def fBrownCluster(self):
if self.brnclst == None:
self.brnclst = utils.readMetaOptimizeBrownCluster()
key = "brnclst1gram"
if key not in self.featurestest:
self.featurestest[key] = []
for instance in self.test:
rs = features.getBrownClusNgram(instance.rawsent,1,self.brnclst)
rs = ["_".join(x) for x in rs]
self.featurestest[key].append(rs)
s = Space(101)
s.loadFromFile(BRNCLSTSPACEFILE)
return s
def readScales(scalefile):
scales = {}
with open(scalefile) as f:
for line in f:
k, v = line.strip().split("\t")
scales[int(k)] = float(v)
f.close()
return scales
brnclst = utils.readMetaOptimizeBrownCluster()
embeddings = utils.readMetaOptimizeEmbeddings()
brnspace = initBrnSpace()
scales_shallow = readScales(SHALLOWSCALEFILE)
scales_neuralbrn = readScales(NEURALBRNSCALEFILE)
model_shallow = ll.load_model(SHALLOWMODELFILE)
model_neuralbrn = ll.load_model(NEURALBRNMODELFILE)
def simpleScale(x, trainmaxes=None):
maxes = trainmaxes if trainmaxes != None else {}
if trainmaxes == None:
for itemd in x:
for k, v in itemd.items():
if k not in maxes or maxes[k] < abs(v): maxes[k] = abs(v)
newx = []
def initBrnSpace():
s = Space(101)
s.loadFromFile(BRNCLSTSPACEFILE)
return s
def readScales(scalefile):
scales = {}
with open(scalefile) as f:
for line in f:
k,v = line.strip().split("\t")
scales[int(k)] = float(v)
f.close()
return scales
brnclst = utils.readMetaOptimizeBrownCluster()
embeddings = utils.readMetaOptimizeEmbeddings()
brnspace = initBrnSpace()
scales_shallow = readScales(SHALLOWSCALEFILE)
scales_neuralbrn = readScales(NEURALBRNSCALEFILE)
model_shallow = ll.load_model(SHALLOWMODELFILE)
model_neuralbrn = ll.load_model(NEURALBRNMODELFILE)
def simpleScale(x, trainmaxes=None):
maxes = trainmaxes if trainmaxes!=None else {}
if trainmaxes == None:
for itemd in x:
for k,v in itemd.items():
if k not in maxes or maxes[k] < abs(v): maxes[k] = abs(v)
newx = []
for itemd in x: