def main():
lines = []
for i in range(11):
lines.append(i)
if sys.argv[1] == "train":
training_file = sys.argv[2]
hypothesis_file = sys.argv[3]
sentences = []
for line in open(training_file, encoding="utf-8-sig"):
sentences.append(line.strip())
sentence_attributes = AttributeCalculator.sentences_to_boolean_attributes(sentences)
node = Node.Node(sentence_attributes, lines)
if sys.argv[4] == "dt":
dt = DecisionTreeTraining(node).train_dt(node.sentences, node.input, 0)
hypothesis_out = open(hypothesis_file, "wb")
pickle.dump(dt, hypothesis_out)
elif sys.argv[4] == "ada":
ada = AdaBoost.AdaBoostTraining(DecisionTreeTraining, AdaBoostTraining.stump).train_ada(node)
hypothesis_out = open(hypothesis_file, "wb")
pickle.dump(ada, hypothesis_out)
elif sys.argv[1] == "predict":
hypothesis = sys.argv[2]
prediction_file = sys.argv[3]
sentences = []
hyp = open(hypothesis, "rb")
model = pickle.load(hyp)
file = open(prediction_file, encoding="utf-8-sig")
for line in file:
line = line.strip()
sentences.append(line)
sentences = AttributeCalculator.sentences_to_boolean_attributes_predict(sentences)
if isinstance(model, Decision):
for sentence in sentences:
print(model.get_language(sentence))
elif isinstance(model, WeightedDecision):
for sentence in sentences:
result = "".join(model.predict_language(sentence))
print(result)
