def ShowConf():
cxBaseC.ShowConf()
print 'word2vecin\nfeaturegroup givenfeature|termpairemb\nqrel\nemblm'
LeToRGivenFeatureExtractorC.ShowConf()
EmbeddingTermPairFeatureExtractorC.ShowConf()
EmbeddingLmFeatureExtractorC.ShowConf()
IndriSearchCenterC.ShowConf()
def ShowConf():
cxBaseC.ShowConf()
QueryPreFetchedNodeCollectorC.ShowConf()
DocNodeFaccAnaCollectorC.ShowConf()
IndriSearchCenterC.ShowConf()
print 'querynodegroup ana'
print 'docnodegroup facc'
def ShowConf(cls):
cxBaseC.ShowConf()
IndriSearchCenterC.ShowConf()
FbObjCacheCenterC.ShowConf()
AdhocEvaC.ShowConf()
print 'qdocnodedatadir\norigqweight 0.5\nqobjonly 1'
def ShowConf(cls):
cxBaseC.ShowConf()
print cls.__name__
print 'nodedir'
IndriSearchCenterC.ShowConf()
FbObjCacheCenterC.ShowConf()
LeToRFeatureExtractCenterC.ShowConf()
FbQObjFeatureExtractCenterC.ShowConf()
FbObjDocFeatureExtractCenterC.ShowConf()
ObjObjFeatureExtractCenterC.ShowConf()
def ShowConf():
cxBaseC.ShowConf()
IndriSearchCenterC.ShowConf()
FbObjCacheCenterC.ShowConf()
print 'nodedir\nqobjfeaturegroup\ndocobjfeaturegroup\nobjobjfeaturegroup'
QueryObjEdgeFeatureAnaExtractorC.ShowConf()
DocObjEdgeFeatureFaccExtractorC.ShowConf()
ObjObjEdgeFeatureKGExtractorC.ShowConf()
ObjObjEdgeFeaturePreCalcSimExtractorC.ShowConf()
ObjObjEdgeFeatureTextSimExtractorC.ShowConf()
def ShowConf():
cxBaseC.ShowConf()
print "sg defines the training algorithm. By default (sg=1), skip gram is used."
print "size is the dimensionality of the feature vectors."
print "window is the maximum distance between the current and predicted word within a sentence."
print "alpha is the initial learning rate (will linearly drop to zero as training progresses)."
print "seed = for the random number generator."
print "min_count = ignore all words with total frequency lower than this."
print "sample = threshold for configuring which higher-frequency words are randomly downsampled;"
print "default is 0 (off), useful value is 1e-5."
print "workers = use this many worker threads to train the model (=faster training with multicore machines)."
# print "hs = if 1 (default), hierarchical sampling will be used for model training (else set to 0)."
print "negative = if > 0, negative sampling will be used, the int for negative specifies."
# print "dm_mean = if 0 (default), use the sum of the context word vectors. If 1, use the mean. Only applies when dm is used."
print "in\nout"
def ShowConf():
cxBaseC.ShowConf()
print 'lambda\nalpha\nb'
def ShowConf():
cxBaseC.ShowConf()
print 'dockgdir\nqanain'
def ShowConf(cls):
cxBaseC.ShowConf()
print cls.__name__
print 'word2vecin\nkernel\nlmname\nbandwidth\nin\nout'
IndriSearchCenterC.ShowConf()
AdhocEvaC.ShowConf()
def ShowConf():
cxBaseC.ShowConf()
IndriSearchCenterC.ShowConf()
FbObjCacheCenterC.ShowConf()
print 'neighbornum'
def ShowConf():
cxBaseC.ShowConf()
FaccDataCenterC.ShowConf()
def ShowConf():
cxBaseC.ShowConf()
print 'indatapre\npartnum\ndocurlmappingpre\noutpre\ntorunscript'
def ShowConf():
cxBaseC.ShowConf()
print 'dockgdir\nqanain\ninference lm|tfidfcos\ntfidfcosweight 1#0#0'
def ShowConf():
cxBaseC.ShowConf()
print 'dockgdir'
def ShowConf(cls):
cxBaseC.ShowConf()
print cls.__name__
print 'word2vecin'
IndriSearchCenterC.ShowConf()
AdhocEvaC.ShowConf()
def ShowConf():
cxBaseC.ShowConf()
print 'indir\noutdir\nqrelnqin'
def ShowConf():
cxBaseC.ShowConf()
print 'docanain\noutdir\nin\ndoctextin'
IndriSearchCenterC.ShowConf()
def ShowConf():
cxBaseC.ShowConf()
print 'graphsource\noutdir'
DocKGFaccFormerC.ShowConf()
DocKGTagMeFormerC.ShowConf()
def ShowConf(cls):
cxBaseC.ShowConf()
print cls.__name__
print 'indir\noutdir'
AdhocQRelC.ShowConf()
def ShowConf(cls):
cxBaseC.ShowConf()
FbObjCacheCenterC.ShowConf()
print 'origqweight 0.5'
def ShowConf():
cxBaseC.ShowConf()
print "distype abs|raw|l2|cos\nqfield topic|desp\ndocvecin\noverwrite 0"
print 'docvecintype text|gensim\ndocnoinname\nqin'
def ShowConf():
cxBaseC.ShowConf()
print 'word2vecin\nin\noutdir\nbinnumber'
IndriSearchCenterC.ShowConf()