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)