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I am trying to implement the nearest neighbor problem in skill, can anyone help me with some examples and tutorials?
basically i am tryint to get the closest point in a list of points for a given x,y
I am using sortcar to do this but it is very slow since there is a huge amount of calculation goes on
So I would like to use any NNP algorithm and would like some pointers,
If you just need the closest point, what's wrong with just running through each element and calculating the distance
closestpoint = car(listofpoints)
shortestdistance = axlDistance(referencepoint closestpoint)
when(axlDistance(referencepoint lid) < shortestdistance
shortestdistance = axlDistance(referencepoint lid)
closestpoint = lid
that should return the closest point and the shortest distance.
In reply to Ejlersen:
there is nothing wrong with your suggestion, that's what i do currently
for each point
i append the axldistance from that point to the nearest point's db (list=cons(axldistance(pt1 pt2) list) list=cons(pt2db list))
then I append that list to another list, then i use sortcar to find the shortest distance and its db
But the number of points is based upon the number of layer sets, for a 6 layer board it is 3 sets, 8 layer is 4 sets and so on
now lets say i got 1000+points in each layer set and have to check each and everyone of them to another set of 1000 so it is a factor of
4.02387260077093773543702433923e+2567 calculations times then number of layer sets
This becomes an huge issue for a big database
I was looking at implementing KD-Tree, i will let you know how it goes
In reply to vramananx:
I used 1000! infact it should be 1000 times 1000
in anycase I am trying to implement kd-tree
I have a way to sort the list of points,
divide them by their median,
then sort them by y value
now i have to figure out a way to add them to a parent->child relation
I am considering using arrays and tables
if you have any ideas please let me know
basically i want this structure
-right child----left and right child
-left child----left and right child
please refer http://en.wikipedia.org/wiki/K-d_tree
I'm sorry but I have no experience in kd tree's and never done anything to handle so much data that computing speed would be an issue, so I have no real experience in recommending arrays versus lists.
Ok, I have no experience with the KD-tree method so would be interested to know how you get on. Your method of solving this problem will depend on your requirement. The time to sort all the points may not be that important if you only need to do it once but, if you have to do it often you're probably on th eright track.
Have you tried this for speed:
nearestObj = cadar(sortcar(mapcar(lambda((objId) list(axlDistance(pt1, objId ->xy), objId)), objs), 'lessp))
Where objs is the list of objects you are measuring to and pt1 is the point.
Dave Elder, Tait Communications
In reply to eDave:
Basically I am trying to probe all the vias depending on their padstack, then foreach type of padstack I need to find the nearest GND via with the same padstack
that being said, there are further more complications like some nets have different ground + I have to ignore whoever passes the minimum requirement
foreach via i am checking the distance and sorting this operation takes a huge amount of time for a large dataset
the kd-tree appeals to me since it looks like i could implement it, I have all the basic things coded, like
a. getting the datasets (xy for all the gnd w.r.to their respective layerset)
b. sort the list
Now for building the tree I am thinking how to implement the following
c. find the median then split them into 2 sets right and left sets based on if the length of the divided list is odd or even
d. then for each left right list form childs and sort them by either x(even) or y(odd) value
I am thinking of using
defstruct(node data datalen depth leftmax leftdata rightmin rightdata)
now the leftdata will have child node but i will add a depth so i do that for the right data similar child operation
I would stop this when i reach ceiling(log(length(data)/log(2))
Now I haven't even thought about finding the NNN using the tree
but as you can see in the node defstruct i might add some data to each structure that way i can skip them using basic checks like if the xCoord of the input is larger than the righmin
please let me know your thoughts
Ok, I can see how this might work (without necessarily following the k-d tree method to the letter).
I created a simple utility that divides a design into 1mm squares. I used this to reduce the number of checks per via. It's very fast with about 8000 vias. There are limitations and flaws with my code but to go further I would be writing the utility for you ;-)
sketching on the back of a napkin, you could try build a list of neighbors, something like this pseudocode:
pin_list_outer = getListOfAllPins();
pin_list_inner = copy( pin_list_outer); // need a deep copy
foreach( pin_db_outer pin_list_outer
foreach( pin_db_inner pin_list_inner
unless( pin_db_outer == pin_list_inner // skip over collisions
if( axlDistance( pin_db_outer.x_coord , pin_db_inner.x_coord) =< yourDesiredFence AND
axlDistance( pin_db_outer.y_coord , pin_db_inner.y_coord) =< yourDesiredFence
neighbor_list = neighbor_array[ pin_db_outer]
cons( pin_db_inner neighbor_list)
neighbor_array[ pin_db_outer] = neighbor_list
winding up with an array, keyed by pin_db , listing all other pin_dbs that are within a square window of desired size
rusty former Cadence guru