api/mllib -> api/scala

mengxr · mengxr · commit 35bdeb93f171 · 2014-05-18T11:27:27.000-07:00
diff --git a/docs/mllib-basics.md b/docs/mllib-basics.md
@@ -26,11 +26,11 @@ of the vector.
 <div data-lang="scala" markdown="1">
 
 The base class of local vectors is
-[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two
-implementations: [`DenseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseVector) and
-[`SparseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.SparseVector).  We recommend
+[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two
+implementations: [`DenseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseVector) and
+[`SparseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector).  We recommend
 using the factory methods implemented in
-[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors.
+[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors.
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -53,11 +53,11 @@ Scala imports `scala.collection.immutable.Vector` by default, so you have to imp
 <div data-lang="java" markdown="1">
 
 The base class of local vectors is
-[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two
-implementations: [`DenseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseVector) and
-[`SparseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.SparseVector).  We recommend
+[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two
+implementations: [`DenseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseVector) and
+[`SparseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector).  We recommend
 using the factory methods implemented in
-[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors.
+[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors.
 
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Vector;
@@ -117,7 +117,7 @@ For multiclass classification, labels should be class indices staring from zero:
 <div data-lang="scala" markdown="1">
 
 A labeled point is represented by the case class
-[`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint).
+[`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint).
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.Vectors
@@ -134,7 +134,7 @@ val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))
 <div data-lang="java" markdown="1">
 
 A labeled point is represented by
-[`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint).
+[`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint).
 
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Vectors;
@@ -184,7 +184,7 @@ After loading, the feature indices are converted to zero-based.
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
+[`MLUtils.loadLibSVMFile`](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
 examples stored in LIBSVM format.
 
 {% highlight scala %}
@@ -197,7 +197,7 @@ val training: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_
 </div>
 
 <div data-lang="java" markdown="1">
-[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
+[`MLUtils.loadLibSVMFile`](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) reads training
 examples stored in LIBSVM format.
 
 {% highlight java %}
@@ -227,10 +227,10 @@ We are going to add sparse matrix in the next release.
 <div data-lang="scala" markdown="1">
 
 The base class of local matrices is
-[`Matrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
-implementation: [`DenseMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
+[`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
+implementation: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
 Sparse matrix will be added in the next release.  We recommend using the factory methods implemented
-in [`Matrices`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrices) to create local
+in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices) to create local
 matrices.
 
 {% highlight scala %}
@@ -244,10 +244,10 @@ val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
 <div data-lang="java" markdown="1">
 
 The base class of local matrices is
-[`Matrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
-implementation: [`DenseMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
+[`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
+implementation: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
 Sparse matrix will be added in the next release.  We recommend using the factory methods implemented
-in [`Matrices`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrices) to create local
+in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices) to create local
 matrices.
 
 {% highlight java %}
@@ -284,7 +284,7 @@ limited by the integer range but it should be much smaller in practice.
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be
+A [`RowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be
 created from an `RDD[Vector]` instance.  Then we can compute its column summary statistics.
 
 {% highlight scala %}
@@ -303,7 +303,7 @@ val n = mat.numCols()
 
 <div data-lang="java" markdown="1">
 
-A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be
+A [`RowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be
 created from a `JavaRDD<Vector>` instance.  Then we can compute its column summary statistics.
 
 {% highlight java %}
@@ -334,7 +334,7 @@ which could be faster if the rows are sparse.
 <div data-lang="scala" markdown="1">
 
 `RowMatrix#computeColumnSummaryStatistics` returns an instance of
-[`MultivariateStatisticalSummary`](api/mllib/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary),
+[`MultivariateStatisticalSummary`](api/scala/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary),
 which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
 total count.
 
@@ -366,9 +366,9 @@ an RDD of indexed rows, which each row is represented by its index (long-typed)
 <div data-lang="scala" markdown="1">
 
 An
-[`IndexedRowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix)
+[`IndexedRowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix)
 can be created from an `RDD[IndexedRow]` instance, where
-[`IndexedRow`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a
+[`IndexedRow`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a
 wrapper over `(Long, Vector)`.  An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping
 its row indices.
 
