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()