diff --git a/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala b/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala
index a47526d36f1a177578e0af82836c9d4699870ee8..0ea687bbccc547b5ea723a39958b22f1fd39fe64 100644
--- a/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala
+++ b/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala
@@ -69,7 +69,7 @@ sealed trait Matrix extends Serializable {
def rowIter: Iterator[Vector] = this.transpose.colIter
/** Converts to a breeze matrix. */
- private[ml] def toBreeze: BM[Double]
+ private[ml] def asBreeze: BM[Double]
/** Gets the (i, j)-th element. */
@Since("2.0.0")
@@ -112,11 +112,11 @@ sealed trait Matrix extends Serializable {
}
/** A human readable representation of the matrix */
- override def toString: String = toBreeze.toString()
+ override def toString: String = asBreeze.toString()
/** A human readable representation of the matrix with maximum lines and width */
@Since("2.0.0")
- def toString(maxLines: Int, maxLineWidth: Int): String = toBreeze.toString(maxLines, maxLineWidth)
+ def toString(maxLines: Int, maxLineWidth: Int): String = asBreeze.toString(maxLines, maxLineWidth)
/**
* Map the values of this matrix using a function. Generates a new matrix. Performs the
@@ -202,7 +202,7 @@ class DenseMatrix @Since("2.0.0") (
this(numRows, numCols, values, false)
override def equals(o: Any): Boolean = o match {
- case m: Matrix => toBreeze == m.toBreeze
+ case m: Matrix => asBreeze == m.asBreeze
case _ => false
}
@@ -210,7 +210,7 @@ class DenseMatrix @Since("2.0.0") (
Seq(numRows, numCols, toArray).##
}
- private[ml] def toBreeze: BM[Double] = {
+ private[ml] def asBreeze: BM[Double] = {
if (!isTransposed) {
new BDM[Double](numRows, numCols, values)
} else {
@@ -488,14 +488,14 @@ class SparseMatrix @Since("2.0.0") (
rowIndices: Array[Int],
values: Array[Double]) = this(numRows, numCols, colPtrs, rowIndices, values, false)
- override def hashCode(): Int = toBreeze.hashCode()
+ override def hashCode(): Int = asBreeze.hashCode()
override def equals(o: Any): Boolean = o match {
- case m: Matrix => toBreeze == m.toBreeze
+ case m: Matrix => asBreeze == m.asBreeze
case _ => false
}
- private[ml] def toBreeze: BM[Double] = {
+ private[ml] def asBreeze: BM[Double] = {
if (!isTransposed) {
new BSM[Double](values, numRows, numCols, colPtrs, rowIndices)
} else {
diff --git a/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala b/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala
index 59f9c2adba5fc39aaf933887b211a2e35bfeb469..909fec1c0665381de4246e705f226fe0d83068ac 100644
--- a/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala
+++ b/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala
@@ -92,14 +92,14 @@ sealed trait Vector extends Serializable {
/**
* Converts the instance to a breeze vector.
*/
- private[spark] def toBreeze: BV[Double]
+ private[spark] def asBreeze: BV[Double]
/**
* Gets the value of the ith element.
* @param i index
*/
@Since("2.0.0")
- def apply(i: Int): Double = toBreeze(i)
+ def apply(i: Int): Double = asBreeze(i)
/**
* Makes a deep copy of this vector.
@@ -453,7 +453,7 @@ class DenseVector @Since("2.0.0") ( @Since("2.0.0") val values: Array[Double]) e
override def toArray: Array[Double] = values
- private[spark] override def toBreeze: BV[Double] = new BDV[Double](values)
+ private[spark] override def asBreeze: BV[Double] = new BDV[Double](values)
override def apply(i: Int): Double = values(i)
@@ -584,7 +584,7 @@ class SparseVector @Since("2.0.0") (
new SparseVector(size, indices.clone(), values.clone())
}
- private[spark] override def toBreeze: BV[Double] = new BSV[Double](indices, values, size)
+ private[spark] override def asBreeze: BV[Double] = new BSV[Double](indices, values, size)
override def foreachActive(f: (Int, Double) => Unit): Unit = {
var i = 0
diff --git a/mllib-local/src/main/scala/org/apache/spark/ml/stat/distribution/MultivariateGaussian.scala b/mllib-local/src/main/scala/org/apache/spark/ml/stat/distribution/MultivariateGaussian.scala
index 383d6d96e8d7c3ac05299f560b639237f016e8aa..0be28677eff31f93f9c98333f735e55e5f631eed 100644
--- a/mllib-local/src/main/scala/org/apache/spark/ml/stat/distribution/MultivariateGaussian.scala
+++ b/mllib-local/src/main/scala/org/apache/spark/ml/stat/distribution/MultivariateGaussian.scala
@@ -47,7 +47,7 @@ class MultivariateGaussian @Since("2.0.0") (
this(Vectors.fromBreeze(mean), Matrices.fromBreeze(cov))
}
- private val breezeMu = mean.toBreeze.toDenseVector
+ private val breezeMu = mean.asBreeze.toDenseVector
/**
* Compute distribution dependent constants:
@@ -61,7 +61,7 @@ class MultivariateGaussian @Since("2.0.0") (
*/
@Since("2.0.0")
def pdf(x: Vector): Double = {
- pdf(x.toBreeze)
+ pdf(x.asBreeze)
}
/**
@@ -69,7 +69,7 @@ class MultivariateGaussian @Since("2.0.0") (
*/
@Since("2.0.0")
def logpdf(x: Vector): Double = {
- logpdf(x.toBreeze)
+ logpdf(x.asBreeze)
}
/** Returns density of this multivariate Gaussian at given point, x */
@@ -113,7 +113,7 @@ class MultivariateGaussian @Since("2.0.0") (
* relation to the maximum singular value (same tolerance used by, e.g., Octave).
*/
private def calculateCovarianceConstants: (BDM[Double], Double) = {
- val eigSym.EigSym(d, u) = eigSym(cov.toBreeze.toDenseMatrix) // sigma = u * diag(d) * u.t
+ val eigSym.EigSym(d, u) = eigSym(cov.asBreeze.toDenseMatrix) // sigma = u * diag(d) * u.t
// For numerical stability, values are considered to be non-zero only if they exceed tol.
