2013/8/24 Peter Prettenhofer <peter.prettenho...@gmail.com>: > can anybody help me understand why the output of the following code snippet > is different depending on 32bit or 64bit architecture:: > > x = np.empty((10 ** 6,), dtype=np.float64) > x.fill(1e-9) > hash(x.mean()) > > on 64bit I get: 2475364768 > on 32bit I get: -1839780448 > > I expected that given you set an explicit dtype the result would be equal on > whatever architecture.
Is that Python's built-in hash function? That's not even guaranteed to be consistent between runs of the same Python interpreter on the same machine. > Even worse when I do the following:: > > np.sum(x) > > I too get different results but when I do:: > > sum(x) > > I get equal results... could it be that the temporary variables that numpy > uses in this routines are platform dependent or am I missing something here. How unequal? IIRC, x86 processors do all computations using 80-bit floating point numbers internally, and amd64 (x86-64) uses 128-bit. -- Lars Buitinck Scientific programmer, ILPS University of Amsterdam ------------------------------------------------------------------------------ Introducing Performance Central, a new site from SourceForge and AppDynamics. Performance Central is your source for news, insights, analysis and resources for efficient Application Performance Management. Visit us today! http://pubads.g.doubleclick.net/gampad/clk?id=48897511&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
