The implementation of the null space solver and especially the normalization is indeed not suitable for this special case. I'll quickly fix it in the coming days...

Am 04.12.2013 um 13:16 schrieb "Jean K" <jean.kossaifi@gmail.com>:

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 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[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,

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