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.kern@gmail.com> wrote:

On Mon, May 20, 2013 at 5:00 PM, Neal Becker <ndbecker2@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