diff options
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala | 21 |
1 files changed, 5 insertions, 16 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala index 8cca926f1c..fe41863bce 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -17,13 +17,12 @@ package org.apache.spark.mllib.regression -import breeze.linalg.{DenseVector => BDV, SparseVector => BSV} - import org.apache.spark.annotation.DeveloperApi import org.apache.spark.{Logging, SparkException} import org.apache.spark.rdd.RDD import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.linalg.{Vectors, Vector} +import org.apache.spark.mllib.util.MLUtils._ /** * :: DeveloperApi :: @@ -124,16 +123,6 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] run(input, initialWeights) } - /** Prepends one to the input vector. */ - private def prependOne(vector: Vector): Vector = { - val vector1 = vector.toBreeze match { - case dv: BDV[Double] => BDV.vertcat(BDV.ones[Double](1), dv) - case sv: BSV[Double] => BSV.vertcat(new BSV[Double](Array(0), Array(1.0), 1), sv) - case v: Any => throw new IllegalArgumentException("Do not support vector type " + v.getClass) - } - Vectors.fromBreeze(vector1) - } - /** * Run the algorithm with the configured parameters on an input RDD * of LabeledPoint entries starting from the initial weights provided. @@ -147,23 +136,23 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] // Prepend an extra variable consisting of all 1.0's for the intercept. val data = if (addIntercept) { - input.map(labeledPoint => (labeledPoint.label, prependOne(labeledPoint.features))) + input.map(labeledPoint => (labeledPoint.label, appendBias(labeledPoint.features))) } else { input.map(labeledPoint => (labeledPoint.label, labeledPoint.features)) } val initialWeightsWithIntercept = if (addIntercept) { - prependOne(initialWeights) + appendBias(initialWeights) } else { initialWeights } val weightsWithIntercept = optimizer.optimize(data, initialWeightsWithIntercept) - val intercept = if (addIntercept) weightsWithIntercept(0) else 0.0 + val intercept = if (addIntercept) weightsWithIntercept(weightsWithIntercept.size - 1) else 0.0 val weights = if (addIntercept) { - Vectors.dense(weightsWithIntercept.toArray.slice(1, weightsWithIntercept.size)) + Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1)) } else { weightsWithIntercept } |