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Apply multiple functions to each row of a numpy array
How can I apply a function to every row/column of a matrix in MATLAB?What's a variable in a inner function definition in python?Apply a function to each row of a ndarrayApply function to vectors in 3D numpy arrayapply function to each column of a matrix (Vectorizing)Vectorize numpy indexing and apply a function to build a matrixnumpy array operation methodAll possible columnwise multiplications in numpy?Create binary array of matching rows in an array using numpy?Numpy - Apply a custom function on all combination of rows in matrix to get a new matrix?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
Let's say we have a matrix of 3 rows and 2 columns as mat, and I want to apply on each 3 row one of the functions of list what_functions_to_apply_list for which I have their definition. So the output of np.apply_along_axis should be 3 rows times the output dimension of the functions.
How can I do this without for looping in a vectorized way?
e.g.
def f1(inp1,inp2):
return out1, out2
where
functions_dic = 'f1': func1, 'f2':func2, 'f3':func3
what_functions_to_apply_list = ['f1','f1','f2']
funcs_inputs = [[inp11,inp12], [inp21,inp32], [inp31,inp32]]
mat = np.ones((3, 2))
np.apply_along_axis(what_functions_to_apply_list , 1, mat)
python function numpy vectorization
asked Mar 26 at 21:17
SoyolSoyol
3532 silver badges19 bronze badges
add a comment |
Let's say we have a matrix of 3 rows and 2 columns as mat, and I want to apply on each 3 row one of the functions of list what_functions_to_apply_list for which I have their definition. So the output of np.apply_along_axis should be 3 rows times the output dimension of the functions.
How can I do this without for looping in a vectorized way?
e.g.
def f1(inp1,inp2):
return out1, out2
where
functions_dic = 'f1': func1, 'f2':func2, 'f3':func3
what_functions_to_apply_list = ['f1','f1','f2']
funcs_inputs = [[inp11,inp12], [inp21,inp32], [inp31,inp32]]
mat = np.ones((3, 2))
np.apply_along_axis(what_functions_to_apply_list , 1, mat)
python function numpy vectorization
asked Mar 26 at 21:17
SoyolSoyol
3532 silver badges19 bronze badges
2
You can't. Applying a custom function like this will necessarily require a call to Python and it will run infor-loop speed
– roganjosh
Mar 26 at 21:22
apply_along_axisisn't meant for that kind of use. For a start, the first argument is a function, not a list of functions. Even with a correct function it isn't faster alternative to explicit iteration. It is not a 'no-loop vectorized' tool.
– hpaulj
Mar 26 at 21:53
Please add sample data.
– Istvan
Mar 26 at 22:13
add a comment |
Let's say we have a matrix of 3 rows and 2 columns as mat, and I want to apply on each 3 row one of the functions of list what_functions_to_apply_list for which I have their definition. So the output of np.apply_along_axis should be 3 rows times the output dimension of the functions.
How can I do this without for looping in a vectorized way?
e.g.
def f1(inp1,inp2):
return out1, out2
where
functions_dic = 'f1': func1, 'f2':func2, 'f3':func3
what_functions_to_apply_list = ['f1','f1','f2']
funcs_inputs = [[inp11,inp12], [inp21,inp32], [inp31,inp32]]
mat = np.ones((3, 2))
np.apply_along_axis(what_functions_to_apply_list , 1, mat)
python function numpy vectorization
asked Mar 26 at 21:17
SoyolSoyol
3532 silver badges19 bronze badges
Let's say we have a matrix of 3 rows and 2 columns as mat, and I want to apply on each 3 row one of the functions of list what_functions_to_apply_list for which I have their definition. So the output of np.apply_along_axis should be 3 rows times the output dimension of the functions.
How can I do this without for looping in a vectorized way?
e.g.
def f1(inp1,inp2):
return out1, out2
where
functions_dic = 'f1': func1, 'f2':func2, 'f3':func3
what_functions_to_apply_list = ['f1','f1','f2']
funcs_inputs = [[inp11,inp12], [inp21,inp32], [inp31,inp32]]
mat = np.ones((3, 2))
np.apply_along_axis(what_functions_to_apply_list , 1, mat)
python function numpy vectorization
python function numpy vectorization
asked Mar 26 at 21:17
SoyolSoyol
3532 silver badges19 bronze badges
asked Mar 26 at 21:17
SoyolSoyol
3532 silver badges19 bronze badges
asked Mar 26 at 21:17
SoyolSoyol
3532 silver badges19 bronze badges
asked Mar 26 at 21:17
SoyolSoyol
3532 silver badges19 bronze badges
asked Mar 26 at 21:17
SoyolSoyol
3532 silver badges19 bronze badges
3532 silver badges19 bronze badges
2
You can't. Applying a custom function like this will necessarily require a call to Python and it will run infor-loop speed
– roganjosh
Mar 26 at 21:22
apply_along_axisisn't meant for that kind of use. For a start, the first argument is a function, not a list of functions. Even with a correct function it isn't faster alternative to explicit iteration. It is not a 'no-loop vectorized' tool.
– hpaulj
Mar 26 at 21:53
Please add sample data.
– Istvan
Mar 26 at 22:13
add a comment |
2
You can't. Applying a custom function like this will necessarily require a call to Python and it will run infor-loop speed
– roganjosh
Mar 26 at 21:22
apply_along_axisisn't meant for that kind of use. For a start, the first argument is a function, not a list of functions. Even with a correct function it isn't faster alternative to explicit iteration. It is not a 'no-loop vectorized' tool.
– hpaulj
Mar 26 at 21:53
Please add sample data.
