diff options
Diffstat (limited to 'mllib')
7 files changed, 8 insertions, 8 deletions
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 0ab4459bdb..61e355ab9f 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 @@ -686,7 +686,7 @@ object LogisticRegressionModel extends MLReadable[LogisticRegressionModel] { /** * MultiClassSummarizer computes the number of distinct labels and corresponding counts, * and validates the data to see if the labels used for k class multi-label classification - * are in the range of {0, 1, ..., k - 1} in a online fashion. + * are in the range of {0, 1, ..., k - 1} in an online fashion. * * Two MultilabelSummarizer can be merged together to have a statistical summary of the * corresponding joint dataset. @@ -923,7 +923,7 @@ class BinaryLogisticRegressionSummary private[classification] ( /** * LogisticAggregator computes the gradient and loss for binary logistic loss function, as used - * in binary classification for instances in sparse or dense vector in a online fashion. + * in binary classification for instances in sparse or dense vector in an online fashion. * * Note that multinomial logistic loss is not supported yet! * diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 82f2de7ccd..ecec61a72f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -790,7 +790,7 @@ trait Params extends Identifiable with Serializable { * :: DeveloperApi :: * Java-friendly wrapper for [[Params]]. * Java developers who need to extend [[Params]] should use this class instead. - * If you need to extend a abstract class which already extends [[Params]], then that abstract + * If you need to extend an abstract class which already extends [[Params]], then that abstract * class should be Java-friendly as well. */ @DeveloperApi 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 e63eb71080..00ef6ccc74 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 @@ -396,7 +396,7 @@ object AFTSurvivalRegressionModel extends MLReadable[AFTSurvivalRegressionModel] /** * AFTAggregator computes the gradient and loss for a AFT loss function, - * as used in AFT survival regression for samples in sparse or dense vector in a online fashion. + * as used in AFT survival regression for samples in sparse or dense vector in an online fashion. * * The loss function and likelihood function under the AFT model based on: * Lawless, J. F., Statistical Models and Methods for Lifetime Data, 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 37552194c5..4d66b0eb37 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 @@ -731,7 +731,7 @@ class LinearRegressionSummary private[regression] ( /** * LeastSquaresAggregator computes the gradient and loss for a Least-squared loss function, - * as used in linear regression for samples in sparse or dense vector in a online fashion. + * as used in linear regression for samples in sparse or dense vector in an online fashion. * * Two LeastSquaresAggregator can be merged together to have a summary of loss and gradient of * the corresponding joint dataset. diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala index b836d2a234..7d12f447f7 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala @@ -74,7 +74,7 @@ class ParamGridBuilder @Since("1.2.0") { } /** - * Adds a int param with multiple values. + * Adds an int param with multiple values. */ @Since("1.2.0") def addGrid(param: IntParam, values: Array[Int]): this.type = { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala index 1d2d777c00..b0fa287473 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPTree.scala @@ -126,7 +126,7 @@ private[fpm] object FPTree { def isRoot: Boolean = parent == null } - /** Summary of a item in an FP-Tree. */ + /** Summary of an item in an FP-Tree. */ private class Summary[T] extends Serializable { var count: Long = 0L val nodes: ListBuffer[Node[T]] = ListBuffer.empty diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala index 98404be260..d4de0fd7d5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala @@ -24,7 +24,7 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors} * :: DeveloperApi :: * MultivariateOnlineSummarizer implements [[MultivariateStatisticalSummary]] to compute the mean, * variance, minimum, maximum, counts, and nonzero counts for instances in sparse or dense vector - * format in a online fashion. + * format in an online fashion. * * Two MultivariateOnlineSummarizer can be merged together to have a statistical summary of * the corresponding joint dataset. |