def average_edit_distance(n): import nltk.metrics.distance as d edits = [ e["noteText"] for e in activity_logs_for_note(n) if e["action"] == "note-save"] ##//ActivityLog.objects.filter(noteid=n["id"],action="note-edit").order_by("when").values_list("noteText") distances = [] if len(edits) > 1: #print "edits: %s " % repr(edits) for i in range(0,len(edits)-1): if edits[i] is None or edits[i+1] is None: continue distances.append( d.edit_distance( edits[i], edits[i+1] ) ) if len(distances) > 0: return make_feature("edit_distance",median(distances)) return make_feature('edit_distance',MISSING)
def average_edit_distance(n): import nltk.metrics.distance as d edits = [ e["noteText"] for e in activity_logs_for_note(n) if e["action"] == "note-save" ] ##//ActivityLog.objects.filter(noteid=n["id"],action="note-edit").order_by("when").values_list("noteText") distances = [] if len(edits) > 1: #print "edits: %s " % repr(edits) for i in range(0, len(edits) - 1): if edits[i] is None or edits[i + 1] is None: continue distances.append(d.edit_distance(edits[i], edits[i + 1])) if len(distances) > 0: return make_feature("edit_distance", median(distances)) return make_feature('edit_distance', MISSING)
def s(counts): print "min:%g max:%g mode:%g mean:%g median:%g var:%g" % (min(counts),max(counts),mode(counts),mean(counts),median(counts),var(counts)) return (min(counts),max(counts),mode(counts),mean(counts),median(counts),var(counts))
def s(counts): print "min:%g max:%g mode:%g mean:%g median:%g var:%g" % ( min(counts), max(counts), mode(counts), mean(counts), median(counts), var(counts)) return (min(counts), max(counts), mode(counts), mean(counts), median(counts), var(counts))