def check_pickle(obj):
fh =StringIO()
cPickle.dump(obj, fh)
plen = fh.pos
fh.seek(0,0)
res = cPickle.load(fh)
fh.close()
return res, plen
6.0 2 2 8
7.0 2 2 9
9.0 2 2 10
10.0 2 3 1
8.0 2 3 2
12.0 2 3 3
3.0 2 3 4
7.0 2 3 5
15.0 2 3 6
4.0 2 3 7
9.0 2 3 8
6.0 2 3 9
1.0 2 3 10
""")
kidney_table.seek(0)
kidney_table = read_csv(kidney_table, sep="\s+", engine='python').astype(int)
class TestAnovaLM(object):
@classmethod
def setup_class(cls):
# kidney data taken from JT's course
# don't know the license
cls.data = kidney_table
cls.kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)',
data=cls.data).fit()
def test_results(self):
Df = np.array([1, 2, 2, 54])
sum_sq = np.array([2.339693, 16.97129, 0.6356584, 28.9892])
6.0 2 2 8
7.0 2 2 9
9.0 2 2 10
10.0 2 3 1
8.0 2 3 2
12.0 2 3 3
3.0 2 3 4
7.0 2 3 5
15.0 2 3 6
4.0 2 3 7
9.0 2 3 8
6.0 2 3 9
1.0 2 3 10
""")
kidney_table.seek(0)
kidney_table = read_csv(kidney_table, sep="\s+", engine='python').astype(int)
class TestAnovaLM(object):
@classmethod
def setup_class(cls):
# kidney data taken from JT's course
# don't know the license
cls.data = kidney_table
cls.kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)',
data=cls.data).fit()
def test_results(self):
Df = np.array([1, 2, 2, 54])
sum_sq = np.array([2.339693, 16.97129, 0.6356584, 28.9892])
mean_sq = np.array([2.339693, 8.485645, 0.3178292, 0.536837])
