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Exponent an arbitrary number by a numpy array
I know np.exp2(x) exists that calculates 2^x where x is a numpy array, however, I am looking for a method that does K^x where K is any arbitrary number.
Is there any elegant way of doing it rather than stretching out K to the shape of x and doing a piecewise exponent?
python numpy
edited Nov 22 '18 at 8:23
user2357112
155k12167260
asked Nov 22 '18 at 5:01
Vj-Vj-
656617
add a comment |
I know np.exp2(x) exists that calculates 2^x where x is a numpy array, however, I am looking for a method that does K^x where K is any arbitrary number.
Is there any elegant way of doing it rather than stretching out K to the shape of x and doing a piecewise exponent?
python numpy
edited Nov 22 '18 at 8:23
user2357112
155k12167260
asked Nov 22 '18 at 5:01
Vj-Vj-
656617
@tel: The "piecewise" seems to make things more confusing rather than less.
– user2357112
Nov 22 '18 at 8:24
add a comment |
I know np.exp2(x) exists that calculates 2^x where x is a numpy array, however, I am looking for a method that does K^x where K is any arbitrary number.
Is there any elegant way of doing it rather than stretching out K to the shape of x and doing a piecewise exponent?
python numpy
edited Nov 22 '18 at 8:23
user2357112
155k12167260
asked Nov 22 '18 at 5:01
Vj-Vj-
656617
I know np.exp2(x) exists that calculates 2^x where x is a numpy array, however, I am looking for a method that does K^x where K is any arbitrary number.
Is there any elegant way of doing it rather than stretching out K to the shape of x and doing a piecewise exponent?
python numpy
python numpy
edited Nov 22 '18 at 8:23
user2357112
155k12167260
asked Nov 22 '18 at 5:01
Vj-Vj-
656617
edited Nov 22 '18 at 8:23
user2357112
155k12167260
asked Nov 22 '18 at 5:01
Vj-Vj-
656617
edited Nov 22 '18 at 8:23
user2357112
155k12167260
edited Nov 22 '18 at 8:23
user2357112
155k12167260
edited Nov 22 '18 at 8:23
user2357112
155k12167260
155k12167260
asked Nov 22 '18 at 5:01
Vj-Vj-
656617
asked Nov 22 '18 at 5:01
Vj-Vj-
656617
asked Nov 22 '18 at 5:01
Vj-Vj-
656617
656617
@tel: The "piecewise" seems to make things more confusing rather than less.
– user2357112
Nov 22 '18 at 8:24
add a comment |
@tel: The "piecewise" seems to make things more confusing rather than less.
– user2357112
Nov 22 '18 at 8:24
@tel: The "piecewise" seems to make things more confusing rather than less.
– user2357112
Nov 22 '18 at 8:24
@tel: The "piecewise" seems to make things more confusing rather than less.
– user2357112
Nov 22 '18 at 8:24
add a comment |
2 Answers
2
active
oldest
votes
Just use the standard Python exponentiation operator **:
K**x
For example, if you have:
x = np.array([1,2,3])
K = 3
print(K**x)
The output is:
[ 3 9 27]
Notes
For Python classes, the behavior of the binary ** operator is implemented via the __pow__, __rpow__, and __ipow__ magic methods (the reality for np.ndarray is slightly more complicated since it's implemented in the C layer, but that's not actually important here). For Numpy arrays, these magic methods in turn appear to call numpy.power, so you can expect that ** will have the same behavior as documented for numpy.power. In particular,
Note that an integer type raised to a negative integer power will raise a ValueError.
answered Nov 22 '18 at 5:07
teltel
7,41621431
small note: castKto a float or else negative numbers in the array will throw an error.
– Vj-
Nov 22 '18 at 5:14
@Vj- I added a note about the implementation of**, including where it mentions that particular error in the documentation
– tel
Nov 22 '18 at 5:32
add a comment |
With numpy you can just use numpy.power
arr = numpy.array([1,2,3])
print(numpy.power(3,arr)) # Outputs [ 3 9 27]
answered Nov 22 '18 at 5:11
b-fgb-fg
1,95911522
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Just use the standard Python exponentiation operator **:
K**x
For example, if you have:
x = np.array([1,2,3])
K = 3
print(K**x)
The output is:
[ 3 9 27]
Notes
For Python classes, the behavior of the binary ** operator is implemented via the __pow__, __rpow__, and __ipow__ magic methods (the reality for np.ndarray is slightly more complicated since it's implemented in the C layer, but that's not actually important here). For Numpy arrays, these magic methods in turn appear to call numpy.power, so you can expect that ** will have the same behavior as documented for numpy.power. In particular,
Note that an integer type raised to a negative integer power will raise a ValueError.
answered Nov 22 '18 at 5:07
teltel
7,41621431
small note: castKto a float or else negative numbers in the array will throw an error.
