jax.numpy.finfo#
- class jax.numpy.finfo(dtype)[source]#
Machine limits for floating point types.
- dtype#
Returns the dtype for which finfo returns information. For complex input, the returned dtype is the associated
float*
dtype for its real and complex components.- Type:
- eps#
The difference between 1.0 and the next smallest representable float larger than 1.0. For example, for 64-bit binary floats in the IEEE-754 standard,
eps = 2**-52
, approximately 2.22e-16.- Type:
- epsneg#
The difference between 1.0 and the next smallest representable float less than 1.0. For example, for 64-bit binary floats in the IEEE-754 standard,
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 there being no leading 0’s in the mantissa.
- Type:
- precision#
The approximate number of decimal digits to which this kind of float is precise.
- Type:
- resolution#
The approximate decimal resolution of this type, i.e.,
10**-precision
.- Type:
floating point number of the appropriate type
- smallest_normal[source]#
The smallest positive floating point number with 1 as leading bit in the mantissa following IEEE-754 (see Notes).
- Type:
- smallest_subnormal#
The smallest positive floating point number with 0 as leading bit in the mantissa following IEEE-754.
- Type:
- Parameters:
dtype (float, dtype, or instance) – Kind of floating point or complex floating point data-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 negatively impacts import times. These objects are cached, so calling
finfo()
repeatedly inside your functions is not a problem.Note that
smallest_normal
is not actually the smallest positive representable value in a NumPy floating point type. As in the IEEE-754 standard [1], NumPy floating point types make use of subnormal numbers to fill the gap between 0 andsmallest_normal
. However, subnormal numbers may 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
Examples
>>> import numpy as np >>> np.finfo(np.float64).dtype dtype('float64') >>> np.finfo(np.complex64).dtype dtype('float32')
- __init__()#
Methods
__init__
()Attributes
Return the value for the smallest normal.
Return the value for tiny, alias of smallest_normal.