Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/10639#discussion_r50390974
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala
---
@@ -0,0 +1,101 @@
+/*
+ * 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.optim
+
+import org.apache.spark.Logging
+import org.apache.spark.ml.feature.Instance
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.rdd.RDD
+
+/**
+ * Model fitted by [[IterativelyReweightedLeastSquares]].
+ * @param coefficients model coefficients
+ * @param intercept model intercept
+ */
+private[ml] class IterativelyReweightedLeastSquaresModel(
+ val coefficients: DenseVector,
+ val intercept: Double) extends Serializable
+
+/**
+ * Implements the method of iteratively reweighted least squares (IRLS)
which is used to solve
+ * certain optimization problems by an iterative method. In each step of
the iterations, it
+ * involves solving a weighted lease squares (WLS) problem by
[[WeightedLeastSquares]].
+ * It can be used to find maximum likelihood estimates of a generalized
linear model (GLM),
+ * find M-estimator in robust regression and some other optimization
problems.
+ *
+ * @param initialModel the initial guess model.
+ * @param reweightFunc the reweight function which is used to update
offsets and weights
+ * at each iteration.
+ * @param fitIntercept whether to fit intercept.
+ * @param regParam L2 regularization parameter used by WLS.
+ * @param maxIter maximum number of iterations.
+ * @param tol the convergence tolerance.
+ */
+private[ml] class IterativelyReweightedLeastSquares(
+ val initialModel: WeightedLeastSquaresModel,
+ val reweightFunc: (Instance, WeightedLeastSquaresModel) => (Double,
Double),
+ val fitIntercept: Boolean,
+ val regParam: Double,
+ val maxIter: Int,
+ val tol: Double) extends Logging with Serializable {
+
+ def fit(instances: RDD[Instance]):
IterativelyReweightedLeastSquaresModel = {
+
+ var converged = false
+ var iter = 0
+
+ var offsetsAndWeights: RDD[(Double, Double)] = null
--- End diff --
R glm has argument named ```offset```, but ```offsetsAndWeights``` is
```private```. I hope it won't confuse users, or should we rename to other
better one?
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