code: public double condition() {
          if (!full) {
              throw new IllegalStateException("This is not a FULL singular value decomposition.");
          }
  
          return (s[0] <= 0.0 || s[n - 1] <= 0.0) ? Double.POSITIVE_INFINITY : s[0] / s[n - 1];
      }
docs: Returns the L<sub>2</sub> norm condition number, which is max(S) / min(S).
  A system of equations is considered to be well-conditioned if a small
  change in the coefficient matrix or a small change in the right hand
  side results in a small change in the solution vector. Otherwise, it is
  called ill-conditioned. Condition number is defined as the product of
  the norm of A and the norm of A<sup>-1</sup>. If we use the usual
  L<sub>2</sub> norm on vectors and the associated matrix norm, then the
  condition number is the ratio of the largest singular value of matrix
  A to the smallest. Condition number depends on the underlying norm.
  However, regardless of the norm, it is always greater or equal to 1.
  If it is close to one, the matrix is well conditioned. If the condition
  number is large, then the matrix is said to be ill-conditioned. A matrix
  that is not invertible has the condition number equal to infinity.