Optimization accuracy is not returned for non-adaptive case

[hkayabilisim]

When I use the non-adaptive case (see example below), the program does not display a measure to evaluate the "goodness" of the calculated optimum point. In the example below, rosenbrock_2d has a global optimum at [1,1] whereas the program returns [0,0]. However, in the output, there is no indication of optimization error. For instance, standard euclidean distance between the true and estimated optimum points can be provided.

$ python src/main.py --numSamples 100 --numVariables 2 --function rosenbrock_2d 

Args:  Namespace(numSamples=100, numVariables=2, function='rosenbrock_2d', min=None, max=None, x0=None, randomInit=False, basisFunction='Cosine', legendreDegree=7, adaptive=False, numClosestPoints=100, epsilon=0.1, clip=0.9, numberOfRuns=1)
is_adaptive: False
x1_min:  -1.9963089700142151
0.16399216651916504 seconds
hdmr_opt status: [ message:  
 success: True
     fun: [[ 1.000e+00]]
       x: [0.0, 0.0]
    nfev: 100
    njev: 0
    nhev: 0]