print ('\n\nPlease uncomment nltk.download() to download text data sets \n')
#nltk.download() # Download text data sets, including stop words
# Initialize an empty list to hold the clean symptoms and summary
clean_train_symptom = []
clean_train_summary ={}
print ("Cleaning and parsing the training set symptoms...\n")
for i in xrange( 0, len(train["symptom"])):
clean_train_symptom.append(" ".join(Word2VecUtility.symptoms_to_wordlist(train["symptom"][i],True)))
print ("Cleaning and parsing the training set summary...\n")
for i in xrange( 0, len(train["summary"])):
clean_train_summary[train["disease"][i]] = "".join(Word2VecUtility.summary_to_wordlist(train["summary"][i]))
# ****** Create a bag of words from the training set
#
print ("Creating the bag of words...\n")
# Initialize the "CountVectorizer" object, which is scikit-learn's
# bag of words tool.
vectorizer = CountVectorizer(analyzer = "word", \
tokenizer = None, \
preprocessor = None, \
stop_words = None, \
max_features = None)
