Minor cleaning up.

README.md

@@ -80,92 +80,6 @@ directories, respectively.
          Performs clean and completely removes the build directory.
 
 
-<!---
-## Getting started
-
-Here are some examples to quickly try out SLIM on the sample datasets that are provided with SLIM.
-
-### Python interface
-
-```python
-import pandas as pd
-from SLIM import SLIM, SLIMatrix
-
-#read training data stored as triplets <user> <item> <rating>
-traindata = pd.read_csv('../test/AutomotiveTrain.ijv', delimiter = ' ', header=None)
-trainmat = SLIMatrix(traindata)
-
-#set up parameters to learn model, e.g., use Coordinate Descent with L1 and L2
-#regularization
-params = {'algo':'cd', 'nthreads':2, 'l1r':1.0, 'l2r':1.0}
-
-#learn the model using training data and desired parameters
-model = SLIM()
-model.train(params, trainmat)
-
-#read test data having candidate items for users
-testdata = pd.read_csv('../test/AutomotiveTest.ijv', delimiter = ' ', header=None)
-#NOTE: model object is passed as an argument while generating test matrix
-testmat = SLIMatrix(testdata, model)
-
-#generate top-10 recommendations
-prediction_res = model.predict(testmat, nrcmds=10, outfile = 'output.txt')
-
-#dump the model to files on disk
-model.save_model(modelfname='model.csr', # filename to save the model as a csr matrix
-                 mapfname='map.csr' # filename to save the item map
-                )
-
-#load the model from from disk
-model_new = SLIM()
-model_new.load_model(modelfname='model.csr', # filename of the model
-                 mapfname='map.csr' # filename of the item map
-                )
-```
-
-The users can also refer to the python notebook [UserGuide.ipynb](./python-package/UserGuide.ipynb) located at
-`./python-package/UserGuide.ipynb` for more examples on using the python api.
-
-###  Command-line programs
-SLIM can be used by running the command-line programs that are located under `./build` directory. Specifically, SLIM provides the following three command-line programs:
-- `slim_learn`: for estimating a model
-- `slim_predict`: for applying a previously estimated model, and
-- `slim_mselect`: for exploring a set of hyper-parameters in order to select the best performing model.
-
-Additional information about how to use these command-line programs is located in
-SLIM's reference manual that is available at
-[./doxygen/html/index.html](http://glaros.dtc.umn.edu/gkhome/files/fs/sw/slim/doc/html/index.html)
-or
-[./doxygen/latex/refman.pdf](http://glaros.dtc.umn.edu/gkhome/files/fs/sw/slim/doc/refman.pdf).
-
-###  Library interface
-
-You can also use SLIM by direclty linking into your C/C++ program via its library interface. SLIM's API is described 
-in SLIM's reference manual (see links above).
-
-## Citing
-If you use any part of this library in your research, please cite it using the
-following BibTex entry:
-
-```
-@online{slim,
-  title = {{SLIM Library for Recommender Systems}},
-  author = {Ning, Xia and Nikolakopoulos, Athanasios N. and Shui, Zeren and Sharma, Mohit and Karypis, George},
-  url = {https://github.com/KarypisLab/SLIM},
-  year = {2019},
-}
-```
-
-## References
-1. [Slim: Sparse linear methods for top-n recommender systems](http://glaros.dtc.umn.edu/gkhome/node/774)
-## Credits & Contact Information
-
-This implementation of SLIM was written by George Karypis with contributions by Xia Ning, Athanasios N. Nikolakopoulos, Zeren Shui and Mohit Sharma.
-
-If you encounter any problems or have any suggestions, please contact George Karypis at <a href="mailto:[email protected]">[email protected]</a>.
-
--->
-
 ## Copyright & License Notice
 Copyright 1998-2020, Regents of the University of Minnesota
 

include/metis.h

@@ -342,7 +342,7 @@ typedef enum {
   METIS_DBG_SEPINFO    = 64, 	  /*!< Show info on vertex moves during sep refinement */
   METIS_DBG_CONNINFO   = 128,     /*!< Show info on minimization of subdomain connectivity */
   METIS_DBG_CONTIGINFO = 256,     /*!< Show info on elimination of connected components */ 
-  METIS_DBG_MEMORY     = 2048,    /*!< Show info related to wspace allocation */
+  METIS_DBG_MEMORY     = 2048     /*!< Show info related to wspace allocation */
 } mdbglvl_et;