I just tried k-nearest neighbors where the data are complex. It doesn't seem
to
work correctly.
I tried
import numpy as np
from const64apsk import gen_constellation_64apsk
const = gen_constellation_64apsk ('3/4')
X = [[e] for e in const]
y = np.arange(64)
from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y) # doctest: +ELLIPSIS
print(neigh.kneighbors([const[0]]))
Don't worry about the module const64apsk, all that matters here are that
const is a 1-d array of 64 complex values.
I'm guessing KNeighborsClassifier doesn't understand complex arithmetic, and
I'd
need to give the points as 2-d real,imag values?
--
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