Hello Everyone, Another behavior we might consider changing for 1.0.2 that I believe is somewhat related in theme is the default type used in computations like the mean() method.
This is best illustrated with the following example: sparty> python Python 2.5 (r25:51908, Sep 21 2006, 13:33:15) [GCC 3.2.3 20030502 (Red Hat Linux 3.2.3-56)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as n >>> n.__version__ '1.0.2.dev3568' >>> a = n.ones((1000,1000),dtype=n.float32)*132.00005 >>> print a [[ 132.00004578 132.00004578 132.00004578 ..., 132.00004578 132.00004578 132.00004578] [ 132.00004578 132.00004578 132.00004578 ..., 132.00004578 132.00004578 132.00004578] [ 132.00004578 132.00004578 132.00004578 ..., 132.00004578 132.00004578 132.00004578] ..., [ 132.00004578 132.00004578 132.00004578 ..., 132.00004578 132.00004578 132.00004578] [ 132.00004578 132.00004578 132.00004578 ..., 132.00004578 132.00004578 132.00004578] [ 132.00004578 132.00004578 132.00004578 ..., 132.00004578 132.00004578 132.00004578]] >>> a.min() 132.000045776 >>> a.max() 132.000045776 >>> a.mean() 133.96639999999999 >>> Having the mean be greater than the max is a tad odd. The calculation of the mean is occurring with a single precision accumulator. I do understand that I can force a double precision calculation with the following command: >>> a.mean(dtype=n.float64) 132.00004577636719 >>> I realize that one reason for not doing all calculations as double precision is performance. However, my users would rather have the correct answer by default than quickly arriving at the wrong one. In my opinion we should swap the default behavior. All calculations should be done in double precision. If you need the performance you can then go back and start setting data types. Not having to worry about overflow would also be consistent with numarray's behavior. Thank you for considering my opinion, Chris _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion