MammadTavakoli opened a new pull request, #37128: URL: https://github.com/apache/spark/pull/37128
In the spark there is an `LSH `function that use for KNN or search similarity; `BucketedRandomProjectionLSH`. The usage of it is: ``` from pyspark.ml.feature import BucketedRandomProjectionLSH brp = BucketedRandomProjectionLSH( inputCol="features", outputCol="hashes", seed=12345, bucketLength=1.0 ) model = brp.fit(df) model.approxSimilarityJoin(df, df1, 3.0, distCol="EuclideanDistance") ``` But I don't understand what is the usage of fit in this method. In this example `BucketedRandomProjectionLSH` fit with `df` and `approxSimilarityJoin` measures the distance between `df` and `df1`, that `df1` can be `df` too. what happend if we use `df2` (other data frame) inested of `df`, that `BucketedRandomProjectionLSH` fitted with that data frame? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org