convert this matlab code to python code
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
Related questions
Question
Could you convert this matlab code to python code?
![1
SAWNH
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gunpBH 15 16 17 18 19 2012 23 24 25 26 27 28 29 38
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TESTERS O
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% Code for random model
17
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& Solve linear system using built-in gaussian elimination
tic
x_gausseli A\b
toc
random model - Jacobi function
-0
* Compute Condition number to see if matrix is ill-conditioned
% Since Cond (A) << 10^16, the matrix is not ill-conditioned
condn cond(A, inf)
% Solve linear system using Jacobi function
tic
jacobi (A, b, 72)
x_jacobi
toc
* Compute infinity norm
diff
inf_err = max(abs (diff))
% Compute the infinity normed error for all iterations of the Jacobi
(x_gausseli - x_jacobi);
% method.
for i = 1:72
endl
6
x_jacob = jacobi (A,b, i);
diff (x_gausselix_jacob);
X= ['Iteration ,num2str(i), '.'];
disp(X)
inf_err max (abs(diff))
end
function soln = jacobi(A, b, itermax)
& Inputs: A, the n by n coefficient matrix
b, the constant right hand vector
Itermax, the maximum number of iterations desired.
& Outputs: soln, the approximate solution after itrmax iterations.
% Repeat:
* Until: the maximum number of iterations is reached.
n=length(b);
d=diag (A);
r=A-diag(d);
x=zeros(n,1);
% find n
% extract diagonal of a
& r is the remainder
% initialize vector x
for j=1:itermax
x=(b-r*x)./d;
end
End of Jacobi iteration loop
soln = x;
% loop for Jacobi iteration](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa4aac48a-4d20-41e4-aba5-4ec9a336b2fd%2F58430452-b188-4330-a80f-307154f1944f%2F5xhpio3a_processed.png&w=3840&q=75)
Transcribed Image Text:1
SAWNH
2 format long
3
6-
7
8
gunpBH 15 16 17 18 19 2012 23 24 25 26 27 28 29 38
9
10
11-
12
13
14
16-
19 -
20 -
24-
30-
1
2
3
4
5
6
TESTERS O
10
11
12
13
14
15
16
% Code for random model
17
18
& Solve linear system using built-in gaussian elimination
tic
x_gausseli A\b
toc
random model - Jacobi function
-0
* Compute Condition number to see if matrix is ill-conditioned
% Since Cond (A) << 10^16, the matrix is not ill-conditioned
condn cond(A, inf)
% Solve linear system using Jacobi function
tic
jacobi (A, b, 72)
x_jacobi
toc
* Compute infinity norm
diff
inf_err = max(abs (diff))
% Compute the infinity normed error for all iterations of the Jacobi
(x_gausseli - x_jacobi);
% method.
for i = 1:72
endl
6
x_jacob = jacobi (A,b, i);
diff (x_gausselix_jacob);
X= ['Iteration ,num2str(i), '.'];
disp(X)
inf_err max (abs(diff))
end
function soln = jacobi(A, b, itermax)
& Inputs: A, the n by n coefficient matrix
b, the constant right hand vector
Itermax, the maximum number of iterations desired.
& Outputs: soln, the approximate solution after itrmax iterations.
% Repeat:
* Until: the maximum number of iterations is reached.
n=length(b);
d=diag (A);
r=A-diag(d);
x=zeros(n,1);
% find n
% extract diagonal of a
& r is the remainder
% initialize vector x
for j=1:itermax
x=(b-r*x)./d;
end
End of Jacobi iteration loop
soln = x;
% loop for Jacobi iteration
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