X-Git-Url: https://git.auder.net/assets/current/gitweb.css?a=blobdiff_plain;f=old_C_code%2FREADME;fp=old_C_code%2FREADME;h=0000000000000000000000000000000000000000;hb=62deb4244895a20a35397dfb062f0b9fe94c5012;hp=6a539afe37f422a70ebd0953ecc4675860a52219;hpb=3eef8d3df59ded9a281cff51f79fe824198a7427;p=epclust.git

diff --git a/old_C_code/README b/old_C_code/README
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-0) Download & compile Benjamin's specific library
-
-   git clone git@auder.net:cgds
-   cd cgds
-   bash makeMakefile.sh src
-   make src
-   sudo make install
-
-Make sure that the install destination is on the LD_LIBRARY_PATH environment variable.
-
-1) Compile source code for 1st stage clustering
-
-   mkdir -p build/stage1/src
-   cd build/stage1/src
-   cmake ../../../stage1/src
-   make
-
-2) #repeat previous lines for stage 2 ???
-
-
-NOTA: Need to have openmpi, mpich (compiler for mpi) libxml, and libgsl installed.
-
-
-Usage (stage 1) :
-
-Serialize input data using 
-
-   ppam.exe serialize inputfile_edf outputfile_edf 1 0 
-
-# 1 indicates data is by column
-# 0 means process all the rows
-
-   mpirun -np nbProcess ppam.exe cluster ifilename nbSeriesInChunk nbClusters randomize p_for_dissims
-
-## ex. > mpirun -np 4 ./ppam.exe cluster ~/tmp/2009.bin 5000 200 1 2 
-
-Where :
-   nbProcess = number of simultaneous processes
-   ifilename = path to serialized dataset (read below)
-   nbSeriesInChunk = number of time-series to process sequentially
-   nbClusters = number of clusters
-   randomize = 1 to dispatch time-series at random. 0 to process them in order
-   p_for_dissims = the 'p' of L_p distance used to compute dissimilarities
-
-
-The results are stored in ppamResult.xml (curves ids and ranks) while ppamFinalSeries.bin
-are the curves used in the last clustering step. The ranks in ppamResult.xml refer to the
-curves in ppamFinalSeries.bin
-
-
-Note : custom [de]serialization. Consider writing your own 
-in src/TimeSeries/ folder if you plan to test the package.
-
-See also src/main.c for the details.