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[SPARK-8140] [MLlib] Remove empty model check in StreamingLinearAlgorithm #6684

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[SPARK-8140] [MLlib] Remove empty model check in StreamingLinearAlgorithm #6684

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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
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Expand Up @@ -179,7 +179,7 @@ object GradientDescent extends Logging {
* if it's L2 updater; for L1 updater, the same logic is followed.
*/
var regVal = updater.compute(
weights, Vectors.dense(new Array[Double](weights.size)), 0, 1, regParam)._2
weights, Vectors.zeros(weights.size), 0, 1, regParam)._2

for (i <- 1 to numIterations) {
val bcWeights = data.context.broadcast(weights)
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6 changes: 3 additions & 3 deletions mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
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Expand Up @@ -195,11 +195,11 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
*/
val initialWeights = {
if (numOfLinearPredictor == 1) {
Vectors.dense(new Array[Double](numFeatures))
Vectors.zeros(numFeatures)
} else if (addIntercept) {
Vectors.dense(new Array[Double]((numFeatures + 1) * numOfLinearPredictor))
Vectors.zeros((numFeatures + 1) * numOfLinearPredictor)
} else {
Vectors.dense(new Array[Double](numFeatures * numOfLinearPredictor))
Vectors.zeros(numFeatures * numOfLinearPredictor)
}
}
run(input, initialWeights)
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3 changes: 0 additions & 3 deletions mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
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Expand Up @@ -87,9 +87,6 @@ abstract class StreamingLinearAlgorithm[
model match {
case Some(m) =>
m.weights
case None =>
val numFeatures = rdd.first().features.size
Vectors.dense(numFeatures)
}
model = Some(algorithm.run(rdd, initialWeights))
logInfo("Model updated at time %s".format(time.toString))
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2 changes: 1 addition & 1 deletion ...b/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
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Expand Up @@ -79,7 +79,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
this
}

/** Set the initial weights. Default: [0.0, 0.0]. */
/** Set the initial weights. */
def setInitialWeights(initialWeights: Vector): this.type = {
this.model = Some(algorithm.createModel(initialWeights, 0.0))
this
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