Fitting plane to points - How do I know if it's correct?

Question

I am busy working on a point cloud simplification algorithm.

Below is the code to calculate the normal to a neighbourhood of points . I have used principal component analysis.

I would like to know if i have computed the normals and curvature of point correctly?

I am aware the normals are unorientated and my next step is to create a graph using network x (with edges from each point to its nearest neighbours), weighting each edge with 1-|ni*ni+1| . Then create a minimum spanning tree and then initiate depth searching by starting at the highest z value point and flip orientation of ni+1 if ni*ni+1=-1 . I will be using networkx , so that i can achieve a global consistent orientation. Is this the best way to go about it?

Thanks

import numpy as np


#NEIGHBOURING POINTS
XYZ = np.array([[-0.0369122 ,  0.12751199 , 0.00276757],
 [-0.0398624 ,  0.128204  ,  0.00299348],
 [-0.0328896  , 0.12613    , 0.00300653],
 [-0.0396095 ,  0.12667701 ,-0.00334699],
 [-0.0331765,   0.12681  ,   0.00839958],
 [-0.0400911   ,0.128618  ,  0.00909496],
 [-0.0328901  , 0.124518 ,  -0.00282055]])


#GET THE COVARIANCE MATRIX
average=sum(XYZ)/XYZ.shape[0]
b = np.transpose(XYZ - average)
cov=np.cov(b)

#GET EIGEN VALUES AND EIGEN VEECTORS
e_val,e_vect = np.linalg.eigh(cov)

print e_val

print 'eigenvectors'
print e_vect


#DIAGONLIZE EIGENVALUES
print 'eigenvalues'
e_val_d = np.diag(e_val)
print e_val_d 




#FIND MIN EIGEN VALUE
h = np.rank(min(e_val))

#FIND NORMAL
norm =  e_vect[:,h]
print 'normal'
print norm

#CALCULATE CURVATURE
curvature = e_val[0]/(e_val[0]+e_val[1]+e_val[2])
print 'curvature'
print curvature