Funny behavior with numba - guvectorized functions using argmax()
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Consider the following script:
from numba import guvectorize, u1, i8
import numpy as np
@guvectorize([(u1[:],i8)], '(n)->()')
def f(x, res):
res = x.argmax()
x = np.array([1,2,3],dtype=np.uint8)
print(f(x))
print(x.argmax())
print(f(x))
When running it, I get the following:
4382569440205035030
2
2
Why is this happening? Is there a way to get it right?
vectorization numba argmax
asked Nov 23 '18 at 20:50
Rodrigo VargasRodrigo Vargas
82
add a comment |
Consider the following script:
from numba import guvectorize, u1, i8
import numpy as np
@guvectorize([(u1[:],i8)], '(n)->()')
def f(x, res):
res = x.argmax()
x = np.array([1,2,3],dtype=np.uint8)
print(f(x))
print(x.argmax())
print(f(x))
When running it, I get the following:
4382569440205035030
2
2
Why is this happening? Is there a way to get it right?
vectorization numba argmax
asked Nov 23 '18 at 20:50
Rodrigo VargasRodrigo Vargas
82
add a comment |
Consider the following script:
from numba import guvectorize, u1, i8
import numpy as np
@guvectorize([(u1[:],i8)], '(n)->()')
def f(x, res):
res = x.argmax()
x = np.array([1,2,3],dtype=np.uint8)
print(f(x))
print(x.argmax())
print(f(x))
When running it, I get the following:
4382569440205035030
2
2
Why is this happening? Is there a way to get it right?
vectorization numba argmax
asked Nov 23 '18 at 20:50
Rodrigo VargasRodrigo Vargas
82
Consider the following script:
from numba import guvectorize, u1, i8
import numpy as np
@guvectorize([(u1[:],i8)], '(n)->()')
def f(x, res):
res = x.argmax()
x = np.array([1,2,3],dtype=np.uint8)
print(f(x))
print(x.argmax())
print(f(x))
When running it, I get the following:
4382569440205035030
2
2
Why is this happening? Is there a way to get it right?
vectorization numba argmax
vectorization numba argmax
asked Nov 23 '18 at 20:50
Rodrigo VargasRodrigo Vargas
82
asked Nov 23 '18 at 20:50
Rodrigo VargasRodrigo Vargas
82
asked Nov 23 '18 at 20:50
Rodrigo VargasRodrigo Vargas
82
asked Nov 23 '18 at 20:50
Rodrigo VargasRodrigo Vargas
82
asked Nov 23 '18 at 20:50
Rodrigo VargasRodrigo Vargas
82
82
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Python doesn't have references, so res = ... is not actually assigning to the output parameter, but instead rebinding the name res. I believe res is pointing to uninitialized memory, which is why your first run gives a seemingly random value.
Numba works around this using the slice syntax ([:]) which does mutate res- you also need to declare the type as an array. A working function is:
@guvectorize([(u1[:], i8[:])], '(n)->()')
def f(x, res):
res[:] = x.argmax()
answered Nov 27 '18 at 14:43
chrisbchrisb
25.2k73840
I had somehow managed to convince myself that the weird behavior had to do with numba in combination with argmax, but indeed something likeres = x[0]has the same problem.
– Rodrigo Vargas
Nov 28 '18 at 18:27
add a comment |
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1 Answer
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active
oldest
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Python doesn't have references, so res = ... is not actually assigning to the output parameter, but instead rebinding the name res. I believe res is pointing to uninitialized memory, which is why your first run gives a seemingly random value.
Numba works around this using the slice syntax ([:]) which does mutate res- you also need to declare the type as an array. A working function is:
@guvectorize([(u1[:], i8[:])], '(n)->()')
def f(x, res):
res[:] = x.argmax()
answered Nov 27 '18 at 14:43
chrisbchrisb
25.2k73840
I had somehow managed to convince myself that the weird behavior had to do with numba in combination with argmax, but indeed something likeres = x[0]has the same problem.
– Rodrigo Vargas
Nov 28 '18 at 18:27
add a comment |
Python doesn't have references, so res = ... is not actually assigning to the output parameter, but instead rebinding the name res. I believe res is pointing to uninitialized memory, which is why your first run gives a seemingly random value.
Numba works around this using the slice syntax ([:]) which does mutate res- you also need to declare the type as an array. A working function is:
@guvectorize([(u1[:], i8[:])], '(n)->()')
def f(x, res):
res[:] = x.argmax()
answered Nov 27 '18 at 14:43
chrisbchrisb
25.2k73840
I had somehow managed to convince myself that the weird behavior had to do with numba in combination with argmax, but indeed something likeres = x[0]has the same problem.
– Rodrigo Vargas
Nov 28 '18 at 18:27
add a comment |
Python doesn't have references, so res = ... is not actually assigning to the output parameter, but instead rebinding the name res. I believe res is pointing to uninitialized memory, which is why your first run gives a seemingly random value.
Numba works around this using the slice syntax ([:]) which does mutate res- you also need to declare the type as an array. A working function is:
@guvectorize([(u1[:], i8[:])], '(n)->()')
def f(x, res):
res[:] = x.argmax()
answered Nov 27 '18 at 14:43
chrisbchrisb
25.2k73840
Python doesn't have references, so res = ... is not actually assigning to the output parameter, but instead rebinding the name res. I believe res is pointing to uninitialized memory, which is why your first run gives a seemingly random value.
Numba works around this using the slice syntax ([:]) which does mutate res- you also need to declare the type as an array. A working function is:
@guvectorize([(u1[:], i8[:])], '(n)->()')
def f(x, res):
res[:] = x.argmax()
answered Nov 27 '18 at 14:43
chrisbchrisb
25.2k73840
answered Nov 27 '18 at 14:43
chrisbchrisb
25.2k73840
answered Nov 27 '18 at 14:43
chrisbchrisb
25.2k73840
answered Nov 27 '18 at 14:43
chrisbchrisb
25.2k73840
25.2k73840
I had somehow managed to convince myself that the weird behavior had to do with numba in combination with argmax, but indeed something likeres = x[0]has the same problem.
– Rodrigo Vargas
Nov 28 '18 at 18:27
add a comment |
I had somehow managed to convince myself that the weird behavior had to do with numba in combination with argmax, but indeed something likeres = x[0]has the same problem.
– Rodrigo Vargas
Nov 28 '18 at 18:27
I had somehow managed to convince myself that the weird behavior had to do with numba in combination with argmax, but indeed something like
res = x[0] has the same problem.– Rodrigo Vargas
Nov 28 '18 at 18:27
I had somehow managed to convince myself that the weird behavior had to do with numba in combination with argmax, but indeed something like
res = x[0] has the same problem.– Rodrigo Vargas
Nov 28 '18 at 18:27
add a comment |
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