def test_scale_continuous(): x = np.arange(11) # apply scaled = scale_continuous.apply(x, rescale) npt.assert_allclose(scaled, x*0.1) npt.assert_allclose(scaled, x*0.1) # Train limits = scale_continuous.train(x) npt.assert_allclose(limits, [0, 10]) # Additional training limits = scale_continuous.train(np.arange(-4, 11), limits) npt.assert_allclose(limits, [-4, 10]) limits = scale_continuous.train([], limits) npt.assert_allclose(limits, [-4, 10]) # branches # scaled = scale_continuous.apply(x, rescale, trans=identity_trans()) mapped = scale_continuous.map(x, list, [0, 10]) npt.assert_allclose(mapped, x*0.1) with pytest.raises(TypeError): # discrete data limits = scale_continuous.train(['a', 'b', 'c'])
def test_scale_continuous(): x = np.arange(11) # apply scaled = scale_continuous.apply(x, rescale) npt.assert_allclose(scaled, x * 0.1) npt.assert_allclose(scaled, x * 0.1) # Train limits = scale_continuous.train(x) npt.assert_allclose(limits, [0, 10]) # Additional training limits = scale_continuous.train(np.arange(-4, 11), limits) npt.assert_allclose(limits, [-4, 10]) limits = scale_continuous.train([], limits) npt.assert_allclose(limits, [-4, 10]) # branches # scaled = scale_continuous.apply(x, rescale, trans=identity_trans()) mapped = scale_continuous.map(x, list, [0, 10]) npt.assert_allclose(mapped, x * 0.1) with pytest.raises(TypeError): # discrete data limits = scale_continuous.train(['a', 'b', 'c'])
def train(self, x): """ Train continuous range """ self.range = scale_continuous.train(x, self.range)