// This prevents any inverted value from exceeding (eps * n * max(d))^-1
diff --git a/mllib-local/src/test/scala/org/apache/spark/ml/linalg/BreezeMatrixConversionSuite.scala b/mllib-local/src/test/scala/org/apache/spark/ml/linalg/BreezeMatrixConversionSuite.scala
index 70a21e41bfbd1f620c551f0de98003c196098aa2..f07ed20cf0e770513828d3337d86ec3b25383257 100644
--- a/mllib-local/src/test/scala/org/apache/spark/ml/linalg/BreezeMatrixConversionSuite.scala
+++ b/mllib-local/src/test/scala/org/apache/spark/ml/linalg/BreezeMatrixConversionSuite.scala
@@ -24,7 +24,7 @@ import org.apache.spark.ml.SparkMLFunSuite
class BreezeMatrixConversionSuite extends SparkMLFunSuite {
test("dense matrix to breeze") {
val mat = Matrices.dense(3, 2, Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0))
- val breeze = mat.toBreeze.asInstanceOf[BDM[Double]]
+ val breeze = mat.asBreeze.asInstanceOf[BDM[Double]]
assert(breeze.rows === mat.numRows)
assert(breeze.cols === mat.numCols)
assert(breeze.data.eq(mat.asInstanceOf[DenseMatrix].values), "should not copy data")
@@ -48,7 +48,7 @@ class BreezeMatrixConversionSuite extends SparkMLFunSuite {
val colPtrs = Array(0, 2, 4)
val rowIndices = Array(1, 2, 1, 2)
val mat = Matrices.sparse(3, 2, colPtrs, rowIndices, values)
- val breeze = mat.toBreeze.asInstanceOf[BSM[Double]]
+ val breeze = mat.asBreeze.asInstanceOf[BSM[Double]]
assert(breeze.rows === mat.numRows)
assert(breeze.cols === mat.numCols)
assert(breeze.data.eq(mat.asInstanceOf[SparseMatrix].values), "should not copy data")
diff --git a/mllib-local/src/test/scala/org/apache/spark/ml/linalg/BreezeVectorConversionSuite.scala b/mllib-local/src/test/scala/org/apache/spark/ml/linalg/BreezeVectorConversionSuite.scala
index 00c9ee79eb1754014ab52af09c72a2717841f9bd..4c9740b6bca7669fe3ada3cc927e63ed005b50d5 100644
--- a/mllib-local/src/test/scala/org/apache/spark/ml/linalg/BreezeVectorConversionSuite.scala
+++ b/mllib-local/src/test/scala/org/apache/spark/ml/linalg/BreezeVectorConversionSuite.scala
@@ -33,12 +33,12 @@ class BreezeVectorConversionSuite extends SparkMLFunSuite {
test("dense to breeze") {
val vec = Vectors.dense(arr)
- assert(vec.toBreeze === new BDV[Double](arr))
+ assert(vec.asBreeze === new BDV[Double](arr))
}
test("sparse to breeze") {
val vec = Vectors.sparse(n, indices, values)
- assert(vec.toBreeze === new BSV[Double](indices, values, n))
+ assert(vec.asBreeze === new BSV[Double](indices, values, n))
}
test("dense breeze to vector") {
diff --git a/mllib-local/src/test/scala/org/apache/spark/ml/linalg/MatricesSuite.scala b/mllib-local/src/test/scala/org/apache/spark/ml/linalg/MatricesSuite.scala
index 5c69c5ed7bdd2c8c84961d0dfca64b299cc502b4..2796fcf2cbc22518634c89bf376f504c51b1e889 100644
--- a/mllib-local/src/test/scala/org/apache/spark/ml/linalg/MatricesSuite.scala
+++ b/mllib-local/src/test/scala/org/apache/spark/ml/linalg/MatricesSuite.scala
@@ -61,7 +61,7 @@ class MatricesSuite extends SparkMLFunSuite {
(1, 2, 2.0), (2, 2, 2.0), (1, 2, 2.0), (0, 0, 0.0))
val mat2 = SparseMatrix.fromCOO(m, n, entries)
- assert(mat.toBreeze === mat2.toBreeze)
+ assert(mat.asBreeze === mat2.asBreeze)
assert(mat2.values.length == 4)
}
@@ -174,8 +174,8 @@ class MatricesSuite extends SparkMLFunSuite {
val spMat2 = deMat1.toSparse
val deMat2 = spMat1.toDense
- assert(spMat1.toBreeze === spMat2.toBreeze)
- assert(deMat1.toBreeze === deMat2.toBreeze)
+ assert(spMat1.asBreeze === spMat2.asBreeze)
+ assert(deMat1.asBreeze === deMat2.asBreeze)
}
test("map, update") {
@@ -209,8 +209,8 @@ class MatricesSuite extends SparkMLFunSuite {
val sATexpected =
new SparseMatrix(3, 4, Array(0, 1, 2, 3, 4), Array(1, 0, 1, 2), Array(2.0, 1.0, 1.0, 3.0))
- assert(dAT.toBreeze === dATexpected.toBreeze)
- assert(sAT.toBreeze === sATexpected.toBreeze)
+ assert(dAT.asBreeze === dATexpected.asBreeze)
+ assert(sAT.asBreeze === sATexpected.asBreeze)
assert(dA(1, 0) === dAT(0, 1))
assert(dA(2, 1) === dAT(1, 2))
assert(sA(1, 0) === sAT(0, 1))
@@ -219,8 +219,8 @@ class MatricesSuite extends SparkMLFunSuite {
assert(!dA.toArray.eq(dAT.toArray), "has to have a new array")
assert(dA.values.eq(dAT.transpose.asInstanceOf[DenseMatrix].values), "should not copy array")
- assert(dAT.toSparse.toBreeze === sATexpected.toBreeze)
- assert(sAT.toDense.toBreeze === dATexpected.toBreeze)
+ assert(dAT.toSparse.asBreeze === sATexpected.asBreeze)
+ assert(sAT.toDense.asBreeze === dATexpected.asBreeze)
}
test("foreachActive") {
diff --git a/mllib-local/src/test/scala/org/apache/spark/ml/linalg/VectorsSuite.scala b/mllib-local/src/test/scala/org/apache/spark/ml/linalg/VectorsSuite.scala
index 887814b5e731af340dce9afef6fae61c994ff056..614be460a414a63265b0f40982d0094ba2b3648e 100644
--- a/mllib-local/src/test/scala/org/apache/spark/ml/linalg/VectorsSuite.scala
+++ b/mllib-local/src/test/scala/org/apache/spark/ml/linalg/VectorsSuite.scala
@@ -230,7 +230,7 @@ class VectorsSuite extends SparkMLFunSuite {
val denseVector1 = Vectors.dense(sparseVector1.toArray)
val denseVector2 = Vectors.dense(sparseVector2.toArray)
- val squaredDist = breezeSquaredDistance(sparseVector1.toBreeze, sparseVector2.toBreeze)
+ val squaredDist = breezeSquaredDistance(sparseVector1.asBreeze, sparseVector2.asBreeze)
// SparseVector vs. SparseVector
assert(Vectors.sqdist(sparseVector1, sparseVector2) ~== squaredDist relTol 1E-8)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
index 0a569c49179a615982e43702d2e2e5c55b3568ef..576584c62797db930c945dae79dca36baab1543d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
@@ -616,8 +616,8 @@ private[ann] class DataStacker(stackSize: Int, inputSize: Int, outputSize: Int)
data.map { v =>
(0.0,
Vectors.fromBreeze(BDV.vertcat(
- v._1.toBreeze.toDenseVector,
- v._2.toBreeze.toDenseVector))
+ v._1.asBreeze.toDenseVector,
+ v._2.asBreeze.toDenseVector))
) }
} else {
data.mapPartitions { it =>
@@ -665,8 +665,8 @@ private[ann] class ANNUpdater extends Updater {
iter: Int,
regParam: Double): (OldVector, Double) = {
val thisIterStepSize = stepSize
- val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector
- Baxpy(-thisIterStepSize, gradient.toBreeze, brzWeights)
+ val brzWeights: BV[Double] = weightsOld.asBreeze.toDenseVector
+ Baxpy(-thisIterStepSize, gradient.asBreeze, brzWeights)
(OldVectors.fromBreeze(brzWeights), 0)
}
}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 61e355ab9fba1cd009fc6b2806a79df7e5dabfc3..cecf3c43dfad40e6705e4da8bc1e37b0f1876d8c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -400,7 +400,7 @@ class LogisticRegression @Since("1.2.0") (
}
val states = optimizer.iterations(new CachedDiffFunction(costFun),
- initialCoefficientsWithIntercept.toBreeze.toDenseVector)
+ initialCoefficientsWithIntercept.asBreeze.toDenseVector)
/*
Note that in Logistic Regression, the objective history (loss + regularization)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
index 773e50e24549c742da92bb2dd5eaf749ecb13688..563a3b14e9a6f9b211fe39a2c78ccfcb6b9b3241 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
@@ -113,7 +113,7 @@ class GaussianMixtureModel private[ml] (
private[clustering] def predictProbability(features: Vector): Vector = {
val probs: Array[Double] =
- GaussianMixtureModel.computeProbabilities(features.toBreeze.toDenseVector, gaussians, weights)
+ GaussianMixtureModel.computeProbabilities(features.asBreeze.toDenseVector, gaussians, weights)
Vectors.dense(probs)
}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
index 0dffba93ac571d8c6e91e0f270fefc7ad037d396..1b5159902eeb54a203ddf348b4461c80492d74a3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
@@ -122,7 +122,7 @@ class MaxAbsScalerModel private[ml] (
// TODO: this looks hack, we may have to handle sparse and dense vectors separately.
