def testFortran(self):
"""Test if `normalize_logspace` works with a F-contiguous array"""
np.random.seed(0)
mat = random((NCOL, NROW)).T
self.assertTrue(mat.flags["F_CONTIGUOUS"])
mat_out = normalize_logspace(mat)
row_sum = mat_out.sum(1)
approx_equal = arrays_almost_equal(row_sum, np.ones(NROW), accuracy=ACC)
self.assertTrue(approx_equal)
def testMean(self):
result = loadmat(join(TEST_DATA_LOC, 'faithful_final_mean.mat'),
squeeze_me=True)
approx_equal = arrays_almost_equal(self.model.ESS.smm_mean,
result['smm_mean'],
accuracy=1e-1)
self.assertTrue(approx_equal)
def runTest(self):
# Test for the correct number of components
self.assertEqual(self.model.ESS.num_comp, 3)
# Test the means
sorted_mean = np.sort(self.model.ESS.smm_mean, 0)
approx_equal = arrays_almost_equal(sorted_mean,
self.mean,
accuracy=1e-1)
self.assertTrue(approx_equal)
def runTest(self):
test_data = loadmat(join(test_data_loc,
mat_filename), squeeze_me=True)
args = (test_data[arg] for arg in argument_keys)
if load_data:
data = loadmat(join(test_data_loc,
'faithful.mat'), squeeze_me=True)
args = (data['data'],) + tuple(args)
test_result = test_function(*args)
approx_equal = arrays_almost_equal(test_data[result_key],
test_result,
accuracy=max_diff)
self.assertTrue(approx_equal)
