You could also try using bincount, (np.bincount(x, y.real) + 1j*np.bincount(x, y.imag)) / np.bincount(x)
Bago On Mon, May 20, 2013 at 9:03 AM, Robert Kern <robert.k...@gmail.com> wrote: > On Mon, May 20, 2013 at 5:00 PM, Neal Becker <ndbeck...@gmail.com> wrote: > > I have a system that transmits signals for an alphabet of M symbols > > over and additive Gaussian noise channel. The receiver has a > > 1-d array of complex received values. I'd like to find the means > > of the received values according to the symbol that was transmitted. > > > > So transmit symbol indexes might be: > > > > x = [0, 1, 2, 1, 3, ...] > > > > and receive output might be: > > > > y = [(1+1j), (1-1j), ...] > > > > Suppose the alphabet was M=4. Then I'd like to get an array of means > > > > m[0...3] that correspond to the values of y for each of the corresponding > > values of x. > > > > I can't think of a better way than manually using loops. Any tricks > here? > > All you need is a single loop over the alphabet, which is usually not > problematic. > > > means = np.empty([M]) > for i in range(M): > means[i] = y[x == i].mean() > > -- > Robert Kern > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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