| 2 | import numpy |
| 3 | |
| 4 | def LUDecompose (table): |
| 5 | # Table that contains our data |
| 6 | # Table has to be a square array so we need to check first |
| 7 | rows,columns=numpy.shape(table) |
| 8 | L=numpy.zeros((rows,columns)) |
| 9 | U=numpy.zeros((rows,columns)) |
| 10 | if rows!=columns: |
| 11 | return [] |
| 12 | for i in range (columns): |
| 13 | for j in range(i-1): |
| 14 | sum=0 |
| 15 | for k in range (j-1): |
| 16 | sum+=L[i][k]*U[k][j] |
| 17 | L[i][j]=(table[i][j]-sum)/U[j][j] |
| 18 | L[i][i]=1 |
| 19 | for j in range(i-1,columns): |
| 20 | sum1=0 |
| 21 | for k in range(i-1): |
| 22 | sum1+=L[i][k]*U[k][j] |
| 23 | U[i][j]=table[i][j]-sum1 |
| 24 | return L,U |
| 25 | |
| 26 | if __name__ == "__main__": |
| 27 | matrix =numpy.array([[2,-2,1], |