[ https://issues.apache.org/jira/browse/SOLR-11863?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joel Bernstein updated SOLR-11863: ---------------------------------- Description: This ticket adds the knnRegress Stream Evaluator to add support for nearest neighbor regression to the Streaming Expressions machine learning library. Sample syntax: {code:java} let(sample=random(collection1, q=":", rows=10000, fl="response_d, filesize_d, service_d"), filesizes=col(sample, filesize_d), responses=col(sample, response_d), serviceLevels=col(sample, service_d), observations=transpose(matrix(filesizes, responses)), model=knnRegress(observations, serviceLevels, 10), prediction=predict(model, array(36365, 645))){code} was: This ticket adds the knnRegress Stream Evaluator to add support for nearest neighbor regression to the Streaming Expressions machine learning library. Sample syntax: {code:java} let(sample=random(collection1, q=":", rows=10000, fl="response_d, filesize_d, service_d"), filesizes=col(sample, filesize_d), responses=col(sample, response_d), serviceLevels=col(sample, service_d), observations=transpose(matrix(filesizes, responses)), model=knnRegress(observations, serviceLevels), prediction=predict(model, array(36365, 645))){code} > 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) > Reporter: Joel Bernstein > Assignee: Joel Bernstein > Priority: Major > Fix For: master (8.0), 7.5 > > Attachments: SOLR-11863.patch, SOLR-11863.patch > > > This ticket adds the knnRegress Stream Evaluator to add support for nearest > neighbor regression to the Streaming Expressions machine learning library. > Sample syntax: > {code:java} > let(sample=random(collection1, q=":", rows=10000, fl="response_d, filesize_d, > service_d"), > filesizes=col(sample, filesize_d), > responses=col(sample, response_d), > serviceLevels=col(sample, service_d), > observations=transpose(matrix(filesizes, responses)), > model=knnRegress(observations, serviceLevels, 10), > prediction=predict(model, array(36365, 645))){code} > -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org