Hi,
You cannot use complex numbers, they should be real numbers. Each data
point should be in |R^d (where d is the dimensionality).
2014-09-17 19:45 GMT+02:00 Neal Becker <ndbeck...@gmail.com>:
> 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?
>
> --
> -- Those who don't understand recursion are doomed to repeat it
>
>
>
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--
Mohamed-Rafik BOUGUELIA
PhD Student
INRIA Nancy Grand Est - LORIA - READ Team
Nancy University - France.
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