@ -73,10 +73,10 @@ Routine{
~dataset.getPoint("point-7",~query_buf);
~dataset.getPoint("point-7",~query_buf);
// find the 2 points with the shortest distances in the dataset
// find the 2 points with the shortest distances in the dataset
~tree = FluidKDTree.new(s);
~tree = FluidKDTree.new(s,numNeighbours:2 );
~tree.fit(~dataset)
~tree.fit(~dataset)
~tree.kNearest(~query_buf,2, {|x| ("Labels:" + x).postln});
~tree.kNearest(~query_buf, {|x| ("Labels:" + x).postln});
~tree.kNearestDist(~query_buf,2, {|x| ("Distances:" + x).postln});
~tree.kNearestDist(~query_buf, {|x| ("Distances:" + x).postln});
// its nearest neighbourg is itself: it should be itself and the distance should be 0. The second point is depending on your input dataset.
// its nearest neighbourg is itself: it should be itself and the distance should be 0. The second point is depending on your input dataset.
// normalise that point (~query_buf) to be at the right scale
// normalise that point (~query_buf) to be at the right scale
@ -85,10 +85,10 @@ Routine{
~normbuf.getn(0,~nb_of_dim,{arg vec;vec.postln;});
~normbuf.getn(0,~nb_of_dim,{arg vec;vec.postln;});
// make a tree of the normalized database and query with the normalize buffer
// make a tree of the normalized database and query with the normalize buffer
~normtree = FluidKDTree.new(s);
~normtree = FluidKDTree.new(s,numNeighbours:2 );
~normtree.fit(~normed_dataset)
~normtree.fit(~normed_dataset)
~normtree.kNearest(~normbuf,2, {|x| ("Labels:" + x).postln});
~normtree.kNearest(~normbuf, {|x| ("Labels:" + x).postln});
~normtree.kNearestDist(~normbuf,2, {|x| ("Distances:" + x).postln});
~normtree.kNearestDist(~normbuf, {|x| ("Distances:" + x).postln});
// its nearest neighbourg is still itself as it should be, but the 2nd neighbourg will probably have changed. The distance is now different too
// its nearest neighbourg is still itself as it should be, but the 2nd neighbourg will probably have changed. The distance is now different too
// standardize that same point (~query_buf) to be at the right scale
// standardize that same point (~query_buf) to be at the right scale
@ -97,10 +97,10 @@ Routine{
~stdbuf.getn(0,~nb_of_dim,{arg vec;vec.postln;});
~stdbuf.getn(0,~nb_of_dim,{arg vec;vec.postln;});
// make a tree of the standardized database and query with the normalize buffer
// make a tree of the standardized database and query with the normalize buffer
~stdtree = FluidKDTree.new(s);
~stdtree = FluidKDTree.new(s, numNeighbours: 2 );
~stdtree.fit(~standardized_dataset)
~stdtree.fit(~standardized_dataset)
~stdtree.kNearest(~stdbuf,2, {|x| ("Labels:" + x).postln});
~stdtree.kNearest(~stdbuf, {|x| ("Labels:" + x).postln});
~stdtree.kNearestDist(~stdbuf,2, {|x| ("Distances:" + x).postln});
~stdtree.kNearestDist(~stdbuf, {|x| ("Distances:" + x).postln});
// its nearest neighbourg is still itself as it should be, but the 2nd neighbourg will probably have changed yet again. The distance is also different too
// its nearest neighbourg is still itself as it should be, but the 2nd neighbourg will probably have changed yet again. The distance is also different too
// where it starts to be interesting is when we query points that are not in our original dataset
// where it starts to be interesting is when we query points that are not in our original dataset
@ -113,6 +113,7 @@ Routine{
~standardize.transformPoint(~query_buf,~stdbuf);
~standardize.transformPoint(~query_buf,~stdbuf);
//query the single nearest neighbourg via 3 different data scaling. Depending on the random source at the begining, you will get small to large differences between the 3 answers!
//query the single nearest neighbourg via 3 different data scaling. Depending on the random source at the begining, you will get small to large differences between the 3 answers!
~tree.kNearest(~query_buf,1, {|x| ("Original:" + x).post;~tree.kNearestDist(~query_buf,1, {|x| (" with a distance of " + x).postln});});
[~tree,~normtree,~stdtree].do{|t| t.numNeighbours =1 };
~normtree.kNearest(~normbuf,1, {|x| ("Normalized:" + x).post;~normtree.kNearestDist(~normbuf,1, {|x| (" with a distance of " + x).postln});});
~tree.kNearest(~query_buf, {|x| ("Original:" + x).post;~tree.kNearestDist(~query_buf,1, {|x| (" with a distance of " + x).postln});});
~stdtree.kNearest(~stdbuf,1, {|x| ("Standardized:" + x).post; ~stdtree.kNearestDist(~stdbuf,1, {|x| (" with a distance of " + x).postln});});
~normtree.kNearest(~normbuf, {|x| ("Normalized:" + x).post;~normtree.kNearestDist(~normbuf,1, {|x| (" with a distance of " + x).postln});});
~stdtree.kNearest(~stdbuf, {|x| ("Standardized:" + x).post; ~stdtree.kNearestDist(~stdbuf,1, {|x| (" with a distance of " + x).postln});});
Owen Green
6 years ago