Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/6756#discussion_r33804988
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
---
@@ -0,0 +1,201 @@
+/*
+ * 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.clustering
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasMaxIter,
HasPredictionCol, HasSeed}
+import org.apache.spark.ml.param.{Param, ParamMap, Params}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans,
KMeansModel => MLlibKMeansModel}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.types.{IntegerType, StructType}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.util.Utils
+
+
+/**
+ * Common params for KMeans and KMeansModel
+ */
+private[clustering] trait KMeansParams
+ extends Params with HasMaxIter with HasFeaturesCol with HasSeed with
HasPredictionCol {
+
+ /**
+ * Set the number of clusters to create (k). Default: 2.
+ * @group param
+ */
+ val k = new Param[Int](this, "k", "number of clusters to create", (x:
Int) => x > 1)
+
+ /** @group getParam */
+ def getK: Int = $(k)
+
+ /**
+ * Param the number of runs of the algorithm to execute in parallel. We
initialize the algorithm
+ * this many times with random starting conditions (configured by the
initialization mode), then
+ * return the best clustering found over any run. Default: 1.
+ * @group param
+ */
+ val runs = new Param[Int](this, "runs", "number of runs of the algorithm
to execute in parallel",
+ (value: Int) => value >= 1)
+
+ /** @group getParam */
+ def getRuns: Int = $(runs)
+
+ /**
+ * Param the distance threshold within which we've consider centers to
have converged.
+ * If all centers move less than this Euclidean distance, we stop
iterating one run.
+ * @group param
+ */
+ val epsilon = new Param[Double](this, "epsilon", "distance threshold")
+
+ /** @group getParam */
+ def getEpsilon: Double = $(epsilon)
+
+ /**
+ * Param for the initialization algorithm. This can be either "random"
to choose random points as
+ * initial cluster centers, or "k-means||" to use a parallel variant of
k-means++
+ * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
+ * @group param
+ */
+ val initMode = new Param[String](this, "initMode", "initialization
algorithm",
+ (value: String) => MLlibKMeans.validateInitializationMode(value))
+
+ /** @group getParam */
+ def getInitializationMode: String = $(initMode)
+
+ /**
+ * Param for the number of steps for the k-means|| initialization mode.
This is an advanced
+ * setting -- the default of 5 is almost always enough. Default: 5.
+ * @group param
+ */
+ val initSteps = new Param[Int](this, "initSteps", "number of steps for
k-means||",
--- End diff --
`IntParam`
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