Neal Becker wrote:
I noticed that if I generate complex rv i.i.d. with var=1, that numpy
says:
var (real part) - (close to 1.0)
var (imag part) - (close to 1.0)
but
var (complex array) - (close to complex 0)
Is that not a strange definition?
I don't think there is any ambiguity
Neal Becker wrote:
Neal Becker wrote:
I noticed that if I generate complex rv i.i.d. with var=1, that numpy
says:
var (real part) - (close to 1.0)
var (imag part) - (close to 1.0)
but
var (complex array) - (close to complex 0)
Is that not a strange definition?
I don't think there
Robert Kern wrote:
Travis E. Oliphant wrote:
Robert Kern wrote:
Neal Becker wrote:
I noticed that if I generate complex rv i.i.d. with var=1, that numpy says:
var (real part) - (close to 1.0)
var (imag part) - (close to 1.0)
but
var (complex array) - (close to
Neal Becker wrote:
I noticed that if I generate complex rv i.i.d. with var=1, that numpy says:
var (real part) - (close to 1.0)
var (imag part) - (close to 1.0)
but
var (complex array) - (close to complex 0)
Is that not a strange definition?
There is some discussion on this in the
I noticed that if I generate complex rv i.i.d. with var=1, that numpy says:
var (real part) - (close to 1.0)
var (imag part) - (close to 1.0)
but
var (complex array) - (close to complex 0)
Is that not a strange definition?
___
Numpy-discussion
Robert Kern wrote:
Neal Becker wrote:
I noticed that if I generate complex rv i.i.d. with var=1, that numpy
says:
var (real part) - (close to 1.0)
var (imag part) - (close to 1.0)
but
var (complex array) - (close to complex 0)
Is that not a strange definition?
There is some
Charles R Harris wrote:
Suppose you have a set of z_i and want to choose z to minimize the
average square error $ \sum_i |z_i - z|^2 $. The solution is that
$z=\mean{z_i}$ and the resulting average error is given by 2). Note that
I didn't mention Gaussians anywhere. No distribution is
Neal Becker wrote:
2 is what I expected. Suppose I have a complex signal x, with additive
Gaussian noise (i.i.d, real and imag are independent).
y = x + n
Not only do the real and imag marginal distributions have to be independent,
but
also of the same scale, i.e. Re(n) ~ Gaussian(0,
On Jan 8, 2008 7:48 PM, Robert Kern [EMAIL PROTECTED] wrote:
Charles R Harris wrote:
Suppose you have a set of z_i and want to choose z to minimize the
average square error $ \sum_i |z_i - z|^2 $. The solution is that
$z=\mean{z_i}$ and the resulting average error is given by 2). Note
Robert Kern wrote:
Neal Becker wrote:
I noticed that if I generate complex rv i.i.d. with var=1, that numpy says:
var (real part) - (close to 1.0)
var (imag part) - (close to 1.0)
but
var (complex array) - (close to complex 0)
Is that not a strange definition?
2. Take a
Charles R Harris wrote:
On Jan 8, 2008 7:48 PM, Robert Kern [EMAIL PROTECTED]
mailto:[EMAIL PROTECTED] wrote:
Charles R Harris wrote:
Suppose you have a set of z_i and want to choose z to minimize the
average square error $ \sum_i |z_i - z|^2 $. The solution is that
Travis E. Oliphant wrote:
Robert Kern wrote:
Neal Becker wrote:
I noticed that if I generate complex rv i.i.d. with var=1, that numpy says:
var (real part) - (close to 1.0)
var (imag part) - (close to 1.0)
but
var (complex array) - (close to complex 0)
Is that not a strange
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