val maxAbsUnzero = Vectors.dense(maxAbs.toArray.map(x => if (x == 0) 1 else x))
val reScale = udf { (vector: Vector) =>
- val brz = vector.toBreeze / maxAbsUnzero.toBreeze
+ val brz = vector.asBreeze / maxAbsUnzero.asBreeze
Vectors.fromBreeze(brz)
}
dataset.withColumn($(outputCol), reScale(col($(inputCol))))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
index c6ff639f2962095eaca56087d579667add56546e..d15f1b8563b24e6e56161cccd20b30131e28cabc 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
@@ -162,7 +162,7 @@ class MinMaxScalerModel private[ml] (
@Since("2.0.0")
override def transform(dataset: Dataset[_]): DataFrame = {
- val originalRange = (originalMax.toBreeze - originalMin.toBreeze).toArray
+ val originalRange = (originalMax.asBreeze - originalMin.asBreeze).toArray
val minArray = originalMin.toArray
val reScale = udf { (vector: Vector) =>
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
index 00ef6ccc74d286590cdb8d070f00d0d39405b0a1..c4400738426b50dcbbb2efca6595f44d3cca3925 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
@@ -223,7 +223,7 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
val initialParameters = Vectors.zeros(numFeatures + 2)
val states = optimizer.iterations(new CachedDiffFunction(costFun),
- initialParameters.toBreeze.toDenseVector)
+ initialParameters.asBreeze.toDenseVector)
val parameters = {
val arrayBuilder = mutable.ArrayBuilder.make[Double]
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 4d66b0eb37ab43c74ee156aaf009eaa1483fa5d0..6be2584785bd37eba70074efd40f9a13e3a04529 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -297,7 +297,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
val initialCoefficients = Vectors.zeros(numFeatures)
val states = optimizer.iterations(new CachedDiffFunction(costFun),
- initialCoefficients.toBreeze.toDenseVector)
+ initialCoefficients.asBreeze.toDenseVector)
val (coefficients, objectiveHistory) = {
/*
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index a8d3fd4177a234a5e6233b4e76b88130f72313dd..783c1c835b3b7370cb1d1fe01fa4a1b7babeff23 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -72,7 +72,7 @@ class SVMModel @Since("1.1.0") (
dataMatrix: Vector,
weightMatrix: Vector,
intercept: Double) = {
- val margin = weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
+ val margin = weightMatrix.asBreeze.dot(dataMatrix.asBreeze) + intercept
threshold match {
case Some(t) => if (margin > t) 1.0 else 0.0
case None => margin
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index f04c87259c9419f2b492d0bcf6f4fe288215b80f..a214b1a26f443b2d8baeb3d41a15d723f28df973 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -166,7 +166,7 @@ class GaussianMixture private (
val sc = data.sparkContext
// we will operate on the data as breeze data
- val breezeData = data.map(_.toBreeze).cache()
+ val breezeData = data.map(_.asBreeze).cache()
// Get length of the input vectors
val d = breezeData.first().length
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index f470b0f3c3690726012aaccdbe0e8dac7c6a3927..31ad56dba6aef39c17973d62c4556cccffaf0ff0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -96,7 +96,7 @@ class GaussianMixtureModel @Since("1.3.0") (
val bcDists = sc.broadcast(gaussians)
val bcWeights = sc.broadcast(weights)
points.map { x =>
- computeSoftAssignments(x.toBreeze.toDenseVector, bcDists.value, bcWeights.value, k)
+ computeSoftAssignments(x.asBreeze.toDenseVector, bcDists.value, bcWeights.value, k)
}
}
@@ -105,7 +105,7 @@ class GaussianMixtureModel @Since("1.3.0") (
*/
@Since("1.4.0")
def predictSoft(point: Vector): Array[Double] = {
- computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
+ computeSoftAssignments(point.asBreeze.toDenseVector, gaussians, weights, k)
}
/**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 0a515f893dc0b7bab0490d374bd129e04ed07090..1b66013d543ad5b22146e5c32b2e5f36572e7703 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -205,7 +205,7 @@ class LocalLDAModel private[spark] (
@Since("1.3.0")
override def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] = {
- val brzTopics = topics.toBreeze.toDenseMatrix
+ val brzTopics = topics.asBreeze.toDenseMatrix
Range(0, k).map { topicIndex =>
val topic = normalize(brzTopics(::, topicIndex), 1.0)
val (termWeights, terms) =
@@ -233,7 +233,7 @@ class LocalLDAModel private[spark] (
*/
@Since("1.5.0")
def logLikelihood(documents: RDD[(Long, Vector)]): Double = logLikelihoodBound(documents,
- docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
+ docConcentration, topicConcentration, topicsMatrix.asBreeze.toDenseMatrix, gammaShape, k,
vocabSize)
/**
@@ -291,7 +291,7 @@ class LocalLDAModel private[spark] (
gammaShape: Double,
k: Int,
vocabSize: Long): Double = {
- val brzAlpha = alpha.toBreeze.toDenseVector
+ val brzAlpha = alpha.asBreeze.toDenseVector
// transpose because dirichletExpectation normalizes by row and we need to normalize
// by topic (columns of lambda)
val Elogbeta = LDAUtils.dirichletExpectation(lambda.t).t
@@ -344,9 +344,9 @@ class LocalLDAModel private[spark] (
def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = {
// Double transpose because dirichletExpectation normalizes by row and we need to normalize
// by topic (columns of lambda)
- val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.toBreeze.toDenseMatrix.t).t)
+ val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.asBreeze.toDenseMatrix.t).t)
val expElogbetaBc = documents.sparkContext.broadcast(expElogbeta)
- val docConcentrationBrz = this.docConcentration.toBreeze
+ val docConcentrationBrz = this.docConcentration.asBreeze
val gammaShape = this.gammaShape
val k = this.k
@@ -367,9 +367,9 @@ class LocalLDAModel private[spark] (
/** Get a method usable as a UDF for [[topicDistributions()]] */
private[spark] def getTopicDistributionMethod(sc: SparkContext): Vector => Vector = {
- val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.toBreeze.toDenseMatrix.t).t)
+ val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.asBreeze.toDenseMatrix.t).t)
val expElogbetaBc = sc.broadcast(expElogbeta)
- val docConcentrationBrz = this.docConcentration.toBreeze
+ val docConcentrationBrz = this.docConcentration.asBreeze
val gammaShape = this.gammaShape
val k = this.k
@@ -399,14 +399,14 @@ class LocalLDAModel private[spark] (
*/
@Since("2.0.0")
def topicDistribution(document: Vector): Vector = {
- val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.toBreeze.toDenseMatrix.t).t)
+ val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.asBreeze.toDenseMatrix.t).t)
if (document.numNonzeros == 0) {
Vectors.zeros(this.k)
} else {
val (gamma, _, _) = OnlineLDAOptimizer.variationalTopicInference(
document,
expElogbeta,
- this.docConcentration.toBreeze,
+ this.docConcentration.asBreeze,
gammaShape,
this.k)
Vectors.dense(normalize(gamma, 1.0).toArray)
@@ -456,7 +456,7 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
("gammaShape" -> gammaShape)))
sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
- val topicsDenseMatrix = topicsMatrix.toBreeze.toDenseMatrix
+ val topicsDenseMatrix = topicsMatrix.asBreeze.toDenseMatrix
val topics = Range(0, k).map { topicInd =>
Data(Vectors.dense((topicsDenseMatrix(::, topicInd).toArray)), topicInd)
}
@@ -480,7 +480,7 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
val brzTopics = BDM.zeros[Double](vocabSize, k)
topics.foreach { case Row(vec: Vector, ind: Int) =>
- brzTopics(::, ind) := vec.toBreeze
+ brzTopics(::, ind) := vec.asBreeze
}
val topicsMat = Matrices.fromBreeze(brzTopics)
@@ -896,9 +896,9 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
Loader.checkSchema[VertexData](vertexDataFrame.schema)
Loader.checkSchema[EdgeData](edgeDataFrame.schema)
val globalTopicTotals: LDA.TopicCounts =
- dataFrame.first().getAs[Vector](0).toBreeze.toDenseVector
+ dataFrame.first().getAs[Vector](0).asBreeze.toDenseVector
val vertices: RDD[(VertexId, LDA.TopicCounts)] = vertexDataFrame.rdd.map {
- case Row(ind: Long, vec: Vector) => (ind, vec.toBreeze.toDenseVector)
+ case Row(ind: Long, vec: Vector) => (ind, vec.asBreeze.toDenseVector)
}
val edges: RDD[Edge[LDA.TokenCount]] = edgeDataFrame.rdd.map {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 1b3e2f600d028a4e621975aafb2f50af0970ab6a..2436efba32489094a03cb234ca1dc6d11a6394c2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -137,7 +137,7 @@ final class EMLDAOptimizer extends LDAOptimizer {
// For each document, create an edge (Document -> Term) for each unique term in the document.
val edges: RDD[Edge[TokenCount]] = docs.flatMap { case (docID: Long, termCounts: Vector) =>
// Add edges for terms with non-zero counts.
