @param data Data to be used for change point detection.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param B Initial guess on the number of change points.
    #' @param cost Cost function to be used to calculate cost values.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list consisting of two: change point locations and negative log
    #'     likelihood values for each segment.
    pelt_vanilla_lasso <- function(data, beta, B = 10, cost = cost_lasso, family = "gaussian") {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      index <- rep(1:B, rep(n / B, B))
      err_sd <- act_num <- rep(NA, B)
      for (i in 1:B)
      {
        cvfit <- glmnet::cv.glmnet(as.matrix(data[index == i, 1:p]), data[index == i, p + 1], family = family)
        coef <- coef(cvfit, s = "lambda.1se")[-1]
        resi <- data[index == i, p + 1] - as.matrix(data[index == i, 1:p]) %*% as.numeric(coef)
        err_sd[i] <- sqrt(mean(resi^2))
        act_num[i] <- sum(abs(coef) > 0)
      }
      err_sd_mean <- mean(err_sd) # only works if error sd is unchanged.
      act_num_mean <- mean(act_num)
      beta <- (act_num_mean + 1) * beta # seems to work but there might be better choices

      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)
      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)
        for (i in 1:m)
        {
          k <- set[i] + 1
          if (t - k >= 1) cval[i] <- suppressWarnings(cost(data[k:t, ], lambda = err_sd_mean * sqrt(2 * log(p) / (t - k + 1)))) else cval[i] <- 0
        }
        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        Fobj <- c(Fobj, min_val)
        if (t %% 100 == 0) print(t)
      }
      cp <- cp_set[[n + 1]]
      # nLL <- 0
      # cp_loc <- unique(c(0,cp,n))
      # for(i in 1:(length(cp_loc)-1))
      # {
      #  seg <- (cp_loc[i]+1):cp_loc[i+1]
      #  data_seg <- data[seg,]
      #  out <- glmnet(as.matrix(data_seg[, 1:p]), data_seg[, p+1], lambda=lambda, family=family)
      #  nLL <- deviance(out)/2 + nLL
      # }
      # output <- list(cp, nLL)
      output <- list(cp)
      names(output) <- c("cp")
      return(output)
    }

    #' Function to update the coefficients using gradient descent.
    #' @param a Coefficient to be updated.
    #' @param lambda Penalty coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Updated coefficient.
    soft_threshold <- function(a, lambda) {
      sign(a) * pmax(abs(a) - lambda, 0)
    }

    #' Function to update the coefficients using gradient descent.
    #'
    #' @param data_new New data point used to update the coeffient.
    #' @param coef Previous coeffient to be updated.
    #' @param cum_coef Summation of all the past coefficients to be used in
    #'     averaging.
    #' @param cmatrix Hessian matrix in gradient descent.
    #' @param lambda Penalty coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list of values containing the new coefficients, summation of
    #'     coefficients so far and all the Hessian matrices.
    cost_lasso_update <- function(data_new, coef, cum_coef, cmatrix, lambda) {
      p <- length(data_new) - 1
      X_new <- data_new[1:p]
      Y_new <- data_new[p + 1]
      mu <- X_new %*% coef
      cmatrix <- cmatrix + X_new %o% X_new
      # B <- as.vector(cmatrix_inv%*%X_new)
      # cmatrix_inv <- cmatrix_inv - B%o%B/(1+sum(X_new*B))
      lik_dev <- as.numeric(-(Y_new - mu)) * X_new
      coef <- coef - solve(cmatrix, lik_dev)
      nc <- norm(cmatrix, type = "F") # the choice of norm affects the speed. Spectral norm is more accurate but slower than F norm.
      coef <- soft_threshold(coef, lambda / nc)
      cum_coef <- cum_coef + coef
      return(list(coef, cum_coef, cmatrix))
    }

    #' Calculate negative log likelihood given data segment and guess of
    #' coefficient.
    #'
    #' @param data Data to be used to calculate the negative log likelihood.
    #' @param b Guess of the coefficient.
    #' @param lambda Penalty coefficient.
    #' @keywords internal
    #'
    #' @noRd
    #' @return Negative log likelihood.
    neg_log_lik_lasso <- function(data, b, lambda) {
      p <- dim(data)[2] - 1
      X <- data[, 1:p, drop = FALSE]
      Y <- data[, p + 1, drop = FALSE]
      resi <- Y - X %*% b
      L <- sum(resi^2) / 2 + lambda * sum(abs(b))
      return(L)
    }

