# Path: lxmls/classifiers/multinomial_naive_bayes.py
# class MultinomialNaiveBayes(lc.LinearClassifier):
#     def __init__(self, xtype="gaussian"):
#     def train(self, x, y):


import sys
import os
import pytest
import warnings
import lxmls.classifiers.gaussian_naive_bayes as gnbc
import lxmls.classifiers.max_ent_batch as mebc
import lxmls.classifiers.max_ent_online as meoc
import lxmls.classifiers.mira as mirac
import lxmls.classifiers.perceptron as percc
import lxmls.classifiers.svm as svmc
import lxmls.readers.sentiment_reader as srs
import lxmls.readers.simple_data_set as sds
from numpy import allclose
from lxmls.classifiers import multinomial_naive_bayes as mnbb


tolerance = 1e-5


@pytest.fixture(scope='module')
def scr():
    return srs.SentimentCorpus("books")


# Exercise 1.1
def test_naive_bayes(scr):

# Path: lxmls/classifiers/multinomial_naive_bayes.py
# class MultinomialNaiveBayes(lc.LinearClassifier):
#     def __init__(self, xtype="gaussian"):
#     def train(self, x, y):


# Path: tests/test_linear_classifier.py
import sys
import os
import pytest
import warnings
import lxmls.classifiers.gaussian_naive_bayes as gnbc
import lxmls.classifiers.max_ent_batch as mebc
import lxmls.classifiers.max_ent_online as meoc
import lxmls.classifiers.mira as mirac
import lxmls.classifiers.perceptron as percc
import lxmls.classifiers.svm as svmc
import lxmls.readers.sentiment_reader as srs
import lxmls.readers.simple_data_set as sds
from numpy import allclose
from lxmls.classifiers import multinomial_naive_bayes as mnbb



tolerance = 1e-5


@pytest.fixture(scope='module')
def scr():
    return srs.SentimentCorpus("books")


# Exercise 1.1
def test_naive_bayes(scr):

    mnb = mnbb.MultinomialNaiveBayes()