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https://issues.apache.org/jira/browse/MADLIB-1059?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan updated MADLIB-1059:
------------------------------------
Description:
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
was:
Follow on from https://issues.apache.org/jira/browse/MADLIB-927
which supports one distance function. This JIRA is to 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
> 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
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