    #' Find change points using dynamic programming with pruning and SeGD.
    #'
    #' @param data Data used to find change points.
    #' @param beta Penalty coefficient for the number of change points.
    #' @param B Initial guess on the number of change points.
    #' @param trim Propotion of the data to ignore the change points at the
    #'     beginning, ending and between change points.
    #' @param epsilon Small adjustment to avoid singularity when doing inverse on
    #'    the Hessian matrix.
    #' @param family Family of the distribution.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing potential change point locations and negative log
    #'     likelihood for each segment based on the change points guess.
    segd_lasso <- function(data, beta, B = 10, trim = 0.025, epsilon = 1e-5, family = "gaussian") {
      n <- dim(data)[1]
      p <- dim(data)[2] - 1
      Fobj <- c(-beta, 0)
      cp_set <- list(NULL, 0)
      set <- c(0, 1)

      # choose the initial values based on pre-segmentation

      index <- rep(1:B, rep(n / B, B))
      coef.int <- matrix(NA, B, p)
      err_sd <- act_num <- rep(NA, B)
      for (i in 1:B)
      {
        cvfit <- glmnet::cv.glmnet(as.matrix(data[index == i, 1:p]), data[index == i, p + 1], family = family)
        coef.int[i, ] <- coef(cvfit, s = "lambda.1se")[-1]
        resi <- data[index == i, p + 1] - as.matrix(data[index == i, 1:p]) %*% as.numeric(coef.int[i, ])
        err_sd[i] <- sqrt(mean(resi^2))
        act_num[i] <- sum(abs(coef.int[i, ]) > 0)
      }
      err_sd_mean <- mean(err_sd) # only works if error sd is unchanged.
      act_num_mean <- mean(act_num)
      beta <- (act_num_mean + 1) * beta # seems to work but there might be better choices

      X1 <- data[1, 1:p]
      cum_coef <- coef <- matrix(coef.int[1, ], p, 1)
      eta <- coef %*% X1
      # c_int <- diag(1/epsilon,p) - X1%o%X1/epsilon^2/(1+sum(X1^2)/epsilon)
      # cmatrix_inv <- array(c_int, c(p,p,1))
      cmatrix <- array(X1 %o% X1 + epsilon * diag(1, p), c(p, p, 1))

      for (t in 2:n)
      {
        m <- length(set)
        cval <- rep(NA, m)

        for (i in 1:(m - 1))
        {
          coef_c <- coef[, i]
          cum_coef_c <- cum_coef[, i]
          # cmatrix_inv_c <- cmatrix_inv[,,i]
          cmatrix_c <- cmatrix[, , i]
          k <- set[i] + 1
          out <- cost_lasso_update(data[t, ], coef_c, cum_coef_c, cmatrix_c, lambda = err_sd_mean * sqrt(2 * log(p) / (t - k + 1)))
          coef[, i] <- out[[1]]
          cum_coef[, i] <- out[[2]]
          # cmatrix_inv[,,i] <- out[[3]]
          cmatrix[, , i] <- out[[3]]
          if (t - k >= 2) cval[i] <- neg_log_lik_lasso(data[k:t, ], cum_coef[, i] / (t - k + 1), lambda = err_sd_mean * sqrt(2 * log(p) / (t - k + 1))) else cval[i] <- 0
        }