- termCounts.toBreeze.activeIterator.filter(_._2 != 0.0).map { case (term, cnt) =>
+ termCounts.asBreeze.activeIterator.filter(_._2 != 0.0).map { case (term, cnt) =>
Edge(docID, term2index(term), cnt)
}
}
@@ -457,7 +457,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
val vocabSize = this.vocabSize
val expElogbeta = exp(LDAUtils.dirichletExpectation(lambda)).t
val expElogbetaBc = batch.sparkContext.broadcast(expElogbeta)
- val alpha = this.alpha.toBreeze
+ val alpha = this.alpha.asBreeze
val gammaShape = this.gammaShape
val stats: RDD[(BDM[Double], List[BDV[Double]])] = batch.mapPartitions { docs =>
@@ -507,7 +507,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
private def updateAlpha(gammat: BDM[Double]): Unit = {
val weight = rho()
val N = gammat.rows.toDouble
- val alpha = this.alpha.toBreeze.toDenseVector
+ val alpha = this.alpha.asBreeze.toDenseVector
val logphat: BDM[Double] = sum(LDAUtils.dirichletExpectation(gammat)(::, breeze.linalg.*)) / N
val gradf = N * (-LDAUtils.dirichletExpectation(alpha) + logphat.toDenseVector)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
index 24e1cff0dcc6b2535b57fb485008de053e53738f..52bdccb919a61e8271a381334c7e97c00c2de447 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -135,8 +135,8 @@ class StreamingKMeansModel @Since("1.2.0") (
while (j < dim) {
val x = largestClusterCenter(j)
val p = 1e-14 * math.max(math.abs(x), 1.0)
- largestClusterCenter.toBreeze(j) = x + p
- smallestClusterCenter.toBreeze(j) = x - p
+ largestClusterCenter.asBreeze(j) = x + p
+ smallestClusterCenter.asBreeze(j) = x - p
j += 1
}
}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index ee1956c2d41357cdb51f3c0a1efc98a0dd8cefa7..e8f34388cd9fecb7eb58c308a39bd9264504bb3c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -75,7 +75,7 @@ sealed trait Matrix extends Serializable {
def rowIter: Iterator[Vector] = this.transpose.colIter
/** Converts to a breeze matrix. */
- private[mllib] def toBreeze: BM[Double]
+ private[mllib] def asBreeze: BM[Double]
/** Gets the (i, j)-th element. */
@Since("1.3.0")
@@ -118,11 +118,11 @@ sealed trait Matrix extends Serializable {
}
/** A human readable representation of the matrix */
- override def toString: String = toBreeze.toString()
+ override def toString: String = asBreeze.toString()
/** A human readable representation of the matrix with maximum lines and width */
@Since("1.4.0")
- def toString(maxLines: Int, maxLineWidth: Int): String = toBreeze.toString(maxLines, maxLineWidth)
+ def toString(maxLines: Int, maxLineWidth: Int): String = asBreeze.toString(maxLines, maxLineWidth)
/**
* Map the values of this matrix using a function. Generates a new matrix. Performs the
@@ -300,7 +300,7 @@ class DenseMatrix @Since("1.3.0") (
this(numRows, numCols, values, false)
override def equals(o: Any): Boolean = o match {
- case m: Matrix => toBreeze == m.toBreeze
+ case m: Matrix => asBreeze == m.asBreeze
case _ => false
}
@@ -308,7 +308,7 @@ class DenseMatrix @Since("1.3.0") (
com.google.common.base.Objects.hashCode(numRows: Integer, numCols: Integer, toArray)
}
- private[mllib] def toBreeze: BM[Double] = {
+ private[mllib] def asBreeze: BM[Double] = {
if (!isTransposed) {
new BDM[Double](numRows, numCols, values)
} else {
@@ -607,13 +607,13 @@ class SparseMatrix @Since("1.3.0") (
values: Array[Double]) = this(numRows, numCols, colPtrs, rowIndices, values, false)
override def equals(o: Any): Boolean = o match {
- case m: Matrix => toBreeze == m.toBreeze
+ case m: Matrix => asBreeze == m.asBreeze
case _ => false
}
- override def hashCode(): Int = toBreeze.hashCode
+ override def hashCode(): Int = asBreeze.hashCode
- private[mllib] def toBreeze: BM[Double] = {
+ private[mllib] def asBreeze: BM[Double] = {
if (!isTransposed) {
new BSM[Double](values, numRows, numCols, colPtrs, rowIndices)
} else {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 7ebcd297bd54934a21ab0000b11f86867703ff8f..02fd60da7d59022bf7f34b7efef5f1297e59cafc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -103,14 +103,14 @@ sealed trait Vector extends Serializable {
/**
* Converts the instance to a breeze vector.
*/
- private[spark] def toBreeze: BV[Double]
+ private[spark] def asBreeze: BV[Double]
/**
* Gets the value of the ith element.
* @param i index
*/
@Since("1.1.0")
- def apply(i: Int): Double = toBreeze(i)
+ def apply(i: Int): Double = asBreeze(i)
/**
* Makes a deep copy of this vector.
@@ -610,7 +610,7 @@ class DenseVector @Since("1.0.0") (
@Since("1.0.0")
override def toArray: Array[Double] = values
- private[spark] override def toBreeze: BV[Double] = new BDV[Double](values)
+ private[spark] override def asBreeze: BV[Double] = new BDV[Double](values)
@Since("1.0.0")
override def apply(i: Int): Double = values(i)
@@ -770,7 +770,7 @@ class SparseVector @Since("1.0.0") (
new SparseVector(size, indices.clone(), values.clone())
}
- private[spark] override def toBreeze: BV[Double] = new BSV[Double](indices, values, size)
+ private[spark] override def asBreeze: BV[Double] = new BSV[Double](indices, values, size)
@Since("1.6.0")
override def foreachActive(f: (Int, Double) => Unit): Unit = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
index 580d7a98fb3629b7f3fd947be01ff2d2d98598db..7a24617781ece5f59d0c55769da727c5a18192de 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
@@ -275,7 +275,7 @@ class BlockMatrix @Since("1.3.0") (
val rows = blocks.flatMap { case ((blockRowIdx, blockColIdx), mat) =>
mat.rowIter.zipWithIndex.map {
case (vector, rowIdx) =>
- blockRowIdx * rowsPerBlock + rowIdx -> (blockColIdx, vector.toBreeze)
+ blockRowIdx * rowsPerBlock + rowIdx -> (blockColIdx, vector.asBreeze)
}
}.groupByKey().map { case (rowIdx, vectors) =>
val numberNonZeroPerRow = vectors.map(_._2.activeSize).sum.toDouble / cols.toDouble
@@ -367,12 +367,12 @@ class BlockMatrix @Since("1.3.0") (
}
if (a.isEmpty) {
val zeroBlock = BM.zeros[Double](b.head.numRows, b.head.numCols)
- val result = binMap(zeroBlock, b.head.toBreeze)
+ val result = binMap(zeroBlock, b.head.asBreeze)
new MatrixBlock((blockRowIndex, blockColIndex), Matrices.fromBreeze(result))
} else if (b.isEmpty) {
new MatrixBlock((blockRowIndex, blockColIndex), a.head)
} else {
- val result = binMap(a.head.toBreeze, b.head.toBreeze)
+ val result = binMap(a.head.asBreeze, b.head.asBreeze)
new MatrixBlock((blockRowIndex, blockColIndex), Matrices.fromBreeze(result))
}
}
@@ -479,7 +479,7 @@ class BlockMatrix @Since("1.3.0") (
case _ =>
throw new SparkException(s"Unrecognized matrix type ${rightBlock.getClass}.")
}
- ((leftRowIndex, rightColIndex), C.toBreeze)
+ ((leftRowIndex, rightColIndex), C.asBreeze)
}
}
}.reduceByKey(resultPartitioner, (a, b) => a + b).mapValues(Matrices.fromBreeze)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 4b8ed301eb3cb4f6255bd161a31ed98aed627464..cd5209d0ebe20743cd50a8f937c2508006e67e5b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -92,7 +92,7 @@ class RowMatrix @Since("1.0.0") (
val vbr = rows.context.broadcast(v)
rows.treeAggregate(BDV.zeros[Double](n))(
seqOp = (U, r) => {
- val rBrz = r.toBreeze
+ val rBrz = r.asBreeze
val a = rBrz.dot(vbr.value)
rBrz match {
// use specialized axpy for better performance
@@ -250,12 +250,12 @@ class RowMatrix @Since("1.0.0") (
val (sigmaSquares: BDV[Double], u: BDM[Double]) = computeMode match {
case SVDMode.LocalARPACK =>
require(k < n, s"k must be smaller than n in local-eigs mode but got k=$k and n=$n.")
