Closed vincefernando closed 10 years ago
Thanks for the report! This was caused by the fact that you used duplicate tuples as points (which should be allowed). I've fixed this in 328f6e2449acf0a904fc130908e7f197b7dfdc83 and will release a new version soon.
Thanks for the email. Document says that " point must be a location, not a node" in search_nn. As you seem to agree, except perhaps in trivial cases, the user would not know whether a point is identical to a node (to within machine precision). Perhaps, you should include a test to determine whether a point is a node. I noted that the point is the 12th entry of the tree when I complained but I should have emphasized it.
On 1 April 2014 21:37, Stefan Kögl notifications@github.com wrote:
Thanks for the report! This was caused by the fact that you used duplicate tuples as points (which should be allowed). I've fixed this in 328f6e2https://github.com/stefankoegl/kdtree/commit/328f6e2449acf0a904fc130908e7f197b7dfdc83and will release a new version soon.
Reply to this email directly or view it on GitHubhttps://github.com/stefankoegl/kdtree/issues/13#issuecomment-39255553 .
This wasn't the problem. The problem was caused by the fact that points ((4.9,3.1,1.5,0.1)
) were contained multiple times in the tree. Nevertheless, this is now fixed in version 0.9 which I just released.
Thanks for your corrections. After writing to you, I realised my mistake. At that time I have forgotten that the data set has duplicates.
Unfortunately, I have an array of 199x11 which fails. It has only 138 unique records. I will raise this issue soon after doing more tests. The program seem to work if I remove the duplicates.
I have also tried large data sets (e.g. 10000x16) and they all worked in version 0.9.
On 2 April 2014 18:58, Stefan Kögl notifications@github.com wrote:
This wasn't the problem. The problem was caused by the fact that points ( (4.9,3.1,1.5,0.1)) were contained multiple times in the tree. Nevertheless, this is now fixed in version 0.9 which I just released.
Reply to this email directly or view it on GitHubhttps://github.com/stefankoegl/kdtree/issues/13#issuecomment-39362445 .
import kdtree A=[(5.1,3.5,1.4,0.2), (4.9,3.0,1.4,0.2), (4.7,3.2,1.3,0.2), (4.6,3.1,1.5,0.2), (5.0,3.6,1.4,0.2), (5.4,3.9,1.7,0.4), (4.6,3.4,1.4,0.3), (5.0,3.4,1.5,0.2), (4.4,2.9,1.4,0.2), (4.9,3.1,1.5,0.1), (5.4,3.7,1.5,0.2), (4.8,3.4,1.6,0.2), (4.8,3.0,1.4,0.1), (4.3,3.0,1.1,0.1), (5.8,4.0,1.2,0.2), (5.7,4.4,1.5,0.4), (5.4,3.9,1.3,0.4), (5.1,3.5,1.4,0.3), (5.7,3.8,1.7,0.3), (5.1,3.8,1.5,0.3), (5.4,3.4,1.7,0.2), (5.1,3.7,1.5,0.4), (4.6,3.6,1.0,0.2), (5.1,3.3,1.7,0.5), (4.8,3.4,1.9,0.2), (5.0,3.0,1.6,0.2), (5.0,3.4,1.6,0.4), (5.2,3.5,1.5,0.2), (5.2,3.4,1.4,0.2), (4.7,3.2,1.6,0.2), (4.8,3.1,1.6,0.2), (5.4,3.4,1.5,0.4), (5.2,4.1,1.5,0.1), (5.5,4.2,1.4,0.2), (4.9,3.1,1.5,0.1), (5.0,3.2,1.2,0.2), (5.5,3.5,1.3,0.2), (4.9,3.1,1.5,0.1), (4.4,3.0,1.3,0.2), (5.1,3.4,1.5,0.2), (5.0,3.5,1.3,0.3), (4.5,2.3,1.3,0.3), (4.4,3.2,1.3,0.2), (5.0,3.5,1.6,0.6), (5.1,3.8,1.9,0.4), (4.8,3.0,1.4,0.3), (5.1,3.8,1.6,0.2), (4.6,3.2,1.4,0.2), (5.3,3.7,1.5,0.2), (5.0,3.3,1.4,0.2)]
p is the 12th element of A
p = ( 4.8,3.0,1.4,0.1) T=kdtree.create(A) print T nn=T.search_nn(p) ~