– Vj-
Nov 22 '18 at 5:14
@Vj- I added a note about the implementation of**, including where it mentions that particular error in the documentation
– tel
Nov 22 '18 at 5:32
add a comment |
Just use the standard Python exponentiation operator **:
K**x
For example, if you have:
x = np.array([1,2,3])
K = 3
print(K**x)
The output is:
[ 3 9 27]
Notes
For Python classes, the behavior of the binary ** operator is implemented via the __pow__, __rpow__, and __ipow__ magic methods (the reality for np.ndarray is slightly more complicated since it's implemented in the C layer, but that's not actually important here). For Numpy arrays, these magic methods in turn appear to call numpy.power, so you can expect that ** will have the same behavior as documented for numpy.power. In particular,
Note that an integer type raised to a negative integer power will raise a ValueError.
answered Nov 22 '18 at 5:07
teltel
7,41621431
small note: castKto a float or else negative numbers in the array will throw an error.
– Vj-
Nov 22 '18 at 5:14
@Vj- I added a note about the implementation of**, including where it mentions that particular error in the documentation
– tel
Nov 22 '18 at 5:32
add a comment |
Just use the standard Python exponentiation operator **:
K**x
For example, if you have:
x = np.array([1,2,3])
K = 3
print(K**x)
The output is:
[ 3 9 27]
Notes
For Python classes, the behavior of the binary ** operator is implemented via the __pow__, __rpow__, and __ipow__ magic methods (the reality for np.ndarray is slightly more complicated since it's implemented in the C layer, but that's not actually important here). For Numpy arrays, these magic methods in turn appear to call numpy.power, so you can expect that ** will have the same behavior as documented for numpy.power. In particular,
Note that an integer type raised to a negative integer power will raise a ValueError.
answered Nov 22 '18 at 5:07
teltel
7,41621431
Just use the standard Python exponentiation operator **:
K**x
For example, if you have:
x = np.array([1,2,3])
K = 3
print(K**x)
The output is:
[ 3 9 27]
Notes
For Python classes, the behavior of the binary ** operator is implemented via the __pow__, __rpow__, and __ipow__ magic methods (the reality for np.ndarray is slightly more complicated since it's implemented in the C layer, but that's not actually important here). For Numpy arrays, these magic methods in turn appear to call numpy.power, so you can expect that ** will have the same behavior as documented for numpy.power. In particular,
Note that an integer type raised to a negative integer power will raise a ValueError.
answered Nov 22 '18 at 5:07
teltel
7,41621431
edited Nov 22 '18 at 5:37
answered Nov 22 '18 at 5:07
teltel
7,41621431
answered Nov 22 '18 at 5:07
teltel
7,41621431
answered Nov 22 '18 at 5:07
teltel
7,41621431
7,41621431
small note: castKto a float or else negative numbers in the array will throw an error.
– Vj-
Nov 22 '18 at 5:14
@Vj- I added a note about the implementation of**, including where it mentions that particular error in the documentation
– tel
Nov 22 '18 at 5:32
add a comment |
small note: castKto a float or else negative numbers in the array will throw an error.
– Vj-
Nov 22 '18 at 5:14
@Vj- I added a note about the implementation of**, including where it mentions that particular error in the documentation
– tel
Nov 22 '18 at 5:32
small note: cast
K to a float or else negative numbers in the array will throw an error.– Vj-
Nov 22 '18 at 5:14
small note: cast
K to a float or else negative numbers in the array will throw an error.– Vj-
Nov 22 '18 at 5:14
@Vj- I added a note about the implementation of
**, including where it mentions that particular error in the documentation– tel
Nov 22 '18 at 5:32
@Vj- I added a note about the implementation of
**, including where it mentions that particular error in the documentation– tel
Nov 22 '18 at 5:32
add a comment |
With numpy you can just use numpy.power
arr = numpy.array([1,2,3])
print(numpy.power(3,arr)) # Outputs [ 3 9 27]
answered Nov 22 '18 at 5:11
b-fgb-fg
1,95911522
add a comment |
With numpy you can just use numpy.power
arr = numpy.array([1,2,3])
print(numpy.power(3,arr)) # Outputs [ 3 9 27]
answered Nov 22 '18 at 5:11
b-fgb-fg
1,95911522
add a comment |
With numpy you can just use numpy.power
arr = numpy.array([1,2,3])
print(numpy.power(3,arr)) # Outputs [ 3 9 27]
answered Nov 22 '18 at 5:11
b-fgb-fg
1,95911522
With numpy you can just use numpy.power
arr = numpy.array([1,2,3])
print(numpy.power(3,arr)) # Outputs [ 3 9 27]
answered Nov 22 '18 at 5:11
b-fgb-fg
1,95911522
answered Nov 22 '18 at 5:11
b-fgb-fg
1,95911522
answered Nov 22 '18 at 5:11
b-fgb-fg
1,95911522
answered Nov 22 '18 at 5:11
b-fgb-fg
1,95911522
1,95911522
add a comment |
add a comment |
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@tel: The "piecewise" seems to make things more confusing rather than less.
– user2357112
Nov 22 '18 at 8:24