– Istvan
Mar 26 at 22:13
2
2
You can't. Applying a custom function like this will necessarily require a call to Python and it will run in
for-loop speed– roganjosh
Mar 26 at 21:22
You can't. Applying a custom function like this will necessarily require a call to Python and it will run in
for-loop speed– roganjosh
Mar 26 at 21:22
apply_along_axis isn't meant for that kind of use. For a start, the first argument is a function, not a list of functions. Even with a correct function it isn't faster alternative to explicit iteration. It is not a 'no-loop vectorized' tool.– hpaulj
Mar 26 at 21:53
apply_along_axis isn't meant for that kind of use. For a start, the first argument is a function, not a list of functions. Even with a correct function it isn't faster alternative to explicit iteration. It is not a 'no-loop vectorized' tool.– hpaulj
Mar 26 at 21:53
Please add sample data.
– Istvan
Mar 26 at 22:13
Please add sample data.
– Istvan
Mar 26 at 22:13
add a comment |
1 Answer
1
active
oldest
votes
A straight forward application of a list of functions to the rows of an array:
In [418]: alist = [np.add, np.subtract, np.multiply]
In [419]: data = np.arange(6).reshape(3,2)
In [420]: [foo(*ab) for foo, ab in zip(alist, data)]
Out[420]: [1, -1, 20]
answered Mar 27 at 6:23
hpauljhpaulj
125k7 gold badges100 silver badges177 bronze badges
how about providing inputs to each function as well
– Soyol
Mar 27 at 16:41
This is providing inputs, the rows ofdata. If you mean other parameters (e.g. axis), you'll have to create functions (or lambdas) that set those first.
– hpaulj
Mar 27 at 17:11
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
A straight forward application of a list of functions to the rows of an array:
In [418]: alist = [np.add, np.subtract, np.multiply]
In [419]: data = np.arange(6).reshape(3,2)
In [420]: [foo(*ab) for foo, ab in zip(alist, data)]
Out[420]: [1, -1, 20]
answered Mar 27 at 6:23
hpauljhpaulj
125k7 gold badges100 silver badges177 bronze badges
how about providing inputs to each function as well
– Soyol
Mar 27 at 16:41
This is providing inputs, the rows ofdata. If you mean other parameters (e.g. axis), you'll have to create functions (or lambdas) that set those first.
– hpaulj
Mar 27 at 17:11
add a comment |
A straight forward application of a list of functions to the rows of an array:
In [418]: alist = [np.add, np.subtract, np.multiply]
In [419]: data = np.arange(6).reshape(3,2)
In [420]: [foo(*ab) for foo, ab in zip(alist, data)]
Out[420]: [1, -1, 20]
answered Mar 27 at 6:23
hpauljhpaulj
125k7 gold badges100 silver badges177 bronze badges
how about providing inputs to each function as well
– Soyol
Mar 27 at 16:41
This is providing inputs, the rows ofdata. If you mean other parameters (e.g. axis), you'll have to create functions (or lambdas) that set those first.
– hpaulj
Mar 27 at 17:11
add a comment |
A straight forward application of a list of functions to the rows of an array:
In [418]: alist = [np.add, np.subtract, np.multiply]
In [419]: data = np.arange(6).reshape(3,2)
In [420]: [foo(*ab) for foo, ab in zip(alist, data)]
Out[420]: [1, -1, 20]
answered Mar 27 at 6:23
hpauljhpaulj
125k7 gold badges100 silver badges177 bronze badges
A straight forward application of a list of functions to the rows of an array:
In [418]: alist = [np.add, np.subtract, np.multiply]
In [419]: data = np.arange(6).reshape(3,2)
In [420]: [foo(*ab) for foo, ab in zip(alist, data)]
Out[420]: [1, -1, 20]
answered Mar 27 at 6:23
hpauljhpaulj
125k7 gold badges100 silver badges177 bronze badges
answered Mar 27 at 6:23
hpauljhpaulj
125k7 gold badges100 silver badges177 bronze badges
answered Mar 27 at 6:23
hpauljhpaulj
125k7 gold badges100 silver badges177 bronze badges
answered Mar 27 at 6:23
hpauljhpaulj
125k7 gold badges100 silver badges177 bronze badges
125k7 gold badges100 silver badges177 bronze badges
how about providing inputs to each function as well
– Soyol
Mar 27 at 16:41
This is providing inputs, the rows ofdata. If you mean other parameters (e.g. axis), you'll have to create functions (or lambdas) that set those first.
– hpaulj
Mar 27 at 17:11
add a comment |
how about providing inputs to each function as well
– Soyol
Mar 27 at 16:41
This is providing inputs, the rows ofdata. If you mean other parameters (e.g. axis), you'll have to create functions (or lambdas) that set those first.
– hpaulj
Mar 27 at 17:11
how about providing inputs to each function as well
– Soyol
Mar 27 at 16:41
how about providing inputs to each function as well
– Soyol
Mar 27 at 16:41
This is providing inputs, the rows of
data. If you mean other parameters (e.g. axis), you'll have to create functions (or lambdas) that set those first.– hpaulj
Mar 27 at 17:11
This is providing inputs, the rows of
data. If you mean other parameters (e.g. axis), you'll have to create functions (or lambdas) that set those first.– hpaulj
Mar 27 at 17:11
add a comment |
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You can't. Applying a custom function like this will necessarily require a call to Python and it will run in
for-loop speed– roganjosh
Mar 26 at 21:22
apply_along_axisisn't meant for that kind of use. For a start, the first argument is a function, not a list of functions. Even with a correct function it isn't faster alternative to explicit iteration. It is not a 'no-loop vectorized' tool.– hpaulj
Mar 26 at 21:53
Please add sample data.
– Istvan
Mar 26 at 22:13