float precision in numpy arrays differ from their elements

Question

I have values of numpy.float32 type, and a numpy array with dtype="float32".

When outputting the representation of the values directly using the individual array element references, I get a difference in precision than when I output the representation of the array object itself.

The ensuing calculations are also a bit off, so the latter value seems to be the one used in consequtive vector / matrix artihmetic.

Why is there a difference, and must these precision "anomalies" be handled manually?

>>> a = math.pi / 2 >>> a 1.5707963267948966 >>> elementx = numpy.float32(math.cos(a)) >>> arrayx = numpy.array([elementx], dtype="float32") >>> elementx 6.1232343e-17 >>> arrayx array([ 6.12323426e-17], dtype=float32) >>> t = numpy.float32(3.0) >>> t * elementx 1.8369703e-16 >>> t * arrayx array([ 1.83697028e-16], dtype=float32)

(Python 3.5.2, GCC 5.4.0, linux 4.4.0-21-generic)

Stop harming Monica · Accepted Answer · 2016-09-19 06:42:02Z

2

These are just differences in the string representation, the values are the same and calculations are not off.

>>> (t * elementx) == (t * arrayx)[0] True

answered Sep 19, 2016 at 6:42

Stop harming Monica

12.7k1 gold badge40 silver badges63 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

lash Over a year ago

Even: (t * elementx) == (t * arrayx) => array([ True], dtype=bool) .... which actually was my question :) thanks.

Collectives™ on Stack Overflow

float precision in numpy arrays differ from their elements

1 Answer 1

1 Comment

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

1 Comment

Related