Periodic Data with Machine Learning (Like Degree Angles -> 179 is 2 different from -179)

Question

I\'m using Python for kernel density estimations and gaussian mixture models to rank likelihood of samples of multidimensional data. Every piece of data is an angle, and I\'m no

Answer 1

As Tal Darom wrote in the comments, you can replace every periodic feature x with two features cos(x) and sin(x) after normalizing to radians. That solves the 359 ≈ 1 problem:

>>> def fromdeg(d):
...     r = d * np.pi / 180.
...     return np.array([np.cos(r), np.sin(r)])
... 
>>> np.linalg.norm(fromdeg(1) - fromdeg(359))
0.03490481287456796
>>> np.linalg.norm(fromdeg(1) - fromdeg(180))
1.9999238461283426
>>> np.linalg.norm(fromdeg(90) - fromdeg(270))
2.0

norm(a - b) is the good old Euclidean distance between vectors a and b. As you can verify using a simple plot, or by realizing that these (cos,sin) pairs are really coordinates on the unit circle, that this distance is maximal (and the dot product minimal) between two of these (cos,sin) vectors when the original angles differ by 180°.

Answer 2

You need to use the mod function. In straight python this would be (ang2-ang1)%360 but with scipy it looks like you can use numpy.mod() - see the documentation.

Answer 3

Another simpler way could be to use time as angle measurements than degree measurements (not DMS though). Since many analytics software features time as a datatype, you can use its periodicity to do your job.

But remember, you need to scale 360 degrees to 24 hours.

Answer 4

An alternative to the methods already posted would be to model the angular variables using the Von Mises distribution.

This distribution appears to be supported by scipy so shouldn't be too difficult to fit into a mixture model.