import numpy as np
from sklearn import preprocessing
from sklearn.naive_bayes import GaussianNB
from sklearn.feature_extraction.text import TfidfVectorizer
le=preprocessing.LabelEncoder()

with open("train/in.tsv") as f:
    data = f.readlines() 
    vectorizer = TfidfVectorizer(ngram_range=(1,2), use_idf = False)
    vectorizer = TfidfVectorizer()
    x = vectorizer.fit_transform(data)
    X=x.toarray()
    with open("train/expected.tsv") as ff:
        Y = ff.readlines() 
        Y=le.fit_transform(Y)
        with open("dev-0/in.tsv") as d:
            fil = d.readlines() 
            vectorizer = TfidfVectorizer(ngram_range=(1,2), use_idf = False)
            vectorizer = TfidfVectorizer()
            r=vectorizer.fit_transform(fil)
            r=r.toarray()
            r=r.reshape(-1,1)
            gnb = GaussianNB()
            model=gnb.fit(X, Y)
            y_pred=model.predict(X)
            print(y_pred)
            y_pred=np.array(y_pred)
            t=np.array2string(y_pred, precision=2, separator='\n',suppress_small=True)
            f = open("dev-0/out.tsv", "a")
            f.write(t)