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 :

1.1 1.1
1.1 1.1

Sample Output :


Linear Algebra in Python – HackerRank Solution

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

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