def compute_nb(X, y, Z):
labels = [int(t) for t in y]
ptrain = [X[i] for i in range(len(labels)) if labels[i] == 0]
ntrain = [X[i] for i in range(len(labels)) if labels[i] == 1]
poscounts = nbsvm.build_dict(ptrain, [1,2])
negcounts = nbsvm.build_dict(ntrain, [1,2])
dic, r = nbsvm.compute_ratio(poscounts, negcounts)
trainX = nbsvm.process_text(X, dic, r, [1,2])
devX = nbsvm.process_text(Z, dic, r, [1,2])
return trainX, devX
def compute_nb(X, y, Z):
"""
Compute NB features
"""
labels = [int(t) for t in y]
ptrain = [X[i] for i in range(len(labels)) if labels[i] == 0]
ntrain = [X[i] for i in range(len(labels)) if labels[i] == 1]
poscounts = nbsvm.build_dict(ptrain, [1,2])
negcounts = nbsvm.build_dict(ntrain, [1,2])
dic, r = nbsvm.compute_ratio(poscounts, negcounts)
trainX = nbsvm.process_text(X, dic, r, [1,2])
devX = nbsvm.process_text(Z, dic, r, [1,2])
return trainX, devX