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Hackerrank Linear Algebra Solution

Hackerrank Linear Algebra Solution

Beeze Aal
Beeze Aal

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.


The linalg.det tool computes the determinant of an array.

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


The 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]]


The 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


You are given a square matrix  with dimensions X. 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 .
The next  lines contains the  space separated elements of array .

Output Format

Print the determinant of .

Sample Input

1.1 1.1
1.1 1.1

Sample Output


Solution in python3

Approach 1.

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

Approach 2.

import numpy
array=[list(map(float,input().split())) for i in range(int(input()))]

Approach 3.

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

Solution in python

Approach 1.

import numpy
print round(numpy.linalg.det([map(float,raw_input().split()) for _ in range(input())]),2)

Approach 2.

import numpy
print round(numpy.linalg.det([ map(float, raw_input().split()) for i in range(int(raw_input()))]),2)

Approach 3.

import numpy
for i in range(n):
print round(numpy.linalg.det(numpy.array(m)),2)