[ 
https://issues.apache.org/jira/browse/SOLR-11863?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Joel Bernstein updated SOLR-11863:
----------------------------------
    Description: 
let(a=random(collection1, q="*:*", rows=10000, fl="response_d, filesize_d, 
service_d"),
 filesizes=col(a, filesize_d),
 responses=col(a, response_d),
 serviceLevels=col(a, service_d),
 observations=transpose(matrix(filesizes, responses)),
 g=knnRegress(observations, serviceLevels),
 h=predict(g, array(36365, 645)))

 

let(a=random(collection1, q="*:*", rows=10000, fl="response_d, filesize_d, 
service_d"),
 filesizes=col(a, filesize_d),
 responses=col(a, response_d),
 serviceLevels=col(a, service_d),
 observations=transpose(matrix(filesizes, responses)),
 g=knnRegress(observations, serviceLevels),
 h=predict(g, array(36365, 645)))

  was:
let(a=random(collection1, q="*:*", rows=10000, fl="response_d, filesize_d, 
service_d"),
 filesizes=col(a, filesize_d),
 responses=col(a, response_d),
 serviceLevels=col(a, service_d),
 observations=transpose(matrix(filesizes, responses)),
 g=knnRegress(observations, serviceLevels),
 h=predict(g, array(36365, 645)))


> Add knnRegress Stream Evaluator to support nearest neighbor regression
> ----------------------------------------------------------------------
>
>                 Key: SOLR-11863
>                 URL: https://issues.apache.org/jira/browse/SOLR-11863
>             Project: Solr
>          Issue Type: New Feature
>      Security Level: Public(Default Security Level. Issues are Public) 
>         Environment: This ticket adds the knnRegress Stream Evaluator to add 
> support for nearest neighbor regression to the Streaming Expressions machine 
> learning library.
>            Reporter: Joel Bernstein
>            Assignee: Joel Bernstein
>            Priority: Major
>             Fix For: master (8.0), 7.5
>
>         Attachments: SOLR-11863.patch, SOLR-11863.patch
>
>
> let(a=random(collection1, q="*:*", rows=10000, fl="response_d, filesize_d, 
> service_d"),
>  filesizes=col(a, filesize_d),
>  responses=col(a, response_d),
>  serviceLevels=col(a, service_d),
>  observations=transpose(matrix(filesizes, responses)),
>  g=knnRegress(observations, serviceLevels),
>  h=predict(g, array(36365, 645)))
>  
> let(a=random(collection1, q="*:*", rows=10000, fl="response_d, filesize_d, 
> service_d"),
>  filesizes=col(a, filesize_d),
>  responses=col(a, response_d),
>  serviceLevels=col(a, service_d),
>  observations=transpose(matrix(filesizes, responses)),
>  g=knnRegress(observations, serviceLevels),
>  h=predict(g, array(36365, 645)))



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