old_contents: #!/usr/bin/env python
  
  
  # Cluster_Ensembles/src/Cluster_Ensembles/__init__.py;
  
  # Author: Gregory Giecold for the GC Yuan Lab
  # Affiliation: Harvard University
  # Contact: g.giecold@gmail.com, ggiecold@jimmy.harvard.edu
  
  
  """Cluster_Ensembles is a package for combining multiple partitions 
  into a consolidated clustering.
  The combinatorial optimization problem of obtaining such a consensus clustering
  is reformulated in terms of approximation algorithms for 
  graph or hyper-graph partitioning.
  
  References
  ----------
  * Giecold, G., Marco, E., Trippa, L. and Yuan, G.-C.,
  "Robust Inference of Cell Lineages", to submitted for publication
  
  * Strehl, A. and Ghosh, J., "Cluster Ensembles - A Knowledge Reuse Framework
  for Combining Multiple Partitions".
  In: Journal of Machine Learning Research, 3, pp. 583-617. 2002
  
  * Kernighan, B. W. and Lin, S., "An Efficient Heuristic Procedure 
  for Partitioning Graphs". 
  In: The Bell System Technical Journal, 49, 2, pp. 291-307. 1970
  
  * Karypis, G. and Kumar, V., "A Fast and High Quality Multilevel Scheme 
  for Partitioning Irregular Graphs"
  In: SIAM Journal on Scientific Computing, 20, 1, pp. 359-392. 1998
  
  * Karypis, G., Aggarwal, R., Kumar, V. and Shekhar, S., "Multilevel Hypergraph Partitioning: 
  Applications in the VLSI Domain".
  In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 7, 1, pp. 69-79. 1999
  """
  
  
  from . Cluster_Ensembles import *
  
  
  __all__ = []
  __all__ += Cluster_Ensembles.__all__
  
  
new_contents: #!/usr/bin/env python
  
  
  # Cluster_Ensembles/src/Cluster_Ensembles/__init__.py;
  
  # Author: Gregory Giecold for the GC Yuan Lab
  # Affiliation: Harvard University
  # Contact: g.giecold@gmail.com, ggiecold@jimmy.harvard.edu
  
  
  """Cluster_Ensembles is a package for combining multiple partitions 
  into a consolidated clustering.
  The combinatorial optimization problem of obtaining such a consensus clustering
  is reformulated in terms of approximation algorithms for 
  graph or hyper-graph partitioning.
  
  References
  ----------
  * Giecold, G., Marco, E., Trippa, L. and Yuan, G.-C.,
  "Robust Lineage Reconstruction from High-Dimensional Single-Cell Data". 
  ArXiv preprint [q-bio.QM, stat.AP, stat.CO, stat.ML]: http://arxiv.org/abs/1601.02748
  
  * Strehl, A. and Ghosh, J., "Cluster Ensembles - A Knowledge Reuse Framework
  for Combining Multiple Partitions".
  In: Journal of Machine Learning Research, 3, pp. 583-617. 2002
  
  * Kernighan, B. W. and Lin, S., "An Efficient Heuristic Procedure 
  for Partitioning Graphs". 
  In: The Bell System Technical Journal, 49, 2, pp. 291-307. 1970
  
  * Karypis, G. and Kumar, V., "A Fast and High Quality Multilevel Scheme 
  for Partitioning Irregular Graphs"
  In: SIAM Journal on Scientific Computing, 20, 1, pp. 359-392. 1998
  
  * Karypis, G., Aggarwal, R., Kumar, V. and Shekhar, S., "Multilevel Hypergraph Partitioning: 
  Applications in the VLSI Domain".
  In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 7, 1, pp. 69-79. 1999
  """
  
  
  from . Cluster_Ensembles import *
  
  
  __all__ = []
  __all__ += Cluster_Ensembles.__all__
  
  
subject: Update __init__.py