        # the choice of initial values requires further investigation

        cval[m] <- 0
        Xt <- data[t, 1:p]
        cum_coef_add <- coef_add <- coef.int[index[t], ]
        # cmatrix_inv_add <- diag(1/epsilon,p) - Xt%o%Xt/epsilon^2/(1+sum(Xt^2)/epsilon)

        coef <- cbind(coef, coef_add)
        cum_coef <- cbind(cum_coef, cum_coef_add)
        # cmatrix_inv <- abind::abind(cmatrix_inv, cmatrix_inv_add, along=3)
        cmatrix <- abind::abind(cmatrix, Xt %o% Xt + epsilon * diag(1, p), along = 3)

        obj <- cval + Fobj[set + 1] + beta
        min_val <- min(obj)
        ind <- which(obj == min_val)[1]
        cp_set_add <- c(cp_set[[set[ind] + 1]], set[ind])
        cp_set <- append(cp_set, list(cp_set_add))
        ind2 <- (cval + Fobj[set + 1]) <= min_val
        set <- c(set[ind2], t)
        coef <- coef[, ind2, drop = FALSE]
        cum_coef <- cum_coef[, ind2, drop = FALSE]
        cmatrix <- cmatrix[, , ind2, drop = FALSE]
        # cmatrix_inv <- cmatrix_inv[,,ind2,drop=FALSE]
        Fobj <- c(Fobj, min_val)
      }

      # Remove change-points close to the boundaries and merge change-points

      cp <- cp_set[[n + 1]]
      if (length(cp) > 0) {
        ind3 <- (1:length(cp))[(cp < trim * n) | (cp > (1 - trim) * n)]
        if (length(ind3) > 0) cp <- cp[-ind3]
      }

      cp <- sort(unique(c(0, cp)))
      index <- which((diff(cp) < trim * n) == TRUE)
      if (length(index) > 0) cp <- floor((cp[-(index + 1)] + cp[-index]) / 2)
      cp <- cp[cp > 0]

      # nLL <- 0
      # cp_loc <- unique(c(0,cp,n))
      # for(i in 1:(length(cp_loc)-1))
      # {
      #  seg <- (cp_loc[i]+1):cp_loc[i+1]
      #  data_seg <- data[seg,]
      #  out <- fastglm(as.matrix(data_seg[, 1:p]), data_seg[, p+1], family="binomial")
      #  nLL <- out$deviance/2 + nLL
      # }

      output <- list(cp)
      names(output) <- c("cp")
      return(output)
    }

    # Generate data from penalized linear regression models with change-points
    #' @param n Number of observations.
    #' @param d Dimension of the covariates.
    #' @param true.coef True regression coefficients.
    #' @param true.cp.loc True change-point locations.
    #' @param Sigma Covariance matrix of the covariates.
    #' @param evar Error variance.
    #' @keywords internal
    #'
    #' @noRd
    #' @return A list containing the generated data and the true cluster
    #'    assignments.
    data_gen_lasso <- function(n, d, true.coef, true.cp.loc, Sigma, evar) {
      loc <- unique(c(0, true.cp.loc, n))
      if (dim(true.coef)[2] != length(loc) - 1) stop("true.coef and true.cp.loc do not match")
      x <- mvtnorm::rmvnorm(n, mean = rep(0, d), sigma = Sigma)
      y <- NULL
      for (i in 1:(length(loc) - 1))
      {
        Xb <- x[(loc[i] + 1):loc[i + 1], , drop = FALSE] %*% true.coef[, i, drop = FALSE]
        add <- Xb + rnorm(length(Xb), sd = sqrt(evar))
        y <- c(y, add)
      }
      data <- cbind(x, y)
      true_cluster <- rep(1:(length(loc) - 1), diff(loc))
      result <- list(data, true_cluster)
      return(result)
    }
    set.seed(1)
    p <- 5
    x <- matrix(rnorm(300 * p, 0, 1), ncol = p)

    # Randomly generate coefficients with different means.
    theta <- rbind(rnorm(p, 0, 1), rnorm(p, 2, 1))

    # Randomly generate response variables based on the segmented data and
    # corresponding coefficients
    y <- c(
      rbinom(125, 1, 1 / (1 + exp(-x[1:125, ] %*% theta[1, ]))),
      rbinom(300 - 125, 1, 1 / (1 + exp(-x[(125 + 1):300, ] %*% theta[2, ])))
    )

    segd_binomial(cbind(x, y), (p + 1) * log(300) / 2, B = 5)$cp
    #> [1] 125

    fastcpd.binomial(
      cbind(y, x),
      segment_count = 5,
      beta = "BIC",
      cost_adjustment = "BIC",
      r.progress = FALSE
    )@cp_set
    #> [1] 125

