def test_simple(self):
     data = Orange.data.Table("iris")
     f = GaussianSmoothing(sd=1.)
     data = data[:1]
     fdata = f(data)
     np.testing.assert_almost_equal(
         fdata.X, [[4.4907066, 3.2794677, 1.7641664, 0.6909083]])
예제 #2
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 def test_unknown_no_propagate(self):
     data = Orange.data.Table("iris")
     f = GaussianSmoothing()
     data = data[:5]
     for i in range(4):
         data.X[i, i] = np.nan
     data.X[4] = np.nan
     fdata = f(data)
     np.testing.assert_equal(np.sum(np.isnan(fdata.X), axis=1), [1, 1, 1, 1, 4])
    Rerturn appropriate test file for a preprocessor.

    Very slow preprocessors should get smaller files.
    """
    if isinstance(preproc, ME_EMSC):
        return SMALLER_COLLAGEN
    return SMALL_COLLAGEN


# Preprocessors that work per sample and should return the same
# result for a sample independent of the other samples
PREPROCESSORS_INDEPENDENT_SAMPLES = [
    Interpolate(np.linspace(1000, 1700, 100)),
    SavitzkyGolayFiltering(window=9, polyorder=2, deriv=2),
    Cut(lowlim=1000, highlim=1800),
    GaussianSmoothing(sd=3.),
    Absorbance(),
    Transmittance(),
    Integrate(limits=[[900, 100], [1100, 1200], [1200, 1300]]),
    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(),
    LinearBaseline(),
    Normalize(method=Normalize.Vector),
    Normalize(method=Normalize.Area,
              int_method=Integrate.PeakMax,