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"""Data preparation / preprocessing algorithms."""
import numpy as np
def scale(x, new_range=(0., 1.), eps=1e-9):
"""Changes the scale of an array
Parameters
----------
x : ndarray
1D array to change the scale (remains unchanged)
new_range : tuple (float, float)
Desired range of the array
eps: float
Numerical precision, to detect degenerate cases (for example, when
every value of *x* is equal)
Returns
-------
ndarray
Scaled array
tuple (float, float)
Previous data range, allowing a rescale to the old range
"""
assert new_range[1] >= new_range[0]
range_ = (x.min(), x.max())
if (range_[1] - range_[0]) < eps:
mean = (new_range[0] + new_range[1]) / 2.0
xp = np.full(x.shape, mean)
else:
xp = (x - range_[0])
xp *= (new_range[1] - new_range[0]) / (range_[1] - range_[0])
xp += new_range[0]
return xp, range_
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