如何使用depmixS4包比较R中的隐马尔可夫模型中的状态？

Question

我正在处理从this blog开始缩减的最小示例，我想知道如何正确匹配状态。示例代码模拟了两个状态，状态1是波动率低的正收益，状态2是波动率高的负收益。如果多次运行代码，您会发现有时HMM的准确度为100％，而其他时候为0％。我认为这只是分类问题，哪种波动率制度是状态1和哪种状态2。如何使输出保持一致？

  library('depmixS4')
  bull_mean <- 0.2
  bull_var <- 0.05
  bear_mean <- -0.1
  bear_var <- 0.1

  # Create the various bull and bear markets returns
  bull <- rnorm(100, bull_mean, bull_var )
  bear <- rnorm(100, bear_mean, bear_var )

  # Create the list of true regime states and full returns list
  true_regimes <- c(rep(1, 100), rep(2, 100))
  returns <- c(bull, bear)

  # Create and fit the Hidden Markov Model
  hmm <- depmix(returns ~ 1, family = gaussian(), nstates = 2, data=data.frame(returns=returns))
  hmmfit <- fit(hmm, verbose = FALSE)
  post_probs <- posterior(hmmfit)
  output <- data.frame(pred = post_probs$state, true_regimes = true_regimes)
  table(output)
  accuracy = sum(post_probs$state == true_regimes) / nrow(post_probs)
  accuracy