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NumPy: Evaluate Einstein’s summation convention of two given multidimensional arrays

NumPy: Linear Algebra Exercise-5 with Solution

Write a NumPy program to evaluate Einstein’s summation convention of two given multidimensional arrays.

Note: In mathematics, especially in applications of linear algebra to physics, the Einstein notation or Einstein summation convention is a notational convention that implies summation over a set of indexed terms in a formula, thus achieving notational brevity.

Sample Solution :

Python Code :

import numpy as np
a = np.array([1,2,3])
b = np.array([0,1,0])
print("Original 1-d arrays:")
print(a)
print(b)
result =  np.einsum("n,n", a, b)
print("Einstein’s summation convention of the said arrays:")
print(result)
x = np.arange(9).reshape(3, 3)
y = np.arange(3, 12).reshape(3, 3)
print("Original Higher dimension:")
print(x)
print(y)
result = np.einsum("mk,kn", x, y)
print("Einstein’s summation convention of the said arrays:")
print(result)

Sample Output:

Original 1-d arrays:
[1 2 3]
[0 1 0]
Einstein’s summation convention of the said arrays:
2
Original Higher dimension:
[[0 1 2]
 [3 4 5]
 [6 7 8]]
[[ 3  4  5]
 [ 6  7  8]
 [ 9 10 11]]
Einstein’s summation convention of the said arrays:
[[ 24  27  30]
 [ 78  90 102]
 [132 153 174]]

Python Code Editor:


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Next: Write a NumPy program to compute the inner product of vectors for 1-D arrays (without complex conjugation) and in higher dimension.

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Python: Tips of the Day

Python: The Zip() Function

>>> students = ('John', 'Mary', 'Mike')
>>> ages = (15, 17, 16)
>>> scores = (90, 88, 82, 17, 14)
>>> for student, age, score in zip(students, ages, scores):
...     print(f'{student}, age: {age}, score: {score}')
... 
John, age: 15, score: 90
Mary, age: 17, score: 88
Mike, age: 16, score: 82
>>> zipped = zip(students, ages, scores)
>>> a, b, c = zip(*zipped)
>>> print(b)
(15, 17, 16)