jax.numpy.finfo
Contents
jax.numpy.finfo#
- classjax.numpy.finfo(dtype)#
Machine limits for floating point types.
- dtype#
Returns the dtype for whichfinfo returns information. For complexinput, the returned dtype is the associated
float*dtype for itsreal and complex components.- Type:
- eps#
The difference between 1.0 and the next smallest representable floatlarger than 1.0. For example, for 64-bit binary floats in the IEEE-754standard,
eps=2**-52, approximately 2.22e-16.- Type:
- epsneg#
The difference between 1.0 and the next smallest representable floatless than 1.0. For example, for 64-bit binary floats in the IEEE-754standard,
epsneg=2**-53, approximately 1.11e-16.- Type:
- max#
The largest representable number.
- Type:
floating point number of the appropriate type
- min#
The smallest representable number, typically
-max.- Type:
floating point number of the appropriate type
- minexp#
The most negative power of the base (2) consistent with therebeing no leading 0’s in the mantissa.
- Type:
- resolution#
The approximate decimal resolution of this type, i.e.,
10**-precision.- Type:
floating point number of the appropriate type
- smallest_normal#
The smallest positive floating point number with 1 as leading bit inthe mantissa following IEEE-754 (see Notes).
- Type:
- smallest_subnormal#
The smallest positive floating point number with 0 as leading bit inthe mantissa following IEEE-754.
- Type:
- Parameters:
dtype (float,dtype, orinstance) – Kind of floating point or complex floating pointdata-type about which to get information.
See also
Notes
For developers of NumPy: do not instantiate this at the module level.The initial calculation of these parameters is expensive and negativelyimpacts import times. These objects are cached, so calling
finfo()repeatedly inside your functions is not a problem.Note that
smallest_normalis not actually the smallest positiverepresentable value in a NumPy floating point type. As in the IEEE-754standard[1], NumPy floating point types make use of subnormal numbers tofill the gap between 0 andsmallest_normal. However, subnormal numbersmay have significantly reduced precision[2].This function can also be used for complex data types as well. If used,the output will be the same as the corresponding real float type(e.g. numpy.finfo(numpy.csingle) is the same as numpy.finfo(numpy.single)).However, the output is true for the real and imaginary components.
References
[1]IEEE Standard for Floating-Point Arithmetic, IEEE Std 754-2008,pp.1-70, 2008,https://doi.org/10.1109/IEEESTD.2008.4610935
[2]Wikipedia, “Denormal Numbers”,https://en.wikipedia.org/wiki/Denormal_number
Examples
>>>importnumpyasnp>>>np.finfo(np.float64).dtypedtype('float64')>>>np.finfo(np.complex64).dtypedtype('float32')
- __init__()#
Methods
__init__()Attributes
Return the value for the smallest normal.
Return the value for tiny, alias of smallest_normal.
