def test_no_samples(self):
data = Orange.data.Table("iris")
proc = PCADenoising()
d1 = proc(data[:0])
newdata = Orange.data.Table(d1.domain, data)
np.testing.assert_equal(newdata.X, np.nan)
Integrate(methods=Integrate.Simple, limits=[[1100, 1200]]),
Integrate(methods=Integrate.Baseline, limits=[[1100, 1200]]),
Integrate(methods=Integrate.PeakMax, limits=[[1100, 1200]]),
Integrate(methods=Integrate.PeakBaseline, limits=[[1100, 1200]]),
Integrate(methods=Integrate.PeakAt, limits=[[1100]]),
Integrate(methods=Integrate.PeakX, limits=[[1100, 1200]]),
Integrate(methods=Integrate.PeakXBaseline, limits=[[1100, 1200]]),
RubberbandBaseline(),
Normalize(method=Normalize.Vector),
Normalize(method=Normalize.Area, int_method=Integrate.PeakMax, lower=0, upper=10000),
]
# Preprocessors that use groups of input samples to infer
# internal parameters.
PREPROCESSORS_GROUPS_OF_SAMPLES = [
PCADenoising(components=2),
]
PREPROCESSORS = PREPROCESSORS_INDEPENDENT_SAMPLES + PREPROCESSORS_GROUPS_OF_SAMPLES
def shuffle_attr(data):
natts = list(data.domain.attributes)
random.Random(0).shuffle(natts)
ndomain = Orange.data.Domain(natts, data.domain.class_vars,
metas=data.domain.metas)
return Orange.data.Table(ndomain, data)
def reverse_attr(data):
natts = reversed(data.domain.attributes)