Hi everyone,

I'm currently trying to use skleanr.Transform.SimilarityTransform to remove scaling translation and rotation from one set of points to align it to the other.

However, if I centre the sets around the origin first, there seems to be frequently a problem (which doesn't occur if the points are all positives), the output being NaN.

I tried to write a small reproducible code:

In [77]:
#I fixed the seed here for reproducibility but this happens oftennp.random.seed(4) #Two random set of pointsa = np.random.randn(10, 2)b = np.random.randn(10, 2) # Center the points arount the origina -= np.mean(a, axis=0)[np.newaxis, :]b -= np.mean(b, axis=0)[np.newaxis, :] tform = SimilarityTransform()tform.estimate(a, b)tform(a)

Out[77]:

array([[ nan,  nan],
       [ nan,  nan],
       [ nan,  nan],
       [ nan,  nan],
       [ nan,  nan],
       [ nan,  nan],
       [ nan,  nan],
       [ nan,  nan],
       [ nan,  nan],
       [ nan,  nan]])

Note that if I don't centre the point there is no problem:

In [89]:
#I fixed the seed here for reproducibility but this happens often
np.random.seed(4)
 
#Two random set of points
a = np.random.randn(10, 2)
b = np.random.randn(10, 2)
 
# Center the points arount the origin
#a -= np.mean(a, axis=0)[np.newaxis, :]
#b -= np.mean(b, axis=0)[np.newaxis, :]
 
tform = SimilarityTransform()
tform.estimate(a, b)
tform(a)
Out[89]:
array([[ 3.76870886, -0.35152078],
       [ 1.83453334, -1.25080725],
       [ 5.42428044, -4.30088121],
       [ 2.51364241, -1.00154154],
       [ 6.14244682, -2.71511189],
       [ 5.37956586, -0.65190768],
       [ 4.5752074 , -0.19039746],
       [ 1.96968262, -1.99729896],
       [ 1.47865106,  0.59493455],
       [ 5.39473376, -0.31125435]])


Sometime it also tells me that the SVD doesn't converge.

Any idea what is going on?

Thanks,

Jean