- val G = computeGramianMatrix().toBreeze.asInstanceOf[BDM[Double]]
+ val G = computeGramianMatrix().asBreeze.asInstanceOf[BDM[Double]]
EigenValueDecomposition.symmetricEigs(v => G * v, n, k, tol, maxIter)
case SVDMode.LocalLAPACK =>
// breeze (v0.10) svd latent constraint, 7 * n * n + 4 * n < Int.MaxValue
require(n < 17515, s"$n exceeds the breeze svd capability")
- val G = computeGramianMatrix().toBreeze.asInstanceOf[BDM[Double]]
+ val G = computeGramianMatrix().asBreeze.asInstanceOf[BDM[Double]]
val brzSvd.SVD(uFull: BDM[Double], sigmaSquaresFull: BDV[Double], _) = brzSvd(G)
(sigmaSquaresFull, uFull)
case SVDMode.DistARPACK =>
@@ -338,7 +338,7 @@ class RowMatrix @Since("1.0.0") (
// large but Cov(X, Y) is small, but it is good for sparse computation.
// TODO: find a fast and stable way for sparse data.
- val G = computeGramianMatrix().toBreeze
+ val G = computeGramianMatrix().asBreeze
var i = 0
var j = 0
@@ -381,7 +381,7 @@ class RowMatrix @Since("1.0.0") (
val n = numCols().toInt
require(k > 0 && k <= n, s"k = $k out of range (0, n = $n]")
- val Cov = computeCovariance().toBreeze.asInstanceOf[BDM[Double]]
+ val Cov = computeCovariance().asBreeze.asInstanceOf[BDM[Double]]
val brzSvd.SVD(u: BDM[Double], s: BDV[Double], _) = brzSvd(Cov)
@@ -436,14 +436,14 @@ class RowMatrix @Since("1.0.0") (
require(B.isInstanceOf[DenseMatrix],
s"Only support dense matrix at this time but found ${B.getClass.getName}.")
- val Bb = rows.context.broadcast(B.toBreeze.asInstanceOf[BDM[Double]].toDenseVector.toArray)
+ val Bb = rows.context.broadcast(B.asBreeze.asInstanceOf[BDM[Double]].toDenseVector.toArray)
val AB = rows.mapPartitions { iter =>
val Bi = Bb.value
iter.map { row =>
val v = BDV.zeros[Double](k)
var i = 0
while (i < k) {
- v(i) = row.toBreeze.dot(new BDV(Bi, i * n, 1, n))
+ v(i) = row.asBreeze.dot(new BDV(Bi, i * n, 1, n))
i += 1
}
Vectors.fromBreeze(v)
@@ -541,7 +541,7 @@ class RowMatrix @Since("1.0.0") (
val bdm = BDM.zeros[Double](partRows.length, col)
var i = 0
partRows.foreach { row =>
- bdm(i, ::) := row.toBreeze.t
+ bdm(i, ::) := row.asBreeze.t
i += 1
}
breeze.linalg.qr.reduced(bdm).r
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index 735e780909ef2d072900cfd6851a6a82b9e04a16..480a64548cb7080ef91c19f95ee5a94a04d51f6f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -301,8 +301,8 @@ object GradientDescent extends Logging {
currentWeights: Vector,
convergenceTol: Double): Boolean = {
// To compare with convergence tolerance.
- val previousBDV = previousWeights.toBreeze.toDenseVector
- val currentBDV = currentWeights.toBreeze.toDenseVector
+ val previousBDV = previousWeights.asBreeze.toDenseVector
+ val currentBDV = currentWeights.asBreeze.toDenseVector
// This represents the difference of updated weights in the iteration.
val solutionVecDiff: Double = norm(previousBDV - currentBDV)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
index 74e2cad76c8f5a7dfc8143b870c0ca45a506cd36..ec6ffe6e1943910558ff8a39882fc09b16ca023b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
@@ -200,7 +200,7 @@ object LBFGS extends Logging {
val lbfgs = new BreezeLBFGS[BDV[Double]](maxNumIterations, numCorrections, convergenceTol)
val states =
- lbfgs.iterations(new CachedDiffFunction(costFun), initialWeights.toBreeze.toDenseVector)
+ lbfgs.iterations(new CachedDiffFunction(costFun), initialWeights.asBreeze.toDenseVector)
/**
* NOTE: lossSum and loss is computed using the weights from the previous iteration
@@ -281,7 +281,7 @@ object LBFGS extends Logging {
// gradientTotal = gradientSum / numExamples + gradientTotal
axpy(1.0 / numExamples, gradientSum, gradientTotal)
- (loss, gradientTotal.toBreeze.asInstanceOf[BDV[Double]])
+ (loss, gradientTotal.asBreeze.asInstanceOf[BDV[Double]])
}
}
}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala
index 03c01e0553d78f1587b2e570b9344f86612e0c7c..67d484575db528f4660e8ed761cdcafcff3dabb1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala
@@ -75,8 +75,8 @@ class SimpleUpdater extends Updater {
iter: Int,
regParam: Double): (Vector, Double) = {
val thisIterStepSize = stepSize / math.sqrt(iter)
- val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector
- brzAxpy(-thisIterStepSize, gradient.toBreeze, brzWeights)
+ val brzWeights: BV[Double] = weightsOld.asBreeze.toDenseVector
+ brzAxpy(-thisIterStepSize, gradient.asBreeze, brzWeights)
(Vectors.fromBreeze(brzWeights), 0)
}
@@ -87,7 +87,7 @@ class SimpleUpdater extends Updater {
* Updater for L1 regularized problems.
* R(w) = ||w||_1
* Uses a step-size decreasing with the square root of the number of iterations.
-
+ *
* Instead of subgradient of the regularizer, the proximal operator for the
* L1 regularization is applied after the gradient step. This is known to
* result in better sparsity of the intermediate solution.
@@ -111,8 +111,8 @@ class L1Updater extends Updater {
regParam: Double): (Vector, Double) = {
val thisIterStepSize = stepSize / math.sqrt(iter)
// Take gradient step
- val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector
- brzAxpy(-thisIterStepSize, gradient.toBreeze, brzWeights)
+ val brzWeights: BV[Double] = weightsOld.asBreeze.toDenseVector
+ brzAxpy(-thisIterStepSize, gradient.asBreeze, brzWeights)
// Apply proximal operator (soft thresholding)
val shrinkageVal = regParam * thisIterStepSize
var i = 0
@@ -146,9 +146,9 @@ class SquaredL2Updater extends Updater {
// w' = w - thisIterStepSize * (gradient + regParam * w)
// w' = (1 - thisIterStepSize * regParam) * w - thisIterStepSize * gradient
val thisIterStepSize = stepSize / math.sqrt(iter)
- val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector
+ val brzWeights: BV[Double] = weightsOld.asBreeze.toDenseVector
brzWeights :*= (1.0 - thisIterStepSize * regParam)
- brzAxpy(-thisIterStepSize, gradient.toBreeze, brzWeights)
+ brzAxpy(-thisIterStepSize, gradient.asBreeze, brzWeights)
val norm = brzNorm(brzWeights, 2.0)
(Vectors.fromBreeze(brzWeights), 0.5 * regParam * norm * norm)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index e14bddf97d0f0993d8b76502ebf8624d382a1579..cef1b4f51b8436b59745c56617110565b6b502a6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -44,7 +44,7 @@ class LassoModel @Since("1.1.0") (
dataMatrix: Vector,
weightMatrix: Vector,
intercept: Double): Double = {
- weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
+ weightMatrix.asBreeze.dot(dataMatrix.asBreeze) + intercept
}
@Since("1.3.0")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 2ceac4b8cc319e5e2bee9fdcfd3c360326115fd6..60262fdc497a6211be7dde1dfc3b346b8e7b2784 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -44,7 +44,7 @@ class LinearRegressionModel @Since("1.1.0") (
dataMatrix: Vector,
weightMatrix: Vector,
intercept: Double): Double = {
- weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
+ weightMatrix.asBreeze.dot(dataMatrix.asBreeze) + intercept
}
@Since("1.3.0")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 301f02fd98155d07b8f869a6e5262b9a4082d9de..52977ac4f062aeb9a92a7c92649abf0cc51aae20 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -45,7 +45,7 @@ class RidgeRegressionModel @Since("1.1.0") (
dataMatrix: Vector,
weightMatrix: Vector,
intercept: Double): Double = {
- weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
+ weightMatrix.asBreeze.dot(dataMatrix.asBreeze) + intercept
}
@Since("1.3.0")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala
index 515be0b81783557fd3ee79031c30652c3504ed75..e478c31bc9a0596256d21576cda830d3eba7c777 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala
@@ -55,7 +55,7 @@ private[stat] object PearsonCorrelation extends Correlation with Logging {
* 0 variance results in a correlation value of Double.NaN.
