Also note that float128 is rarely what you want. It is not a quad precision value, it maps to C long double which is 80 bit on x86 and less on stuff like arm. On 30.11.2016 22:59, Nathan Goldbaum wrote:
I think this is a deficiency in the current implementation of bincount, which always casts the weights to float64. This WIP pull request should probably fix it:
https://github.com/numpy/numpy/pull/7464
On Wed, Nov 30, 2016 at 3:54 PM, Wei, Huayi <weihuayi@xtu.edu.cn <mailto:weihuayi@xtu.edu.cn>> wrote:
Hi, There,
Here is a sample code using `numpy.bincount`
import numpy as np a = np.array([1.0, 2.0, 3.0], dtype=np.float128) b = np.array([1, 2, 0], dtype=np.int <http://np.int>) c = np.bincount(b, weights=a)
If run it, I get the following error report:
----> 1 c = np.bincount(b, weights=a) TypeError: Cannot cast array data from dtype('float128') to dtype('float64') according to the rule 'safe'
Is it a bug of `np.bincount`? Does there exist any similar function which I can use to do the similar thing with numpy.float128 type weights?
Best
Huayi
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org> https://mail.scipy.org/mailman/listinfo/numpy-discussion <https://mail.scipy.org/mailman/listinfo/numpy-discussion>
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion