úΞoKiet Lam <ktklam9@gmail.com> Safe-InferedIType that represents the function that can calculate the jacobian matrix 1 of the residue with respect to each parameter BType that represents the function that can calculate the residues AEvolves the parameter x for f(x) = sum-square(e(x)) so that f(x)  will be minimized, where: $f = real-valued error function, ) e(x) = {e1(x),e2(x),..,eN(x)}, where ' e1(x) = the residue at the vector x ?NOTE: eN(x) is usually represented as (sample - hypothesis(x)) >e.g.: In training neural networks, hypothesis(x) would be the  network'5s output for a training set, and sample would be the 5 expected output for that training set MNOTE: The dampening constant(lambda) should be set to 0.01 and the dampening 4 update value (beta) should be set to be 10 %Multi-dimensional function that will  return a vector of residues )The function that calculate the Jacobian * matrix of each residue with respect to  each parameter $The initial guess for the parameter 7Dampening constant (usually lambda in most literature) 9Dampening update value (usually beta in most literature) The precision desired The maximum iteration Returns the optimal parameter  and the matrix path HaskellLM-0.1.0Math.LevenbergMarquardtJacobianFunction lmMinimize