[SPARK-8053] [MLLIB] renamed scalingVector to scalingVec
I searched the Spark codebase for all occurrences of "scalingVector"
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #6596 from jkbradley/scalingVec-rename and squashes the following commits:
d3812f8 [Joseph K. Bradley] renamed scalingVector to scalingVec
(cherry picked from commit 07c16cb5ba
)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
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@ -41,7 +41,7 @@ class ElementwiseProduct(override val uid: String)
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* the vector to multiply with input vectors
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* @group param
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*/
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val scalingVec: Param[Vector] = new Param(this, "scalingVector", "vector for hadamard product")
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val scalingVec: Param[Vector] = new Param(this, "scalingVec", "vector for hadamard product")
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/** @group setParam */
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def setScalingVec(value: Vector): this.type = set(scalingVec, value)
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@ -25,10 +25,10 @@ import org.apache.spark.mllib.linalg._
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* Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a
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* provided "weight" vector. In other words, it scales each column of the dataset by a scalar
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* multiplier.
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* @param scalingVector The values used to scale the reference vector's individual components.
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* @param scalingVec The values used to scale the reference vector's individual components.
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*/
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@Experimental
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class ElementwiseProduct(val scalingVector: Vector) extends VectorTransformer {
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class ElementwiseProduct(val scalingVec: Vector) extends VectorTransformer {
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/**
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* Does the hadamard product transformation.
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@ -37,15 +37,15 @@ class ElementwiseProduct(val scalingVector: Vector) extends VectorTransformer {
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* @return transformed vector.
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*/
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override def transform(vector: Vector): Vector = {
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require(vector.size == scalingVector.size,
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s"vector sizes do not match: Expected ${scalingVector.size} but found ${vector.size}")
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require(vector.size == scalingVec.size,
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s"vector sizes do not match: Expected ${scalingVec.size} but found ${vector.size}")
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vector match {
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case dv: DenseVector =>
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val values: Array[Double] = dv.values.clone()
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val dim = scalingVector.size
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val dim = scalingVec.size
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var i = 0
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while (i < dim) {
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values(i) *= scalingVector(i)
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values(i) *= scalingVec(i)
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i += 1
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}
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Vectors.dense(values)
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@ -54,7 +54,7 @@ class ElementwiseProduct(val scalingVector: Vector) extends VectorTransformer {
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val dim = values.length
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var i = 0
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while (i < dim) {
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values(i) *= scalingVector(indices(i))
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values(i) *= scalingVec(indices(i))
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i += 1
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}
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Vectors.sparse(size, indices, values)
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