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 $\mbox{A}$ with dimensions $N$X$N$. 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 $\mbox{A}$.

Output Format

Print the determinant of $\mbox{A}$.

Sample Input
2
1.1 1.1
1.1 1.1

Sample Output
0.0

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见题面