nxnxn matrix python Implementation Step by Step

We can implement nxnxn matrix python with numpy module. We will require numpy.array() , numpy.reshape() and numpy.arange() function for this. In this article, We will see the implementation for nxnxn matrix stepwise with explanations and outputs of the respective codes.

nxnxn matrix python Implementation –

Firstly we will import the numpy package . This numpy package provides good interface to create and manipulate arrays.

Step 1 : ( import) –

import numpy as np

Step 2: Implementing Base nxnxn matrix –

Here we will choose any value for n. After this we will use

1. list of numbers for [0,n] using np.arange(n)
2. Using the above list, we will create array nxn dimension using np.array() function.
3. Again we will use np.array() to create nxnxn matrix.

Here is the code for the same.

n = 2
ele = np.arange(n)
arr = np.array([ele]*n)
matrix = np.array([arr]*n)
print(matrix)

Step 3 : Adding Business Context to nxnxn Matrix ( Optional ) –

This is optional step we can write any logic on the basis of our requirement on business context. Here we firstly

1. reshape the above matrix (base one ) using reshape method as mention in below code.
2. We will create a rotatory base matrix with any random logic.
3. Then will create the dot product for the same with base matrix.
4. Finally, we will reshape using reshape() function from numpy module to form nxnxn matrix.

Lastly we will print the final_matrix to understand the complete implementation with examples.

Here is the code for the above mention steps.

flat_matrix = matrix.reshape((int(matrix.size/n),n))
rotatatory_base = np.eye(n) + 3
final_matrix = np.array([rotatatory_base.dot(x) for x in flat_matrix])
final_matrix=final_matrix.reshape((n,n,n))
print(final_matrix)

As the output is 2x2x2 final matrix we get from above implementation.

Other Examples ( Tweaking Configuration )-

Lets consider n=4 in the above script to generate 4x4x4 Matrix with same logic.

import numpy as np
# Choose any value for n
n = 4
ele = np.arange(n)
arr = np.array([ele]*n)
matrix = np.array([arr]*n)
flat_matrix = matrix.reshape((int(matrix.size/n),n))
rotatatory_base = np.eye(n) + 3
final_matrix = np.array([rotatatory_base.dot(x) for x in flat_matrix])
final_matrix=final_matrix.reshape((n,n,n))
print(final_matrix)

Here you can observe that we are getting 4x4x4 matrix as the output. Basically there are four 4×4 matrix that is why it is 4x4x4 or nxnxn matrix.

Conclusion

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Thanks

Data Science Learner Team