def test_transform_max_depth1(self):
a = LocalEncodedAngle(max_depth=1)
a.fit(ALL_DATA)
m = a.transform(ALL_DATA)
expected_results = numpy.array([13.078022, 7.028573, 146.255683])
mm = numpy.array([x.sum() for x in m])
try:
numpy.testing.assert_allclose(mm, expected_results)
except AssertionError as e:
self.fail(e)
def test_transform(self):
a = LocalEncodedAngle()
a.fit(ALL_DATA)
m = a.transform([METHANE, MID])
expected_results = numpy.array([42.968775, 53.28433])
mm = numpy.array([x.sum() for x in m])
try:
numpy.testing.assert_allclose(mm, expected_results)
except AssertionError as e:
self.fail(e)
def test_small_to_large(self):
a = LocalEncodedAngle()
a.fit([METHANE])
# This is a cheap test to prevent needing all the values here
expected_results = numpy.array([
0.005350052647, # mean
0.036552192752, # std
0., # min
0.614984152986, # max
9.630094765591, # sum
])
try:
m = a.transform([MID])
val = numpy.array([
m.mean(),
m.std(),
m.min(),
m.max(),
m.sum(),
])
numpy.testing.assert_allclose(val, expected_results)
except AssertionError as e:
self.fail(e)
def test_add_unknown(self):
a = LocalEncodedAngle(add_unknown=True)
a.fit([METHANE])
m = a.transform([MID])
self.assertEqual(m.shape, (1, 9, 300))
def test_transform_before_fit(self):
a = LocalEncodedAngle()
with self.assertRaises(ValueError):
a.transform(ALL_DATA)