Try training with `--random_initialization`; this should fix the issue.
Without that option, the transition probabilities are initialized to all equal
values and this can mess up the optimization. When mlpack 2.1.1 is released
the option will be removed because I will make random initialization the
default---uniform initialization has caused too many problems to be reliable.
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