hi Neal,
you should concat the imaginery and real parts of the features.
X = np.c_[X.real, X.imag]
if you use the euclidian distance it should do the join.
Alex
On Wed, Sep 17, 2014 at 8:05 PM, Mohamed-Rafik Bouguelia
<bouguelia.med.ra...@gmail.com> wrote:
> 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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