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?
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