On Sa, 2015-12-19 at 06:55 -0600, Andy Ray Terrel wrote:

A simple fix would certainly by pass the check in random.choice, but I don't know how to get that. So let's focus on the summation.

I believe you are hitting an instability in summing small numbers as a power to 10th order would produce. Here is an example:

mymatrix = np.random.rand(1024,1024).astype('float16')*1e-7 row_normalized = mymatrix / np.sum(mymatrix, axis=1, keepdims=True)

sums = row_normalized.sum(axis=1) len(sums[sums != 1]) # -> 108

One can use things like Kahan summation and you will need to collect the size of the error and truncate all numbers in mymatrix under that error. I'm not quite sure how to quickly implement such a thing in numpy without a loop.

In fact, the code even seems to do kahan summation, however, I think it always assumes double precision for the p keyword argument, so as a work around at least, you have to sum to convert to and normalize it as double. - Sebastian

On Fri, Dec 18, 2015 at 7:00 PM, Nathaniel Smith wrote: On Fri, Dec 18, 2015 at 1:25 PM, Ryan R. Rosario wrote: > Hi, > > I have a matrix whose entries I must raise to a certain power and then normalize by row. After I do that, when I pass some rows to numpy.random.choice, I get a ValueError: probabilities do not sum to 1. > > I understand that floating point is not perfect, and my matrix is so large that I cannot use np.longdouble because I will run out of RAM. > > As an example on a smaller matrix: > > np.power(mymatrix, 10, out=mymatrix) > row_normalized = np.apply_along_axis(lambda x: x / np.sum(x), 1, mymatrix)

I'm sorry I don't have a solution to your actual problem off the top of my head, but it's probably helpful in general to know that a better way to write this would be just

row_normalized = mymatrix / np.sum(mymatrix, axis=1, keepdims=True)

apply_along_axis is slow and can almost always be replaced by a broadcasting expression like this.

> sums = row_normalized.sum(axis=1) > sums[np.where(sums != 1)]

And here you can just write

sums[sums != 1]

i.e. the call to where() isn't doing anything useful.

-n

-- Nathaniel J. Smith -- http://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

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