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remove the empty line after :: DeveloperApi/Experimental ::
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mengxr committed Apr 9, 2014
1 parent bde9cc1 commit 9c35bdc
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Showing 33 changed files with 21 additions and 71 deletions.
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Expand Up @@ -30,7 +30,6 @@ import org.apache.spark.rdd.RDD

/**
* :: DeveloperApi ::
*
* The Java stubs necessary for the Python mllib bindings.
*/
@DeveloperApi
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Expand Up @@ -29,7 +29,6 @@ import org.apache.spark.rdd.RDD

/**
* :: Experimental ::
*
* Model for Naive Bayes Classifiers.
*
* @param labels list of labels
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Expand Up @@ -78,7 +78,6 @@ class KMeans private (

/**
* :: Experimental ::
*
* Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm
* this many times with random starting conditions (configured by the initialization mode), then
* return the best clustering found over any run. Default: 1.
Expand Down Expand Up @@ -398,9 +397,6 @@ object KMeans {
MLUtils.fastSquaredDistance(v1.vector, v1.norm, v2.vector, v2.norm)
}

/**
* :: Experimental ::
*/
@Experimental
def main(args: Array[String]) {
if (args.length < 4) {
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Expand Up @@ -34,7 +34,6 @@ case class MatrixEntry(i: Long, j: Long, value: Double)

/**
* :: Experimental ::
*
* Represents a matrix in coordinate format.
*
* @param entries matrix entries
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Expand Up @@ -26,15 +26,13 @@ import org.apache.spark.mllib.linalg.SingularValueDecomposition

/**
* :: Experimental ::
*
* Represents a row of [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]].
*/
@Experimental
case class IndexedRow(index: Long, vector: Vector)

/**
* :: Experimental ::
*
* Represents a row-oriented [[org.apache.spark.mllib.linalg.distributed.DistributedMatrix]] with
* indexed rows.
*
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Expand Up @@ -30,7 +30,6 @@ import org.apache.spark.Logging

/**
* :: Experimental ::
*
* Represents a row-oriented distributed Matrix with no meaningful row indices.
*
* @param rows rows stored as an RDD[Vector]
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Expand Up @@ -24,7 +24,6 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}

/**
* :: DeveloperApi ::
*
* Class used to compute the gradient for a loss function, given a single data point.
*/
@DeveloperApi
Expand Down Expand Up @@ -56,7 +55,6 @@ abstract class Gradient extends Serializable {

/**
* :: DeveloperApi ::
*
* Compute gradient and loss for a logistic loss function, as used in binary classification.
* See also the documentation for the precise formulation.
*/
Expand Down Expand Up @@ -100,7 +98,6 @@ class LogisticGradient extends Gradient {

/**
* :: DeveloperApi ::
*
* Compute gradient and loss for a Least-squared loss function, as used in linear regression.
* This is correct for the averaged least squares loss function (mean squared error)
* L = 1/n ||A weights-y||^2
Expand Down Expand Up @@ -135,7 +132,6 @@ class LeastSquaresGradient extends Gradient {

/**
* :: DeveloperApi ::
*
* Compute gradient and loss for a Hinge loss function, as used in SVM binary classification.
* See also the documentation for the precise formulation.
* NOTE: This assumes that the labels are {0,1}
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Expand Up @@ -28,7 +28,6 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}

/**
* :: DeveloperApi ::
*
* Class used to solve an optimization problem using Gradient Descent.
* @param gradient Gradient function to be used.
* @param updater Updater to be used to update weights after every iteration.
Expand Down Expand Up @@ -113,7 +112,6 @@ class GradientDescent(private var gradient: Gradient, private var updater: Updat

/**
* :: DeveloperApi ::
*
* Top-level method to run gradient descent.
*/
@DeveloperApi
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Expand Up @@ -24,7 +24,6 @@ import org.apache.spark.mllib.linalg.Vector

/**
* :: DeveloperApi ::
*
* Trait for optimization problem solvers.
*/
@DeveloperApi
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Expand Up @@ -26,7 +26,6 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}

/**
* :: DeveloperApi ::
*
* Class used to perform steps (weight update) using Gradient Descent methods.
*
* For general minimization problems, or for regularized problems of the form
Expand Down Expand Up @@ -64,7 +63,6 @@ abstract class Updater extends Serializable {

/**
* :: DeveloperApi ::
*
* A simple updater for gradient descent *without* any regularization.
* Uses a step-size decreasing with the square root of the number of iterations.
*/
Expand All @@ -86,7 +84,6 @@ class SimpleUpdater extends Updater {

/**
* :: DeveloperApi ::
*
* Updater for L1 regularized problems.
* R(w) = ||w||_1
* Uses a step-size decreasing with the square root of the number of iterations.
Expand Down Expand Up @@ -131,7 +128,6 @@ class L1Updater extends Updater {

/**
* :: DeveloperApi ::
*
* Updater for L2 regularized problems.
* R(w) = 1/2 ||w||^2
* Uses a step-size decreasing with the square root of the number of iterations.
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Expand Up @@ -140,7 +140,6 @@ class ALS private (

/**
* :: Experimental ::
*
* Sets the constant used in computing confidence in implicit ALS. Default: 1.0.
*/
@Experimental
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Expand Up @@ -70,7 +70,6 @@ class MatrixFactorizationModel(

