def CFG2CNF(modelPath):
K, V, Productions = helper.loadModel(modelPath)
Productions = START(Productions, V)
Productions = removeTERM(Productions, V)
Productions = nonunitaryremoval(Productions, V)
Productions = DELmakeNewRules(Productions)
Productions = UNITremoval(Productions, V)
return K, Productions
def UNIT(productions, variables):
i = 0
result = unit_routine(productions, variables)
tmp = unit_routine(result, variables)
while result != tmp and i < 1000:
result = unit_routine(tmp, variables)
tmp = unit_routine(result, variables)
i += 1
return result
if __name__ == '__main__':
if len(sys.argv) > 1:
modelPath = str(sys.argv[1])
else:
modelPath = 'model.txt'
K, V, Productions = helper.loadModel(modelPath)
Productions = START(Productions, variables=V)
Productions = TERM(Productions, variables=V)
Productions = BIN(Productions, variables=V)
Productions = DEL(Productions)
Productions = UNIT(Productions, variables=V)
print(helper.prettyForm(Productions))
print(len(Productions))
open('out.txt', 'w').write(helper.prettyForm(Productions))
"""Define parameters of the run"""
DUMP_WEIGHTS = True #will we dump the weights of conv layers for visualization
batch_size = 4 #how many training examples per batch
"""Define the folder where the model will be stored based on the input arguments"""
folder = helper.defineFolder(True,outType,size,run)
print folder
trainNP = folder+"tempTrain/"
print trainNP
model = helper.loadModel(folder+"wholeModel")
""" TRAINING """
superEpochs = 10000
RMSE = 1000000
oldRMSE = 1000000
for sup in range(0,superEpochs):
oldRMSE = min(oldRMSE,RMSE)
print "*"*80
print "TRUE EPOCH ", sup
print "*"*80
count = 0
added = 0