*/
def computeCorrelationMatrixFromCovariance(covarianceMatrix: Matrix): Matrix = {
- val cov = covarianceMatrix.toBreeze.asInstanceOf[BDM[Double]]
+ val cov = covarianceMatrix.asBreeze.asInstanceOf[BDM[Double]]
val n = cov.cols
// Compute the standard deviation on the diagonals first
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
index c806d6bdf6b63a86e5ff4a20b4c66a945b6e8b49..39c3644450d6d321a3c7e67069fb9a55e0845391 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
@@ -42,7 +42,7 @@ class MultivariateGaussian @Since("1.3.0") (
require(sigma.numCols == sigma.numRows, "Covariance matrix must be square")
require(mu.size == sigma.numCols, "Mean vector length must match covariance matrix size")
- private val breezeMu = mu.toBreeze.toDenseVector
+ private val breezeMu = mu.asBreeze.toDenseVector
/**
* private[mllib] constructor
@@ -66,7 +66,7 @@ class MultivariateGaussian @Since("1.3.0") (
*/
@Since("1.3.0")
def pdf(x: Vector): Double = {
- pdf(x.toBreeze)
+ pdf(x.asBreeze)
}
/**
@@ -74,7 +74,7 @@ class MultivariateGaussian @Since("1.3.0") (
*/
@Since("1.3.0")
def logpdf(x: Vector): Double = {
- logpdf(x.toBreeze)
+ logpdf(x.asBreeze)
}
/** Returns density of this multivariate Gaussian at given point, x */
@@ -118,7 +118,7 @@ class MultivariateGaussian @Since("1.3.0") (
* relation to the maximum singular value (same tolerance used by, e.g., Octave).
*/
private def calculateCovarianceConstants: (DBM[Double], Double) = {
- val eigSym.EigSym(d, u) = eigSym(sigma.toBreeze.toDenseMatrix) // sigma = u * diag(d) * u.t
+ val eigSym.EigSym(d, u) = eigSym(sigma.asBreeze.toDenseMatrix) // sigma = u * diag(d) * u.t
// For numerical stability, values are considered to be non-zero only if they exceed tol.
// This prevents any inverted value from exceeding (eps * n * max(d))^-1
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala
index 76ca6a8abd03271887242b795e133e8cd2394add..da5df9bf45e5a2b2c0abe6d74656f288ed173ba6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala
@@ -110,7 +110,7 @@ private[stat] object ChiSqTest extends Logging {
}
i += 1
distinctLabels += label
- val brzFeatures = features.toBreeze
+ val brzFeatures = features.asBreeze
(startCol until endCol).map { col =>
val feature = brzFeatures(col)
allDistinctFeatures(col) += feature
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
index ff52115ec0ec19ee6db1ebbe49f3ce2a38c7836d..04c010bd13e1ec15d42975074173c3ec47e79fe4 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
@@ -69,7 +69,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with Defa
}
def expectedMultinomialProbabilities(model: NaiveBayesModel, feature: Vector): Vector = {
- val logClassProbs: BV[Double] = model.pi.toBreeze + model.theta.multiply(feature).toBreeze
+ val logClassProbs: BV[Double] = model.pi.asBreeze + model.theta.multiply(feature).asBreeze
val classProbs = logClassProbs.toArray.map(math.exp)
val classProbsSum = classProbs.sum
Vectors.dense(classProbs.map(_ / classProbsSum))
@@ -78,8 +78,8 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with Defa
def expectedBernoulliProbabilities(model: NaiveBayesModel, feature: Vector): Vector = {
val negThetaMatrix = model.theta.map(v => math.log(1.0 - math.exp(v)))
val negFeature = Vectors.dense(feature.toArray.map(v => 1.0 - v))
- val piTheta: BV[Double] = model.pi.toBreeze + model.theta.multiply(feature).toBreeze
- val logClassProbs: BV[Double] = piTheta + negThetaMatrix.multiply(negFeature).toBreeze
+ val piTheta: BV[Double] = model.pi.asBreeze + model.theta.multiply(feature).asBreeze
+ val logClassProbs: BV[Double] = piTheta + negThetaMatrix.multiply(negFeature).asBreeze
val classProbs = logClassProbs.toArray.map(math.exp)
val classProbsSum = classProbs.sum
Vectors.dense(classProbs.map(_ / classProbsSum))
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 28fada7053d65fb530a55dfb61acbc593490c3e4..5cf43777685168ba41172036e0029af9c4d412f7 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -411,10 +411,10 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext w
val testRDD1 = sc.parallelize(testData, 2)
val testRDD2 = sc.parallelize(
- testData.map(x => LabeledPoint(x.label, Vectors.fromBreeze(x.features.toBreeze * 1.0E3))), 2)
+ testData.map(x => LabeledPoint(x.label, Vectors.fromBreeze(x.features.asBreeze * 1.0E3))), 2)
val testRDD3 = sc.parallelize(
- testData.map(x => LabeledPoint(x.label, Vectors.fromBreeze(x.features.toBreeze * 1.0E6))), 2)
+ testData.map(x => LabeledPoint(x.label, Vectors.fromBreeze(x.features.asBreeze * 1.0E6))), 2)
testRDD1.cache()
testRDD2.cache()
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
index ab54cb06d5aab2134b9d88bc9d4f2b70f1ba0bef..0c0aefc52b9bfe54ee14db80cd94dfb6d66c9a88 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
@@ -182,7 +182,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
val piVector = new BDV(model.pi)
// model.theta is row-major; treat it as col-major representation of transpose, and transpose:
val thetaMatrix = new BDM(model.theta(0).length, model.theta.length, model.theta.flatten).t
- val logClassProbs: BV[Double] = piVector + (thetaMatrix * testData.toBreeze)
+ val logClassProbs: BV[Double] = piVector + (thetaMatrix * testData.asBreeze)
val classProbs = logClassProbs.toArray.map(math.exp)
val classProbsSum = classProbs.sum
classProbs.map(_ / classProbsSum)
@@ -234,7 +234,7 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
val thetaMatrix = new BDM(model.theta(0).length, model.theta.length, model.theta.flatten).t
val negThetaMatrix = new BDM(model.theta(0).length, model.theta.length,
model.theta.flatten.map(v => math.log(1.0 - math.exp(v)))).t
- val testBreeze = testData.toBreeze
+ val testBreeze = testData.asBreeze
val negTestBreeze = new BDV(Array.fill(testBreeze.size)(1.0)) - testBreeze
val piTheta: BV[Double] = piVector + (thetaMatrix * testBreeze)
val logClassProbs: BV[Double] = piTheta + (negThetaMatrix * negTestBreeze)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index ea23196d2c8018bf881f9c8f79f08d6a801dba99..eb050158d48fee336bbfa0047fff2ac1db56c109 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -116,7 +116,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
case (docId, (topicDistribution, (indices, weights))) =>
assert(indices.length == 2)
assert(weights.length == 2)
- val bdvTopicDist = topicDistribution.toBreeze
+ val bdvTopicDist = topicDistribution.asBreeze
val top2Indices = argtopk(bdvTopicDist, 2)
assert(top2Indices.toArray === indices)
assert(bdvTopicDist(top2Indices).toArray === weights)
@@ -369,7 +369,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
val actualPredictions = ldaModel.topicDistributions(docs).cache()
val topTopics = actualPredictions.map { case (id, topics) =>
// convert results to expectedPredictions format, which only has highest probability topic
- val topicsBz = topics.toBreeze.toDenseVector
+ val topicsBz = topics.asBreeze.toDenseVector
(id, (argmax(topicsBz), max(topicsBz)))
}.sortByKey()
.values
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala
index 65e37c64d404e755192bc1a9e25438a6bc64bf61..fdaa098345d13b37d1d4fe258394d8e7cd989e9b 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala
@@ -67,7 +67,7 @@ class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase {
// estimated center from streaming should exactly match the arithmetic mean of all data points
// because the decay factor is set to 1.0
val grandMean =
