Reputation: 31567
I have an array x
and I want to apply a function f
to every item in the matrix that meets some condition. Does Numpy offer a mechanism to make this easy?
Here's an example. My matrix x
is supposed to contain only elements in the exclusive range (0, 1)
. However, due to rounding errors, some elements can be equal to 0
or 1
. For every element in x
that is exactly 0
I want to add epsilon
and for every element that is exactly 1
I want to subtract epsilon
.
Edit: (This edit was made after I had accepted askewchan's answer.) Another way to do this is to use numpy.clip
.
Upvotes: 5
Views: 2998
Reputation: 83
Sorry this isn't more concrete, but you could create a Boolean array that has a TRUE value for every position that meets your condition and FALSE for those that don't.
For something like [0, 1, 0, 0] when testing for 1 you will get an array [FALSE, TRUE, FALSE, FALSE]. In which case you can do [0, 1, 0, 0] - (epsilon)[FALSE, TRUE, FALSE, FALSE] and leave the 0 values unaffected.
Upvotes: 3
Reputation: 46530
You can do this:
a = np.array([0,.1,.5,1])
epsilon = 1e-5
a[a==0] += epsilon
a[a==1] += -epsilon
The reason this works is that a==0
returns a boolean array, just like what Валера Горбунов referred to in their answer:
In : a==0
Out: array([True, False, False, False], dtype=bool)
Then you're using that array as an index to a
, which exposes the elements where True
but not where False
. There's a lot that you can do with this, see http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
Upvotes: 9
Reputation: 152
You can use map()
as documented at http://docs.python.org/2/tutorial/datastructures.html#functional-programming-tools:
def applyEpsilon(value):
myEpsilon = 0.001
if value == 0:
return myEpsilon
elif value == 1:
return 1-myEpsilon
return value
inputList = [0, 0.25, 0.5, 0.75, 0.99, 1]
print map(applyEpsilon, inputList)
Yields:
[0.001, 0.25, 0.5, 0.75, 0.99, 0.999]
Upvotes: 2