Thomas Robitaille wrote: > Hi, > > I'm trying to generate random 64-bit integer values for integers and > floats using Numpy, within the entire range of valid values for that > type. To generate random 32-bit floats, I can use: > > np.random.uniform(low=np.finfo(np.float32).min,high=np.finfo > (np.float32).max,size=10) > > which gives for example > > array([ 1.47351436e+37, 9.93620693e+37, 2.22893053e+38, > -3.33828977e+38, 1.08247781e+37, -8.37481260e+37, > 2.64176554e+38, -2.72207226e+37, 2.54790459e+38, > -2.47883866e+38]) > > but if I try and use this for 64-bit numbers, i.e. > > np.random.uniform(low=np.finfo(np.float64).min,high=np.finfo > (np.float64).max,size=10) > > I get > > array([ Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf]) > > Similarly, for integers, I can successfully generate random 32-bit > integers: > > np.random.random_integers(np.iinfo(np.int32).min,high=np.iinfo > (np.int32).max,size=10) > > which gives > > array([-1506183689, 662982379, -1616890435, -1519456789, 1489753527, > -604311122, 2034533014, 449680073, -444302414, > -1924170329]) > > but am unsuccessful for 64-bit integers, i.e. > > np.random.random_integers(np.iinfo(np.int64).min,high=np.iinfo > (np.int64).max,size=10) > > which produces the following error: > > OverflowError: long int too large to convert to int > > Is this expected behavior, or are these bugs? >
I think those are bugs, but it may be difficult to fix. You can check that if you restrict a tiny bit your interval, you get better result: import numpy as np # max/min for double precision is ~ 1.8e308 low, high = -1e308, 1e308 np.random.uniformat(low, high, 100) # bunch of inf low, high = -1e307, 1e307 np.random.uniformat(low, high, 100) # much more reasonable It may be that you are pushing the limits of the random generator. Your min and max may be border cases: if you use the min/max representable numbers, and the random generator needs to do any addition of a positive number, you will 'overflow' your float number (Robert will have a better answer to this). The problem is that it may be difficult to detect this in advance. David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
