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> 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) 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

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Fist off, a word of caution. float128 depends on your system and maps to whatever longdouble is (IIRC) or may not even exist. So I hope you don't expect IEEE 128 bit floats, if you are unsure, maybe check `np.finfo`. If speed does not matter ``` res = np.zeros(np.max(b), dtype=np.longdouble) np.add.at(res, b, a) ``` will work, but do not expect it to be fast. - Sebastian On Do, 2016-12-01 at 05:54 +0800, Wei, Huayi 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)      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

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