- input.flatten.map(x => x.toBreeze).reduce(_ + _) / (numBatches * numPoints).toDouble
+ input.flatten.map(x => x.asBreeze).reduce(_ + _) / (numBatches * numPoints).toDouble
assert(model.latestModel().clusterCenters(0) ~== Vectors.dense(grandMean.toArray) absTol 1E-5)
}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala
index 34122d6ed2e951fbe66e71a4a7ead428c2472504..10f7bafd6cf5b6aba732211242e13930f23a4a32 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala
@@ -51,10 +51,10 @@ class NormalizerSuite extends SparkFunSuite with MLlibTestSparkContext {
assert((data1, data1RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
- assert(brzNorm(data1(0).toBreeze, 1) ~== 1.0 absTol 1E-5)
- assert(brzNorm(data1(2).toBreeze, 1) ~== 1.0 absTol 1E-5)
- assert(brzNorm(data1(3).toBreeze, 1) ~== 1.0 absTol 1E-5)
- assert(brzNorm(data1(4).toBreeze, 1) ~== 1.0 absTol 1E-5)
+ assert(brzNorm(data1(0).asBreeze, 1) ~== 1.0 absTol 1E-5)
+ assert(brzNorm(data1(2).asBreeze, 1) ~== 1.0 absTol 1E-5)
+ assert(brzNorm(data1(3).asBreeze, 1) ~== 1.0 absTol 1E-5)
+ assert(brzNorm(data1(4).asBreeze, 1) ~== 1.0 absTol 1E-5)
assert(data1(0) ~== Vectors.sparse(3, Seq((0, -0.465116279), (1, 0.53488372))) absTol 1E-5)
assert(data1(1) ~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
@@ -78,10 +78,10 @@ class NormalizerSuite extends SparkFunSuite with MLlibTestSparkContext {
assert((data2, data2RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
- assert(brzNorm(data2(0).toBreeze, 2) ~== 1.0 absTol 1E-5)
- assert(brzNorm(data2(2).toBreeze, 2) ~== 1.0 absTol 1E-5)
- assert(brzNorm(data2(3).toBreeze, 2) ~== 1.0 absTol 1E-5)
- assert(brzNorm(data2(4).toBreeze, 2) ~== 1.0 absTol 1E-5)
+ assert(brzNorm(data2(0).asBreeze, 2) ~== 1.0 absTol 1E-5)
+ assert(brzNorm(data2(2).asBreeze, 2) ~== 1.0 absTol 1E-5)
+ assert(brzNorm(data2(3).asBreeze, 2) ~== 1.0 absTol 1E-5)
+ assert(brzNorm(data2(4).asBreeze, 2) ~== 1.0 absTol 1E-5)
assert(data2(0) ~== Vectors.sparse(3, Seq((0, -0.65617871), (1, 0.75460552))) absTol 1E-5)
assert(data2(1) ~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala
index de2c3c13bd923e993a44256817f567c148ef01cf..9e4735afdd59f9e69ac3213757095a1f5df1e089 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala
@@ -24,7 +24,7 @@ import org.apache.spark.SparkFunSuite
class BreezeMatrixConversionSuite extends SparkFunSuite {
test("dense matrix to breeze") {
val mat = Matrices.dense(3, 2, Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0))
- val breeze = mat.toBreeze.asInstanceOf[BDM[Double]]
+ val breeze = mat.asBreeze.asInstanceOf[BDM[Double]]
assert(breeze.rows === mat.numRows)
assert(breeze.cols === mat.numCols)
assert(breeze.data.eq(mat.asInstanceOf[DenseMatrix].values), "should not copy data")
@@ -48,7 +48,7 @@ class BreezeMatrixConversionSuite extends SparkFunSuite {
val colPtrs = Array(0, 2, 4)
val rowIndices = Array(1, 2, 1, 2)
val mat = Matrices.sparse(3, 2, colPtrs, rowIndices, values)
- val breeze = mat.toBreeze.asInstanceOf[BSM[Double]]
+ val breeze = mat.asBreeze.asInstanceOf[BSM[Double]]
assert(breeze.rows === mat.numRows)
assert(breeze.cols === mat.numCols)
assert(breeze.data.eq(mat.asInstanceOf[SparseMatrix].values), "should not copy data")
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala
index 3772c9235ad3a7610df0c9b3a9c336e56773f94d..996f621f18c80946173f4bccb39106fd6561cda5 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeVectorConversionSuite.scala
@@ -33,12 +33,12 @@ class BreezeVectorConversionSuite extends SparkFunSuite {
test("dense to breeze") {
val vec = Vectors.dense(arr)
- assert(vec.toBreeze === new BDV[Double](arr))
+ assert(vec.asBreeze === new BDV[Double](arr))
}
test("sparse to breeze") {
val vec = Vectors.sparse(n, indices, values)
- assert(vec.toBreeze === new BSV[Double](indices, values, n))
+ assert(vec.asBreeze === new BSV[Double](indices, values, n))
}
test("dense breeze to vector") {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
index 8c5b4bda2518d9882fa2402fd54c11ee1597085e..d0c4dd28e14eeac81b41057705d04c02aadafdd4 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
@@ -63,7 +63,7 @@ class MatricesSuite extends SparkFunSuite {
(1, 2, 2.0), (2, 2, 2.0), (1, 2, 2.0), (0, 0, 0.0))
val mat2 = SparseMatrix.fromCOO(m, n, entries)
- assert(mat.toBreeze === mat2.toBreeze)
+ assert(mat.asBreeze === mat2.asBreeze)
assert(mat2.values.length == 4)
}
@@ -176,8 +176,8 @@ class MatricesSuite extends SparkFunSuite {
val spMat2 = deMat1.toSparse
val deMat2 = spMat1.toDense
- assert(spMat1.toBreeze === spMat2.toBreeze)
- assert(deMat1.toBreeze === deMat2.toBreeze)
+ assert(spMat1.asBreeze === spMat2.asBreeze)
+ assert(deMat1.asBreeze === deMat2.asBreeze)
}
test("map, update") {
@@ -211,8 +211,8 @@ class MatricesSuite extends SparkFunSuite {
val sATexpected =
new SparseMatrix(3, 4, Array(0, 1, 2, 3, 4), Array(1, 0, 1, 2), Array(2.0, 1.0, 1.0, 3.0))
- assert(dAT.toBreeze === dATexpected.toBreeze)
- assert(sAT.toBreeze === sATexpected.toBreeze)
+ assert(dAT.asBreeze === dATexpected.asBreeze)
+ assert(sAT.asBreeze === sATexpected.asBreeze)
assert(dA(1, 0) === dAT(0, 1))
assert(dA(2, 1) === dAT(1, 2))
assert(sA(1, 0) === sAT(0, 1))
@@ -221,8 +221,8 @@ class MatricesSuite extends SparkFunSuite {
assert(!dA.toArray.eq(dAT.toArray), "has to have a new array")
assert(dA.values.eq(dAT.transpose.asInstanceOf[DenseMatrix].values), "should not copy array")
- assert(dAT.toSparse.toBreeze === sATexpected.toBreeze)
- assert(sAT.toDense.toBreeze === dATexpected.toBreeze)
+ assert(dAT.toSparse.asBreeze === sATexpected.asBreeze)
+ assert(sAT.toDense.asBreeze === dATexpected.asBreeze)
}
test("foreachActive") {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
index 2e9c40ab88ed9b1e24248a7fc6af72aa97a4b8f9..71a3ceac1b9471eef162682613991780373db760 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
@@ -269,7 +269,7 @@ class VectorsSuite extends SparkFunSuite with Logging {
val denseVector1 = Vectors.dense(sparseVector1.toArray)
val denseVector2 = Vectors.dense(sparseVector2.toArray)
- val squaredDist = breezeSquaredDistance(sparseVector1.toBreeze, sparseVector2.toBreeze)
+ val squaredDist = breezeSquaredDistance(sparseVector1.asBreeze, sparseVector2.asBreeze)
// SparseVector vs. SparseVector
assert(Vectors.sqdist(sparseVector1, sparseVector2) ~== squaredDist relTol 1E-8)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrixSuite.scala
index f37eaf225ab88c6ccdf0c73e06963a212691ddb4..e5a2cbbb588df05d3fbf8d098bc21abd44557c92 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrixSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrixSuite.scala
@@ -152,7 +152,7 @@ class BlockMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
val C = B.toIndexedRowMatrix.rows.collect
- (C(0).vector.toBreeze, C(1).vector.toBreeze) match {
+ (C(0).vector.asBreeze, C(1).vector.asBreeze) match {
case (denseVector: BDV[Double], sparseVector: BSV[Double]) =>
assert(denseVector.length === sparseVector.length)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala
index 5b7ccb90158b03022112dce671334b0a370cbbcb..99af5fa10d999e12851d9d53db94d8dc08a29d2b 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala
@@ -108,7 +108,7 @@ class IndexedRowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
val C = A.multiply(B)
val localA = A.toBreeze()
val localC = C.toBreeze()
- val expected = localA * B.toBreeze.asInstanceOf[BDM[Double]]
