Linear Algebra in Python – HackerRank Solution

Linear Algebra in Python - HackerRank Solution
Linear Algebra in Python – HackerRank Solution

The NumPy module also comes with a number of built-in routines for linear algebra calculations. These can be found in the sub-module linalg. linalg.det The linalg.det tool computes the determinant of an array.

print numpy.linalg.det([[1 , 2], [2, 1]])       #Output : -3.0

linalg.eigThe linalg.eig computes the eigenvalues and right eigenvectors of a square array.

vals, vecs = numpy.linalg.eig([[1 , 2], [2, 1]])
print vals                                      #Output : [ 3. -1.]
print vecs                                      #Output : [[ 0.70710678 -0.70710678]
                                                #          [ 0.70710678  0.70710678]]

linalg.invThe linalg.inv tool computes the (multiplicative) inverse of a matrix.

print numpy.linalg.inv([[1 , 2], [2, 1]])       #Output : [[-0.33333333  0.66666667]
                                                #          [ 0.66666667 -0.33333333]]

 Other routines can be found here

Task :

You are given a square matrix A with dimensions NXN. Your task is to find the determinant. Note: Round the answer to 2 places after the decimal.


Input Format :

The first line contains the integer N.
The next N lines contains the N space separated elements of array A. 

Output Format :

Print the determinant of A.


Sample Input :

2
1.1 1.1
1.1 1.1

Sample Output :

0.0




Linear Algebra in Python – HackerRank Solution

import numpy
N = int(input())
A = numpy.array([input().split() for _ in range(N)], float)
print(round(numpy.linalg.det(A),2))

Disclaimer: The above Problem (Linear Algebra in Python – HackerRank Solution ) is generated by Hackerrank but the Solution is Provided by Chase2Learn. This tutorial is only for Educational and Learning purposes. Authority if any of the queries regarding this post or website fill the following contact form thank you.

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