main() {
  Execution directory: tag.out
  Reading examples {
    sample-data/hmm.tag (0 examples so far)
  }
  Stats {
    numExamples = 2
    numTags = 2
    numStates = 2
  }
  Init parameters: random {
    AParams.output(tag.out/init.params)
  }
  Train: stage1
  Train: stage2 {
    Iteration 0/100: temperature = 1 {
      E-step {
        Example 0/2: train: logZ = NaN, logVZ = NaN, logCZ = NaN, elogZ = NaN, entropy = NaN, objective = NaN, accuracy = NaN
      }
      Inference complexity: 6/ << 12~6 >> /18 (2)
      train: logZ = -0.693, logVZ = -1.369, logCZ = -0.676, elogZ = NaN, entropy = NaN, objective = NaN, accuracy = 0.583
      ... 1 lines omitted ...
    }
    Iteration 1/100: temperature = 1 {
      E-step {
        Example 0/2: train: logZ = NaN, logVZ = NaN, logCZ = NaN, elogZ = NaN, entropy = NaN, objective = NaN, accuracy = NaN
      }
      Inference complexity: 6/ << 12~6 >> /18 (2)
      train: logZ = -0.679, logVZ = -1.355, logCZ = -0.676, elogZ = NaN, entropy = NaN, objective = NaN, accuracy = 0.583
      ... 1 lines omitted ...
    }
    Iteration 2/100: temperature = 1 {
      E-step {
        Example 0/2: train: logZ = NaN, logVZ = NaN, logCZ = NaN, elogZ = NaN, entropy = NaN, objective = NaN, accuracy = NaN
      }
      Inference complexity: 6/ << 12~6 >> /18 (2)
      train: logZ = -0.679, logVZ = -1.355, logCZ = -0.676, elogZ = NaN, entropy = NaN, objective = NaN, accuracy = 0.583
      ... 1 lines omitted ...
    }
    Iteration 38/100: temperature = 1 {
      E-step {
        Example 0/2: train: logZ = NaN, logVZ = NaN, logCZ = NaN, elogZ = NaN, entropy = NaN, objective = NaN, accuracy = NaN
      }
      Inference complexity: 6/ << 12~6 >> /18 (2)
      train: logZ = -0.679, logVZ = -1.342, logCZ = -0.663, elogZ = NaN, entropy = NaN, objective = NaN, accuracy = 0.917
      ... 1 lines omitted ...
    }
    ... 97 lines omitted ...
  }
  Execution directory: tag.out
} [1.1s]
