Github user Yunni commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15148#discussion_r79639164
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/lsh/LSH.scala ---
    @@ -0,0 +1,270 @@
    +/*
    + * Licensed to the Apache Software Foundation (ASF) under one or more
    + * contributor license agreements.  See the NOTICE file distributed with
    + * this work for additional information regarding copyright ownership.
    + * The ASF licenses this file to You under the Apache License, Version 2.0
    + * (the "License"); you may not use this file except in compliance with
    + * the License.  You may obtain a copy of the License at
    + *
    + *    http://www.apache.org/licenses/LICENSE-2.0
    + *
    + * Unless required by applicable law or agreed to in writing, software
    + * distributed under the License is distributed on an "AS IS" BASIS,
    + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    + * See the License for the specific language governing permissions and
    + * limitations under the License.
    + */
    +
    +package org.apache.spark.ml.lsh
    +
    +import scala.util.Random
    +
    +import org.apache.spark.ml.{Estimator, Model}
    +import org.apache.spark.ml.linalg.{Vector, VectorUDT}
    +import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators}
    +import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
    +import org.apache.spark.sql._
    +import org.apache.spark.sql.expressions.UserDefinedFunction
    +import org.apache.spark.sql.functions._
    +import org.apache.spark.sql.types._
    +
    +/**
    + * Params for [[LSH]].
    + */
    +private[ml] trait LSHParams extends HasInputCol with HasOutputCol {
    +  /**
    +   * Param for output dimension.
    +   *
    +   * @group param
    +   */
    +  final val outputDim: IntParam = new IntParam(this, "outputDim", "output 
dimension",
    +    ParamValidators.gt(0))
    +
    +  /** @group getParam */
    +  final def getOutputDim: Int = $(outputDim)
    +
    +  setDefault(outputDim -> 1)
    +
    +  setDefault(outputCol -> "lsh_output")
    +
    +  /**
    +   * Transform the Schema for LSH
    +   * @param schema The schema of the input dataset without outputCol
    +   * @return A derived schema with outputCol added
    +   */
    +  final def transformLSHSchema(schema: StructType): StructType = {
    +    val outputFields = schema.fields :+
    +      StructField($(outputCol), new VectorUDT, nullable = false)
    +    StructType(outputFields)
    +  }
    +}
    +
    +/**
    + * Model produced by [[LSH]].
    + */
    +abstract class LSHModel[KeyType, T <: LSHModel[KeyType, T]] private[ml]
    +  extends Model[T] with LSHParams {
    +  override def copy(extra: ParamMap): T = defaultCopy(extra)
    +  /**
    +   * :: DeveloperApi ::
    +   *
    +   * The hash function of LSH, mapping a predefined KeyType to a Vector
    +   * @return The mapping of LSH function.
    +   */
    +  protected[this] val hashFunction: KeyType => Vector
    +
    +  /**
    +   * :: DeveloperApi ::
    +   *
    +   * Calculate the distance between two different keys using the distance 
metric corresponding
    +   * to the hashFunction
    +   * @param x One of the point in the metric space
    +   * @param y Another the point in the metric space
    +   * @return The distance between x and y in double
    +   */
    +  protected[ml] def keyDistance(x: KeyType, y: KeyType): Double
    +
    +  /**
    +   * :: DeveloperApi ::
    +   *
    +   * Calculate the distance between two different hash Vectors. By 
default, the distance is the
    +   * minimum distance of two hash values in any dimension.
    +   *
    +   * @param x One of the hash vector
    +   * @param y Another hash vector
    +   * @return The distance between hash vectors x and y in double
    +   */
    +  protected[ml] def hashDistance(x: Vector, y: Vector): Double = {
    +    (x.asBreeze - y.asBreeze).toArray.map(math.abs).min
    --- End diff --
    
    Thanks! Since it's generated by hashing, I am assuming it's a pair of dense 
vector.


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