/**
* :: DeveloperApi ::
*
* Predict the rating of many users for many products.
* This is a Java stub for python predictAll()
*
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Expand Up @@ -103,7 +103,6 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]

/**
* :: Experimental ::
*
* Set if the algorithm should validate data before training. Default true.
*/
@Experimental
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Expand Up @@ -35,7 +35,6 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors}

/**
* :: Experimental ::
*
* A class that implements a decision tree algorithm for classification and regression. It
* supports both continuous and categorical features.
* @param strategy The configuration parameters for the tree algorithm which specify the type
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Expand Up @@ -21,7 +21,6 @@ import org.apache.spark.annotation.Experimental

/**
* :: Experimental ::
*
* Enum to select the algorithm for the decision tree
*/
@Experimental
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Expand Up @@ -21,7 +21,6 @@ import org.apache.spark.annotation.Experimental

/**
* :: Experimental ::
*
* Enum to describe whether a feature is "continuous" or "categorical"
*/
@Experimental
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Expand Up @@ -21,7 +21,6 @@ import org.apache.spark.annotation.Experimental

/**
* :: Experimental ::
*
* Enum for selecting the quantile calculation strategy
*/
@Experimental
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Expand Up @@ -24,7 +24,6 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._

/**
* :: Experimental ::
*
* Stores all the configuration options for tree construction
* @param algo classification or regression
* @param impurity criterion used for information gain calculation
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Expand Up @@ -21,7 +21,6 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}

/**
* :: Experimental ::
*
* Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
* binary classification.
*/
Expand All @@ -32,7 +31,6 @@ object Entropy extends Impurity {

/**
* :: DeveloperApi ::
*
* entropy calculation
* @param c0 count of instances with label 0
* @param c1 count of instances with label 1
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Expand Up @@ -21,7 +21,6 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}

/**
* :: Experimental ::
*
* Class for calculating the
* [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
* during binary classification.
Expand All @@ -31,7 +30,6 @@ object Gini extends Impurity {

/**
* :: DeveloperApi ::
*
* Gini coefficient calculation
* @param c0 count of instances with label 0
* @param c1 count of instances with label 1
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Expand Up @@ -21,15 +21,13 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}

/**
* :: Experimental ::
*
* Trait for calculating information gain.
*/
@Experimental
trait Impurity extends Serializable {

/**
* :: DeveloperApi ::
*
* information calculation for binary classification
* @param c0 count of instances with label 0
* @param c1 count of instances with label 1
Expand All @@ -40,7 +38,6 @@ trait Impurity extends Serializable {

/**
* :: DeveloperApi ::
*
* information calculation for regression
* @param count number of instances
* @param sum sum of labels
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Expand Up @@ -21,7 +21,6 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}

/**
* :: Experimental ::
*
* Class for calculating variance during regression
*/
@Experimental
Expand All @@ -31,7 +30,6 @@ object Variance extends Impurity {

/**
* :: DeveloperApi ::
*
* variance calculation
* @param count number of instances
* @param sum sum of labels
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Expand Up @@ -24,7 +24,6 @@ import org.apache.spark.mllib.linalg.Vector

/**
* :: Experimental ::
*
* Model to store the decision tree parameters
* @param topNode root node
* @param algo algorithm type -- classification or regression
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Expand Up @@ -21,7 +21,6 @@ import org.apache.spark.annotation.DeveloperApi

/**
* :: DeveloperApi ::
*
* Information gain statistics for each split
* @param gain information gain value
* @param impurity current node impurity
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Expand Up @@ -24,7 +24,6 @@ import org.apache.spark.mllib.linalg.Vector

/**
* :: DeveloperApi ::
*
* Node in a decision tree
* @param id integer node id
* @param predict predicted value at the node
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Expand Up @@ -22,7 +22,6 @@ import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType

/**
* :: DeveloperApi ::
*
* Split applied to a feature
* @param feature feature index
* @param threshold threshold for continuous feature
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Expand Up @@ -24,7 +24,6 @@ import org.apache.spark.mllib.regression.LabeledPoint

/**
* :: DeveloperApi ::
*
* A collection of methods used to validate data before applying ML algorithms.
*/
@DeveloperApi
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Expand Up @@ -25,7 +25,6 @@ import org.apache.spark.rdd.RDD

/**
* :: DeveloperApi ::
*
* Generate test data for KMeans. This class first chooses k cluster centers
* from a d-dimensional Gaussian distribution scaled by factor r and then creates a Gaussian
* cluster with scale 1 around each center.
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Expand Up @@ -30,7 +30,6 @@ import org.apache.spark.mllib.regression.LabeledPoint

/**
* :: DeveloperApi ::
*
* Generate sample data used for Linear Data. This class generates
* uniformly random values for every feature and adds Gaussian noise with mean `eps` to the
* response variable `Y`.
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Expand Up @@ -27,7 +27,6 @@ import org.apache.spark.mllib.linalg.Vectors

/**
* :: DeveloperApi ::
*
* Generate test data for LogisticRegression. This class chooses positive labels
* with probability `probOne` and scales features for positive examples by `eps`.
*/
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