Description Usage Arguments Details Value Examples
k-Nearest Neighbour Indexes
1 | knnx.index(data, query, k = 1, algorithm = "kd_tree", ...)
|
data |
input FLTable with pool space to search for |
query |
input search FLTable |
k |
number of neighbours considered. |
algorithm |
currently only brute is supported |
... |
additional parameters like dist Flag to get the distances as well. |
For each row of the test set, the k nearest (according to Euclidean distance metric) training set vectors are found and indices,distances are returned.
FLMatrix of indices or a list of dist & index if dist FLag is true.
1 2 3 | FLdeepTbl <- FLTable(getTestTableName("ARknnDevSmall"),"obsid","varid","num_val")
FLknnOutput <- knnx.index(FLdeepTbl,FLdeepTbl,k=2)
FLknnOutput
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.