Hackerrank Linear Algebra Solution
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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 : -3.0The 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
Task
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
2
1.1 1.1
1.1 1.1Sample Output
0.0Solution 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()))]
print(round(numpy.linalg.det(array),2))Approach 3.
import numpy
N = int(input())
A = numpy.array([input().split() for _ in range(N)], float)
print(round(numpy.linalg.det(A),2))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
n=int(raw_input())
m=[]
for i in range(n):
m.append(map(float,raw_input().split()))
print round(numpy.linalg.det(numpy.array(m)),2)
