This code:
a = np.array([10], dtype=np.int8)
b = np.array([2], dtype=np.int8)
print(np.dot(a, b))
a = np.array([10], dtype=np.int8)
b = np.array([5], dtype=np.int8)
print(np.dot(a, b))
a = np.array([10], dtype=np.int8)
b = np.array([20], dtype=np.int8)
print(np.dot(a, b))
produces the following output:
20
50
-56
It appears that np.dot will attempt to return the result in the same data type object even if it can't fit. Surely this is a bug? Why doesn't it throw an exception?
This is true for multiplication and addition.
In [89]: np.array([128], 'int8')*2
Out[89]: array([0], dtype=int8)
In [90]: np.array([127], 'int8')*2
Out[90]: array([-2], dtype=int8) # same int8 dtype
But if I work with an element of the array, a np.int8 object, the result is promoted.
In [91]: np.array([127], 'int8')[0]*2
Out[91]: 254
In [92]: type(_)
Out[92]: numpy.int32
I think, though can't offhand produce, there are cases where this kind of thing raises an error.
This has been discussed in other SO, for multiplication, if not for np.dot.
This is a overflow question for 'uint8' dtypes, and github issues link:
Allow overflow for numpy types
https://github.com/numpy/numpy/issues/8987 "BUG: Integer overflow warning applies to scalars but not arrays"