On Fri, Dec 18, 2015 at 1:25 PM, Ryan R. Rosario <r...@bytemining.com> 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
