scipy - using python to do 3-D surface fitting -


i can use module(scipy.optimize.least_squares) 1-d curve fitting(of course,i can use curve_fit module directly) , this

def f(par,data,obs):     return par[0]*data+par[1]-obs def get_f(x,a,b):     return x*a+b data = np.linspace(0, 50, 100) obs = get_f(data,3.2,2.3)  par = np.array([1.0, 1.0]) res_lsq = least_squares(f, par, args=(data, obs)) print res_lsq.x

i can right fitting parameter (3.2,2.3),but when generalize method multi-dimension,like this

def f(par,data,obs): return par[0]*data[0,:]+par[1]*data[1,:]-obs def get_f(x,a,b):     return x[0]*a+b*x[1]  data = np.asarray((np.linspace(0, 50, 100),(np.linspace(0, 50, 100)) ) ) obs = get_f(data,1.,1.)  par = np.array([3.0, 5.0]) res_lsq = least_squares(f, par, args=(data, obs)) print res_lsq.x

i find can not right answer, i.e (1.,1.)，i have no idea whether have made mistake.

the way generate data , observations in "multi-dimensional" case results in get_f returning (a+b)*x[0] (input values x[0], x[1] same) and, similarly, f returning (par[0]+par[1])*data[0]-obs. of course, a=1 , b=1, exact same obs generated other values a, b such a+b=1. scipy correctly returns 1 of (infinite) possible values satisfying constraint, depending on initial estimate.


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