Question about numpy.random, especially .poisson
I stumbled across code that looks like this: imageArr = # a 2-d array of floats noiseArr = numpy.random.poisson(imageArr) This works fine in numpy 1.5.1 and seems to do what I would hope: return an array of random ints whose "expectation of interval" is set by the corresponding element of the input array. Very nice! However, I can't find any documentation supporting this usage. The standard help says: poisson(lam=1.0, size=None) Draw samples from a Poisson distribution. The Poisson distribution is the limit of the Binomial distribution for large N. Parameters ---------- lam : float Expectation of interval, should be >= 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Which suggest that Iam must be a scalar. So... is the usage of passing in an array for Iam actually supported/safe to use? And is there some general rule I could have used to predict that? I'm not complaining -- quite the opposite. But I'd hate to code up something that uses an unsafe API, and I'd also like to be able to predict nifty features like this to get the most out of numpy. -- Russell
On Mon, Apr 18, 2011 at 17:08, Russell E. Owen
I stumbled across code that looks like this:
imageArr = # a 2-d array of floats noiseArr = numpy.random.poisson(imageArr)
This works fine in numpy 1.5.1 and seems to do what I would hope: return an array of random ints whose "expectation of interval" is set by the corresponding element of the input array. Very nice!
However, I can't find any documentation supporting this usage. The standard help says:
poisson(lam=1.0, size=None)
Draw samples from a Poisson distribution.
The Poisson distribution is the limit of the Binomial distribution for large N.
Parameters ---------- lam : float Expectation of interval, should be >= 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn.
Which suggest that Iam must be a scalar.
So... is the usage of passing in an array for Iam actually supported/safe to use?
Yes, it is safe. All of the univariate distribution parameters will broadcast against each other as they would with ufuncs. I don't think we do it for multinomial and a couple of the other ones where the semantics would have made my head hurt.
And is there some general rule I could have used to predict that?
Just the one I stated, which is limited to numpy.random. It might even be documented somewhere. Unfortunately, most of the individual methods had their parameters documented before this capability was added. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco
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Robert Kern -
Russell E. Owen