    pelt_vanilla_binomial(cbind(x, y), (p + 1) * log(300) / 2)$cp
    #> [1]   0 125

    fastcpd.binomial(
      cbind(y, x),
      segment_count = 5,
      vanilla_percentage = 1,
      beta = "BIC",
      cost_adjustment = "BIC",
      r.progress = FALSE
    )@cp_set
    #> [1] 125
    set.seed(1)
    n <- 1500
    d <- 5
    rho <- 0.9
    Sigma <- array(0, c(d, d))
    for (i in 1:d) {
      Sigma[i, ] <- rho^(abs(i - (1:d)))
    }
    delta <- c(5, 7, 9, 11, 13)
    a.sq <- 1
    delta.new <-
      delta * sqrt(a.sq) / sqrt(as.numeric(t(delta) %*% Sigma %*% delta))
    true.cp.loc <- c(375, 750, 1125)

    # regression coefficients
    true.coef <- matrix(0, nrow = d, ncol = length(true.cp.loc) + 1)
    true.coef[, 1] <- c(1, 1.2, -1, 0.5, -2)
    true.coef[, 2] <- true.coef[, 1] + delta.new
    true.coef[, 3] <- true.coef[, 1]
    true.coef[, 4] <- true.coef[, 3] - delta.new

    out <- data_gen_poisson(n, d, true.coef, true.cp.loc, Sigma)
    data <- out[[1]]
    g_tr <- out[[2]]
    beta <- log(n) * (d + 1) / 2

    segd_poisson(
      data, beta, trim = 0.03, B = 10, epsilon = 0.001, G = 10^10, L = -20, H = 20
    )$cp
    #> [1]  380  751 1136 1251

    fastcpd.poisson(
      cbind(data[, d + 1], data[, 1:d]),
      beta = beta,
      cost_adjustment = "BIC",
      epsilon = 0.001,
      segment_count = 10,
      r.progress = FALSE
    )@cp_set
    #> [1]  380  751 1136 1251

    pelt_vanilla_poisson(data, beta)$cp
    #> [1]    0  374  752 1133

    fastcpd.poisson(
      cbind(data[, d + 1], data[, 1:d]),
      segment_count = 10,
      vanilla_percentage = 1,
      beta = beta,
      cost_adjustment = "BIC",
      r.progress = FALSE
    )@cp_set
    #> [1]  374  752 1133
    set.seed(1)
    n <- 1000
    s <- 3
    d <- 50
    evar <- 0.5
    Sigma <- diag(1, d)
    true.cp.loc <- c(100, 300, 500, 800, 900)
    seg <- length(true.cp.loc) + 1
    true.coef <- matrix(rnorm(seg * s), s, seg)
    true.coef <- rbind(true.coef, matrix(0, d - s, seg))
    out <- data_gen_lasso(n, d, true.coef, true.cp.loc, Sigma, evar)
    data <- out[[1]]
    beta <- log(n) / 2 # beta here has different meaning

    segd_lasso(data, beta, B = 10, trim = 0.025)$cp
    #> [1] 100 300 520 800 901

    fastcpd.lasso(
      cbind(data[, d + 1], data[, 1:d]),
      epsilon = 1e-5,
      beta = beta,
      cost_adjustment = "BIC",
      pruning_coef = 0,
      r.progress = FALSE
    )@cp_set
    #> [1] 100 300 520 800 901

    pelt_vanilla_lasso(data, beta, cost = cost_lasso)$cp
    #> [1] 100
    #> [1] 200
    #> [1] 300
    #> [1] 400
    #> [1] 500
    #> [1] 600
    #> [1] 700
    #> [1] 800
    #> [1] 900
    #> [1] 1000
    #> [1]   0 103 299 510 800 900

    fastcpd.lasso(
      cbind(data[, d + 1], data[, 1:d]),
      vanilla_percentage = 1,
      epsilon = 1e-5,
      beta = beta,
      cost_adjustment = "BIC",
      pruning_coef = 0,
      r.progress = FALSE
    )@cp_set
    #> [1] 103 299 510 800 900