+ val expected = localA * B.asBreeze.asInstanceOf[BDM[Double]]
assert(localC === expected)
}
@@ -119,7 +119,7 @@ class IndexedRowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
(90.0, 12.0, 24.0),
(12.0, 17.0, 22.0),
(24.0, 22.0, 30.0))
- assert(G.toBreeze === expected)
+ assert(G.asBreeze === expected)
}
test("svd") {
@@ -128,8 +128,8 @@ class IndexedRowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
assert(svd.U.isInstanceOf[IndexedRowMatrix])
val localA = A.toBreeze()
val U = svd.U.toBreeze()
- val s = svd.s.toBreeze.asInstanceOf[BDV[Double]]
- val V = svd.V.toBreeze.asInstanceOf[BDM[Double]]
+ val s = svd.s.asBreeze.asInstanceOf[BDV[Double]]
+ val V = svd.V.asBreeze.asInstanceOf[BDM[Double]]
assert(closeToZero(U.t * U - BDM.eye[Double](n)))
assert(closeToZero(V.t * V - BDM.eye[Double](n)))
assert(closeToZero(U * brzDiag(s) * V.t - localA))
@@ -155,7 +155,7 @@ class IndexedRowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
test("similar columns") {
val A = new IndexedRowMatrix(indexedRows)
- val gram = A.computeGramianMatrix().toBreeze.toDenseMatrix
+ val gram = A.computeGramianMatrix().asBreeze.toDenseMatrix
val G = A.columnSimilarities().toBreeze()
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
index 2dff52c601d8177dcfa673997ef2146be5367b94..7c4c6d8409c6c0d78ba3f306aa588f23abb52883 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
@@ -96,7 +96,7 @@ class RowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
Matrices.dense(n, n, Array(126.0, 54.0, 72.0, 54.0, 66.0, 78.0, 72.0, 78.0, 94.0))
for (mat <- Seq(denseMat, sparseMat)) {
val G = mat.computeGramianMatrix()
- assert(G.toBreeze === expected.toBreeze)
+ assert(G.asBreeze === expected.asBreeze)
}
}
@@ -153,8 +153,8 @@ class RowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
assert(V.numRows === n)
assert(V.numCols === k)
assertColumnEqualUpToSign(U.toBreeze(), localU, k)
- assertColumnEqualUpToSign(V.toBreeze.asInstanceOf[BDM[Double]], localV, k)
- assert(closeToZero(s.toBreeze.asInstanceOf[BDV[Double]] - localSigma(0 until k)))
+ assertColumnEqualUpToSign(V.asBreeze.asInstanceOf[BDM[Double]], localV, k)
+ assert(closeToZero(s.asBreeze.asInstanceOf[BDV[Double]] - localSigma(0 until k)))
}
}
val svdWithoutU = mat.computeSVD(1, computeU = false, 1e-9, 300, 1e-10, mode)
@@ -207,7 +207,7 @@ class RowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
val (pc, expVariance) = mat.computePrincipalComponentsAndExplainedVariance(k)
assert(pc.numRows === n)
assert(pc.numCols === k)
- assertColumnEqualUpToSign(pc.toBreeze.asInstanceOf[BDM[Double]], principalComponents, k)
+ assertColumnEqualUpToSign(pc.asBreeze.asInstanceOf[BDM[Double]], principalComponents, k)
assert(
closeToZero(BDV(expVariance.toArray) -
BDV(Arrays.copyOfRange(explainedVariance.data, 0, k))))
@@ -256,12 +256,12 @@ class RowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
val calcQ = result.Q
val calcR = result.R
assert(closeToZero(abs(expected.q) - abs(calcQ.toBreeze())))
- assert(closeToZero(abs(expected.r) - abs(calcR.toBreeze.asInstanceOf[BDM[Double]])))
+ assert(closeToZero(abs(expected.r) - abs(calcR.asBreeze.asInstanceOf[BDM[Double]])))
assert(closeToZero(calcQ.multiply(calcR).toBreeze - mat.toBreeze()))
// Decomposition without computing Q
val rOnly = mat.tallSkinnyQR(computeQ = false)
assert(rOnly.Q == null)
- assert(closeToZero(abs(expected.r) - abs(rOnly.R.toBreeze.asInstanceOf[BDM[Double]])))
+ assert(closeToZero(abs(expected.r) - abs(rOnly.R.asBreeze.asInstanceOf[BDM[Double]])))
}
}
@@ -269,7 +269,7 @@ class RowMatrixSuite extends SparkFunSuite with MLlibTestSparkContext {
for (mat <- Seq(denseMat, sparseMat)) {
val result = mat.computeCovariance()
val expected = breeze.linalg.cov(mat.toBreeze())
- assert(closeToZero(abs(expected) - abs(result.toBreeze.asInstanceOf[BDM[Double]])))
+ assert(closeToZero(abs(expected) - abs(result.asBreeze.asInstanceOf[BDM[Double]])))
}
}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
index 700f803490c080817e3b8b75f7f55f5096355475..e32767edb17a8a7222a70c4eb907d2c6c1644784 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
@@ -104,8 +104,8 @@ class CorrelationSuite extends SparkFunSuite with MLlibTestSparkContext with Log
(Double.NaN, Double.NaN, 1.00000000, Double.NaN),
(0.40047142, 0.91359586, Double.NaN, 1.0000000))
// scalastyle:on
- assert(matrixApproxEqual(defaultMat.toBreeze, expected))
- assert(matrixApproxEqual(pearsonMat.toBreeze, expected))
+ assert(matrixApproxEqual(defaultMat.asBreeze, expected))
+ assert(matrixApproxEqual(pearsonMat.asBreeze, expected))
}
test("corr(X) spearman") {
@@ -118,7 +118,7 @@ class CorrelationSuite extends SparkFunSuite with MLlibTestSparkContext with Log
(Double.NaN, Double.NaN, 1.00000000, Double.NaN),
(0.4000000, 0.9486833, Double.NaN, 1.0000000))
// scalastyle:on
- assert(matrixApproxEqual(spearmanMat.toBreeze, expected))
+ assert(matrixApproxEqual(spearmanMat.asBreeze, expected))
}
test("method identification") {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
index 0c6aabf1926e9b7e78d65b9fda5997cb2cc916d5..7b6bfee00cb1fb11800201c76359121db58544da 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala
@@ -53,13 +53,13 @@ class MLUtilsSuite extends SparkFunSuite with MLlibTestSparkContext {
val norm2 = Vectors.norm(v2, 2.0)
val v3 = Vectors.sparse(n, indices, indices.map(i => a(i) + 0.5))
val norm3 = Vectors.norm(v3, 2.0)
- val squaredDist = breezeSquaredDistance(v1.toBreeze, v2.toBreeze)
+ val squaredDist = breezeSquaredDistance(v1.asBreeze, v2.asBreeze)
val fastSquaredDist1 = fastSquaredDistance(v1, norm1, v2, norm2, precision)
assert((fastSquaredDist1 - squaredDist) <= precision * squaredDist, s"failed with m = $m")
val fastSquaredDist2 =
fastSquaredDistance(v1, norm1, Vectors.dense(v2.toArray), norm2, precision)
assert((fastSquaredDist2 - squaredDist) <= precision * squaredDist, s"failed with m = $m")
- val squaredDist2 = breezeSquaredDistance(v2.toBreeze, v3.toBreeze)
+ val squaredDist2 = breezeSquaredDistance(v2.asBreeze, v3.asBreeze)
val fastSquaredDist3 =
fastSquaredDistance(v2, norm2, v3, norm3, precision)
assert((fastSquaredDist3 - squaredDist2) <= precision * squaredDist2, s"failed with m = $m")
@@ -67,7 +67,7 @@ class MLUtilsSuite extends SparkFunSuite with MLlibTestSparkContext {
val v4 = Vectors.sparse(n, indices.slice(0, m - 10),
indices.map(i => a(i) + 0.5).slice(0, m - 10))
val norm4 = Vectors.norm(v4, 2.0)
- val squaredDist = breezeSquaredDistance(v2.toBreeze, v4.toBreeze)
+ val squaredDist = breezeSquaredDistance(v2.asBreeze, v4.asBreeze)
val fastSquaredDist =
fastSquaredDistance(v2, norm2, v4, norm4, precision)
assert((fastSquaredDist - squaredDist) <= precision * squaredDist, s"failed with m = $m")
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 73debe9da427732ba77bd7f40a037660dde1abb0..9d0d9b1be0771c52a21b357041fce7a5e3c195f8 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -775,6 +775,9 @@ object MimaExcludes {
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.annotation.AlphaComponent"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.annotation.Experimental"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.annotation.DeveloperApi")
+ ) ++ Seq(
+ ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.mllib.linalg.Vector.asBreeze"),
+ ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.mllib.linalg.Matrix.asBreeze")
)
case v if v.startsWith("1.6") =>
Seq(