Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4306#discussion_r23942029
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
---
@@ -0,0 +1,97 @@
+/*
+ * 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.mllib.classification
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.regression.StreamingLinearAlgorithm
+
+/**
+ * Train or predict a logistic regression model on streaming data.
Training uses
+ * Stochastic Gradient Descent to update the model based on each new batch
of
+ * incoming data from a DStream (see `LogisticRegressionWithSGD` for model
equation)
+ *
+ * Each batch of data is assumed to be an RDD of LabeledPoints.
+ * The number of data points per batch can vary, but the number
+ * of features must be constant. An initial weight
+ * vector must be provided.
+ *
+ * Use a builder pattern to construct a streaming logistic regression
+ * analysis in an application, like:
+ *
+ * val model = new StreamingLogisticRegressionWithSGD()
+ * .setStepSize(0.5)
+ * .setNumIterations(10)
+ * .setInitialWeights(Vectors.dense(...))
+ * .trainOn(DStream)
+ *
+ */
+@Experimental
+class StreamingLogisticRegressionWithSGD (
+ private var stepSize: Double,
+ private var numIterations: Int,
+ private var miniBatchFraction: Double,
+ private var regParam: Double,
+ private var initialWeights: Vector)
+ extends StreamingLinearAlgorithm[
--- End diff --
~~~
extends StreamingLinearAlgorithm[LogisticRegressionModel,
LogisticRegressionWithSGD]
with Serializable {
~~~
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