Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/10639#discussion_r49144794
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala
---
@@ -0,0 +1,99 @@
+/*
+ * 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.mllib.linalg.BLAS._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * Model fitted by [[IterativelyReweightedLeastSquares]].
+ * @param coefficients model coefficients
+ * @param intercept model intercept
+ */
+private[ml] class IterativelyReweightedLeastSquaresModel(
+ val coefficients: DenseVector,
+ val intercept: Double) extends Serializable
+
+/**
+ * Fits a generalized linear model (GLM) for a given family using
+ * iteratively reweighted least squares (IRLS).
+ */
+private[ml] class IterativelyReweightedLeastSquares(
+ val family: Family,
+ val fitIntercept: Boolean,
+ val regParam: Double,
+ val standardizeFeatures: Boolean,
+ val standardizeLabel: Boolean,
+ val maxIter: Int,
+ val tol: Double) extends Logging with Serializable {
+
+ def fit(instances: RDD[Instance]):
IterativelyReweightedLeastSquaresModel = {
+
+ val y = instances.map(_.label).persist(StorageLevel.MEMORY_AND_DISK)
+ val yMean = y.reduce(_ + _) / y.count()
+ var mu = y.map { yi => family.startingMu(yi, yMean) }
+ var eta = mu.map { mu => family.link.link(mu) }
--- End diff --
Pre-computing `eta` here seems unnecessary since it is re-assigned within
the while loop before it is used.
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