def predict(self, X):
y_pred = []
X = np.array(X)
m, n = self.X.shape
k = m
l = int(math.sqrt(n))
nb = MultinomialNB()
nb.row_idxs = np.random.choice(m, k)
nb.col_idxs = list(set(np.random.choice(n, l)))
self.X = (X - min(X)) / (max(X) - min(X))
nb.fit(self.X, self.y)
for xi in X:
predictions = {}
predicted = nb.predict(xi.reshape(1, -1))[0]
predictions[predicted] = predictions.get(predicted, 0) + 1
sorted_votes = list(predictions.items())
sorted_votes.sort(key=operator.itemgetter(1))
y_pred.append(sorted_votes[-1][0])
return y_pred
