Having looked over the man pages for numpy
's eye
and identity
, I'd assumed that identity
was a special case of eye
, since it has fewer options (e.g. eye
can fill shifted diagonals, identity
cannot), but could plausibly run more quickly. However, this isn't the case on either small or large arrays:
>>> np.identity(3) array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> np.eye(3) array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> timeit.timeit("import numpy; numpy.identity(3)", number = 10000) 0.05699801445007324 >>> timeit.timeit("import numpy; numpy.eye(3)", number = 10000) 0.03787708282470703 >>> timeit.timeit("import numpy", number = 10000) 0.00960087776184082 >>> timeit.timeit("import numpy; numpy.identity(1000)", number = 10000) 11.379066944122314 >>> timeit.timeit("import numpy; numpy.eye(1000)", number = 10000) 11.247124910354614
What, then, is the advantage of using identity
over eye
?
identity returns a square matrix (special case of a 2D-array) which is an identity matrix with the main diagonal (i.e. 'k=0') as 1's and the other values as 0's. you can't change the diagonal k here. np. eye returns a 2D-array, which fills the diagonal, i.e. 'k' which can be set, with 1's and rest with 0's.
identity(n, dtype = None) : Return a identity matrix i.e. a square matrix with ones on the main diagonal. Parameters : n : [int] Dimension n x n of output array dtype : [optional, float(by Default)] Data type of returned array.
matlib. identity() is another function for doing matrix operations in numpy. It returns a square identity matrix of given input size.
NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. The array object in NumPy is called ndarray , it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important.
identity
just calls eye
so there is no difference in how the arrays are constructed. Here's the code for identity
:
def identity(n, dtype=None): from numpy import eye return eye(n, dtype=dtype)
As you say, the main difference is that with eye
the diagonal can may be offset, whereas identity
only fills the main diagonal.
Since the identity matrix is such a common construct in mathematics, it seems the main advantage of using identity
is for its name alone.
To see the difference in an example, run the below codes:
import numpy as np #Creates an array of 4 x 4 with the main diagonal of 1 arr1 = np.eye(4) print(arr1) print("\n") #or you can change the diagonal position arr2 = np.eye(4, k=1) # or try with another number like k= -2 print(arr2) print("\n") #but you can't change the diagonal in identity arr3 = np.identity(4) print(arr3)
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