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The following code:
>>> import numpy as np
>>> np.arange(2).astype(np.int8) * 127
produces for numpy 1.13.3
# On Windows
array([0, 127], dtype=int16)
# On Linux
array([0, 127], dtype=int8)
However, if I change the 127 to a 126, both return a np.int8 array. And if I change the 127 to a 128 both return a np.int16 array.
Questions:
630
multiply() function is used when we want to compute the multiplication of two array. It returns the product of arr1 and arr2, element-wise.
What does Numpy Multiply Function do? The numpy multiply function calculates the product between the two numpy arrays. It calculates the product between the two arrays, say x1 and x2, element-wise.
We can use numpy. prod() from import numpy to get the multiplication of all the numbers in the list.
If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred. If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy.multiply(a, b) or a * b is preferred. If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.
This is due to NumPy issue 5917. A < instead of <= caused np.can_cast(127, np.int8) to be False, so NumPy used a too-large dtype for 127. The OS-dependence is because C longs have a different size on Linux and Windows, and some NumPy code paths depend on the size of a C long.
A fix has been released in NumPy 1.14.0. Once you update to at least NumPy 1.14.0, you should see a dtype of int8 on all platforms.
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