This software implements the Spectacle-Tree algorithm for learning hidden Markov models with tree-structured hidden states using spectral learning. For details, please see:

Chicheng Zhang, Jimin Song, Kevin C. Chen and Kamalika Chaudhuri, Spectral learning of large structured HMMs for comparative epigenomics. Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), Montreal, Canada, 2015.

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The MIT License (MIT)

Copyright (C) 2015 Jimin Song, Chicheng Zhang and Kevin Chen

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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THE SOFTWARE.
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Spectacle-Tree is written in Python and requires SciPy and NumPy.

E.g. python Spectacle-Tree.py 6 Param_Spectacle_Tree.py Output imp_tree

python [m] [param_file] [outdir] [outfile]

'm' is the number of chromatin states. 
'param_file' must define Python objects tree, marks, chromosomes and inputdir
'outdir' must be created before running Spectacle-Tree.py 
'outfile' can be an arbitrary string

Comments:
We recommend using a relatively low number of chromatin states, for example 6 or 10. In our experience this is more biologically interpretable than a higher number, such as 25 or 50.