Github user freeman-lab commented on a diff in the pull request:
https://github.com/apache/spark/pull/1361#discussion_r15684474
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegression.scala
---
@@ -0,0 +1,104 @@
+/*
+ * 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.regression
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.annotation.Experimental
+
+/**
+ * Train or predict a linear 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 LinearRegressionWithSGD 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.
+ */
+@Experimental
+class StreamingLinearRegressionWithSGD private (
+ private var stepSize: Double,
+ private var numIterations: Int,
+ private var miniBatchFraction: Double,
+ private var numFeatures: Int)
+ extends StreamingRegression[LinearRegressionModel,
LinearRegressionWithSGD] with Serializable {
+
+ val algorithm = new LinearRegressionWithSGD(stepSize, numIterations,
miniBatchFraction)
+
+ var model = algorithm.createModel(Vectors.dense(new
Array[Double](numFeatures)), 0.0)
+
+}
+
+/**
+ * Top-level methods for calling StreamingLinearRegressionWithSGD.
+ */
+@Experimental
+object StreamingLinearRegressionWithSGD {
+
+ /**
+ * Start a streaming Linear Regression model by setting optimization
parameters.
+ *
+ * @param numIterations Number of iterations of gradient descent to run.
+ * @param stepSize Step size to be used for each iteration of gradient
descent.
+ * @param miniBatchFraction Fraction of data to be used per iteration.
+ * @param numFeatures Number of features per record, must be constant
for all batches of data.
+ */
+ def start(
--- End diff --
Ok, I added setters and do it that way in the example, but kept the static
``start`` method for consistency with the others, can always drop later.
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