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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?
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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.
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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) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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