def kmedoids_clustering(similarity_matrix, k, maxits = 1000000):
"""Takes symmetric similarity matrix (list of lists) and returns list: assignment to clusters."""
logging.info("[kmedoids_clustering] clustering objects="+str(len(similarity_matrix))+" k="+str(k))
if len(similarity_matrix) <= k:
logging.warn("[kmedoids_clustering] objects="+str(len(similarity_matrix))+" is no more than clusters="+str(k)+"!")
return range(0, len(similarity_matrix))
labels = list( i for i in xrange(len(similarity_matrix)) )
matrix_io.fwrite_smatrix(similarity_matrix, labels, labels, TMP_INPATH)
if matlab_wrapper.run_matlab("../clustering/kmedoids_matlab/kmedoids.m", [TMP_INPATH, (k), (maxits), TMP_OUTPATH]) != 0:
raise Exception("[kmedoids_clustering] Matlab failure!")
assignment = matrix_io.fread_ivector(TMP_OUTPATH)
clusters = set(assignment) #przenumerowanie klastrow:
clust2clust = dict( (old_no, new_no) for (new_no,old_no) in enumerate(clusters) )
assignment = list(clust2clust[c] for c in assignment)
return assignment
def kmedoids_clustering(similarity_matrix, k, maxits=1000000):
"""Takes symmetric similarity matrix (list of lists) and returns list: assignment to clusters."""
logging.info("[kmedoids_clustering] clustering objects=" +
str(len(similarity_matrix)) + " k=" + str(k))
if len(similarity_matrix) <= k:
logging.warn("[kmedoids_clustering] objects=" +
str(len(similarity_matrix)) +
" is no more than clusters=" + str(k) + "!")
return range(0, len(similarity_matrix))
labels = list(i for i in xrange(len(similarity_matrix)))
matrix_io.fwrite_smatrix(similarity_matrix, labels, labels, TMP_INPATH)
if matlab_wrapper.run_matlab("../clustering/kmedoids_matlab/kmedoids.m",
[TMP_INPATH, (k),
(maxits), TMP_OUTPATH]) != 0:
raise Exception("[kmedoids_clustering] Matlab failure!")
assignment = matrix_io.fread_ivector(TMP_OUTPATH)
clusters = set(assignment) #przenumerowanie klastrow:
clust2clust = dict(
(old_no, new_no) for (new_no, old_no) in enumerate(clusters))
assignment = list(clust2clust[c] for c in assignment)
return assignment
print "Loading ZBL records from zbl_path=", zbl_path
zblid2zbl = dict((zbl[zbl_io.ZBL_ID_FIELD], zbl)
for zbl in _get_zbl_generator_(zbl_path))
print " zblid2zbl [", len(zblid2zbl), "docs loaded] =", str(
list(zblid2zbl.iteritems()))[:100]
print "--------------------------------------------------------"
print "Building model MSC codes counts..."
mscmodel = msc_processing.MscModel(zblid2zbl.values())
print "--------------------------------------------------------"
print "Filtering msccodes with MIN_COUNT_MSC=", MIN_COUNT_MSC, " MIN_COUNT_MSCPRIM=", MIN_COUNT_MSCPRIM, " MIN_COUNT_MSCSEC=", MIN_COUNT_MSCSEC
mscmodel.keep_msc_mincount(MIN_COUNT_MSC, MIN_COUNT_MSCPRIM,
MIN_COUNT_MSCSEC)
mscmodel.report()
#store_mscgroups_primary(open("msc_groups.txt", "w"), mscmodel.mscprim2zblidlist)
print "--------------------------------------------------------"
print "Calculating msc2ix mapping..."
msc2ix = dict(
(msc, ix) for ix, msc in enumerate(sorted(mscmodel.allcodes())))
ix2msc = dict((ix, msc) for msc, ix in msc2ix.iteritems())
msc_list = list(ix2msc[ix] for ix in xrange(len(ix2msc)))
print " msc2ix=", str(list(msc2ix.iteritems()))[:100]
print "--------------------------------------------------------"
print "Storing random similarity matrix to", output_path
matrix_io.fwrite_smatrix([[random.random() for msc in msc_list]
for msc in msc_list], msc_list, msc_list,
output_path)
print "The program generates random similarity matrix for MSC codes (filtered and extracted from ZBL file)."
print "--------------------------------------------------------"
print "Loading ZBL records from zbl_path=",zbl_path
zblid2zbl = dict( (zbl[zbl_io.ZBL_ID_FIELD],zbl) for zbl in _get_zbl_generator_(zbl_path) )
print " zblid2zbl [",len(zblid2zbl),"docs loaded] =",str(list(zblid2zbl.iteritems()))[:100]
print "--------------------------------------------------------"
print "Building model MSC codes counts..."
mscmodel = msc_processing.MscModel( zblid2zbl.values() )
print "--------------------------------------------------------"
print "Filtering msccodes with MIN_COUNT_MSC=",MIN_COUNT_MSC," MIN_COUNT_MSCPRIM=",MIN_COUNT_MSCPRIM," MIN_COUNT_MSCSEC=",MIN_COUNT_MSCSEC
mscmodel.keep_msc_mincount(MIN_COUNT_MSC, MIN_COUNT_MSCPRIM, MIN_COUNT_MSCSEC)
mscmodel.report()
#store_mscgroups_primary(open("msc_groups.txt", "w"), mscmodel.mscprim2zblidlist)
print "--------------------------------------------------------"
print "Calculating msc2ix mapping..."
msc2ix = dict((msc,ix) for ix,msc in enumerate(sorted(mscmodel.allcodes())))
ix2msc = dict((ix,msc) for msc,ix in msc2ix.iteritems())
msc_list = list( ix2msc[ix] for ix in xrange(len(ix2msc)) )
print " msc2ix=",str(list(msc2ix.iteritems()))[:100]
print "--------------------------------------------------------"
print "Storing random similarity matrix to",output_path
matrix_io.fwrite_smatrix([ [random.random() for msc in msc_list] for msc in msc_list], msc_list, msc_list, output_path)