def test_iris(self):
data = Orange.data.Table("iris")
proc = PCADenoising(components=2)
d1 = proc(data)
newdata = data.transform(d1.domain)
differences = newdata.X - data.X
self.assertTrue(np.all(np.abs(differences) < 0.6))
# pin some values to detect changes in the PCA implementation
# (for example normalization)
np.testing.assert_almost_equal(
newdata.X[:2], [[5.08718247, 3.51315614, 1.40204280, 0.21105556],
[4.75015528, 3.15366444, 1.46254138, 0.23693223]])
def test_no_samples(self):
data = Orange.data.Table("iris")
proc = PCADenoising()
d1 = proc(data[:0])
newdata = data.transform(d1.domain)
np.testing.assert_equal(newdata.X, np.nan)
PREPROCESSORS_INDEPENDENT_SAMPLES += list(
add_edge_case_data_parameter(EMSC, "reference", SMALL_COLLAGEN[0:1]))
# EMSC with different kinds of bad spectra
PREPROCESSORS_INDEPENDENT_SAMPLES += list(
add_edge_case_data_parameter(EMSC,
"badspectra",
SMALL_COLLAGEN[0:2],
reference=SMALL_COLLAGEN[-1:]))
PREPROCESSORS_INDEPENDENT_SAMPLES += \
list(add_edge_case_data_parameter(NormalizeReference, "reference", SMALL_COLLAGEN[:1]))
# Preprocessors that use groups of input samples to infer
# internal parameters.
PREPROCESSORS_GROUPS_OF_SAMPLES = [
PCADenoising(components=2),
]
PREPROCESSORS_INDEPENDENT_SAMPLES += list(
add_edge_case_data_parameter(ME_EMSC,
"reference",
SMALLER_COLLAGEN[0:1],
max_iter=4))
PREPROCESSORS = PREPROCESSORS_INDEPENDENT_SAMPLES + PREPROCESSORS_GROUPS_OF_SAMPLES
class TestTransmittance(unittest.TestCase):
def test_domain_conversion(self):
"""Test whether a domain can be used for conversion."""
data = SMALL_COLLAGEN
