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- #!/usr/bin/python3
- import sys
- import pickle
- from math import log, exp
- from tokenizer import tokenize
- #Load model
- model = pickle.load(open("model.pkl","rb"))
- weights, word_to_index_mapping, word_count = model
- for line in sys.stdin:
- document = line.rstrip()
- fields = document.split('\t')
- document = fields[0]
- terms = tokenize(document)
- y_predicted = weights[0]
- for word in terms:
- y_predicted += weights[word_to_index_mapping.get(word,0)] * (word_count.get(word,0) / len(word_count))
- if y_predicted <= 0.5:
- print(0)
- else:
- print(1)
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