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https://issues.apache.org/jira/browse/MADLIB-1059?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16172432#comment-16172432
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Himanshu Pandey commented on MADLIB-1059:
-----------------------------------------
[~fmcquillan],
Currently K-NN uses squared_dist_norm2 function by default to calculate the
distance. So with this functionality, the idea is, it can use any of these
functions to calculate the distance right?
{code}
dist_norm1
dist_norm2
squared_dist_norm2
dist_angle
dist_tanimoto
user defined function with signature DOUBLE PRECISION[] x, DOUBLE PRECISION[] y
-> DOUBLE PRECISION
{code}
Are we going to add an overloaded function with this extra param or making it
optional in the same function?
> Add additional distance metrics for k-NN
> ----------------------------------------
>
> Key: MADLIB-1059
> URL: https://issues.apache.org/jira/browse/MADLIB-1059
> Project: Apache MADlib
> Issue Type: Improvement
> Components: k-NN
> Reporter: Frank McQuillan
> Assignee: Himanshu Pandey
> Labels: starter
> Fix For: v2.0
>
>
> Follow on from https://issues.apache.org/jira/browse/MADLIB-927
> which supports one distance function. This JIRA is to
> (1)
> add additional distance metrics. The model is follow is
> http://madlib.incubator.apache.org/docs/latest/group__grp__kmeans.html
> fn_dist (optional)
> TEXT, default: squared_dist_norm2'. The name of the function to use to
> calculate the distance between data points.
> The following distance functions can be used (computation of barycenter/mean
> in parentheses):
> dist_norm1: 1-norm/Manhattan (element-wise median [Note that MADlib does not
> provide a median aggregate function for support and performance reasons.])
> dist_norm2: 2-norm/Euclidean (element-wise mean)
> squared_dist_norm2: squared Euclidean distance (element-wise mean)
> dist_angle: angle (element-wise mean of normalized points)
> dist_tanimoto: tanimoto (element-wise mean of normalized points [5])
> user defined function with signature DOUBLE PRECISION[] x, DOUBLE PRECISION[]
> y -> DOUBLE PRECISION
> and also check of there are other distance functions under
> http://madlib.apache.org/docs/latest/group__grp__linalg.html
> that might make sense to include while you are at it, in addition to the ones
> listed above
> (2) Add an option for weighted average in the voting.
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