As in title, I need to perform numpy.exp on a very large ndarray, let's say ar, and store the result in ar itself. Can this operation be performed in-place?
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问题:
回答1:
You can use the optional outargument of exp:
a = np.array([3.4, 5]) res = np.exp(a, a) print(res is a) print(a) Output:
True [ 29.96410005 148.4131591 ] exp(x[, out])
Calculate the exponential of all elements in the input array.
Returns
out : ndarray Output array, element-wise exponential of
x.
Here all elements of a will be replaced by the result of exp. The return value res is the same as a. No new array is created
回答2:
Mike Mueller's answer is good but please note that if your array is of type int32, int, int64 etc., it will throw a TypeError. Thus, a safe way to do this is to typecast your array to float64 or float32 etc., before doing exp like,
In [12]: b Out[12]: array([1, 2, 3, 4, 5], dtype=int32) In [13]: np.exp(b, b) -------------------------------------------------------------------------- TypeError: ufunc 'exp' output (typecode 'd') could not be coerced to provided output parameter (typecode 'i') according to the casting rule ''same_kind'' Type Casting & exp:
# in-place typecasting In [14]: b = b.astype(np.float64, copy=False) In [15]: b Out[15]: array([ 1., 2., 3., 4., 5.], dtype=float64) # modifies b in-place In [16]: np.exp(b, b) Out[16]: array([ 2.718, 7.389, 20.086, 54.598, 148.413], dtype=float64)