numpy_linear_algebra.py

"""

Problem Statement

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.eig

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]]
linalg.inv

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 A with dimensions NXN. Your task is to find the determinant.

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

"""
import numpy
N = input()
A = numpy.array([map(float,raw_input().split()) for _ in xrange(N)])
print numpy.linalg.det(A)