@@ -391,9 +391,9 @@ val rowMat: RowMatrix = mat.toRowMatrix()
 <div data-lang="java" markdown="1">
 
 An
-[`IndexedRowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix)
+[`IndexedRowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix)
 can be created from an `JavaRDD<IndexedRow>` instance, where
-[`IndexedRow`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a
+[`IndexedRow`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a
 wrapper over `(long, Vector)`.  An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping
 its row indices.
 
@@ -427,9 +427,9 @@ dimensions of the matrix are huge and the matrix is very sparse.
 <div data-lang="scala" markdown="1">
 
 A
-[`CoordinateMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix)
+[`CoordinateMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix)
 can be created from an `RDD[MatrixEntry]` instance, where
-[`MatrixEntry`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a
+[`MatrixEntry`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a
 wrapper over `(Long, Long, Double)`.  A `CoordinateMatrix` can be converted to a `IndexedRowMatrix`
 with sparse rows by calling `toIndexedRowMatrix`.  In this release, we do not provide other
 computation for `CoordinateMatrix`.
@@ -453,9 +453,9 @@ val indexedRowMatrix = mat.toIndexedRowMatrix()
 <div data-lang="java" markdown="1">
 
 A
-[`CoordinateMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix)
+[`CoordinateMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix)
 can be created from a `JavaRDD<MatrixEntry>` instance, where
-[`MatrixEntry`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a
+[`MatrixEntry`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a
 wrapper over `(long, long, double)`.  A `CoordinateMatrix` can be converted to a `IndexedRowMatrix`
 with sparse rows by calling `toIndexedRowMatrix`.
 
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
@@ -40,7 +40,7 @@ a given dataset, the algorithm returns the best clustering result).
 Following code snippets can be executed in `spark-shell`.
 
 In the following example after loading and parsing data, we use the
-[`KMeans`](api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data
+[`KMeans`](api/scala/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data
 into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
 Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
 optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
@@ -48,7 +48,7 @@ user for an item.
 
 <div data-lang="scala" markdown="1">
 In the following example we load rating data. Each row consists of a user, a product and a rating.
-We use the default [ALS.train()](api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS$) 
+We use the default [ALS.train()](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS$) 
 method which assumes ratings are explicit. We evaluate the
 recommendation model by measuring the Mean Squared Error of rating prediction.
 
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
@@ -28,7 +28,7 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv
   * limited-memory BFGS (L-BFGS)
 
 MLlib is a new component under active development.
-The APIs marked `Experimental`/`DeveloperApi` may change in the future releases, 
+The APIs marked `Experimental`/`DeveloperApi` may change in future releases, 
 and we will provide migration guide between releases.
 
 ## Dependencies
@@ -62,9 +62,9 @@ take advantage of sparsity in both storage and computation.
 <div data-lang="scala" markdown="1">
 
 We used to represent a feature vector by `Array[Double]`, which is replaced by
-[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
+[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
 to accept `RDD[Array[Double]]` now take
-`RDD[Vector]`. [`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint)
+`RDD[Vector]`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint)
 is now a wrapper of `(Double, Vector)` instead of `(Double, Array[Double])`. Converting
 `Array[Double]` to `Vector` is straightforward:
 
@@ -75,7 +75,7 @@ val array: Array[Double] = ... // a double array
 val vector: Vector = Vectors.dense(array) // a dense vector
 {% endhighlight %}
 
-[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.
+[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.
 
 *Note*. Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`.
 
@@ -84,9 +84,9 @@ val vector: Vector = Vectors.dense(array) // a dense vector
 <div data-lang="java" markdown="1">
 
 We used to represent a feature vector by `double[]`, which is replaced by
-[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
+[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
 to accept `RDD<double[]>` now take
-`RDD<Vector>`. [`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint)
+`RDD<Vector>`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint)
 is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to
 `Vector` is straightforward:
 
@@ -98,7 +98,7 @@ double[] array = ... // a double array
 Vector vector = Vectors.dense(array); // a dense vector
 {% endhighlight %}
 
-[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to
+[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to
 create sparse vectors.
 
 </div>
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
@@ -233,7 +233,7 @@ val modelL1 = svmAlg.run(training)
 {% endhighlight %}
 
 Similarly, you can use replace `SVMWithSGD` by
-[`LogisticRegressionWithSGD`](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
+[`LogisticRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
 
 </div>
 
@@ -328,8 +328,8 @@ println("training Mean Squared Error = " + MSE)
 {% endhighlight %}
 
 Similarly you can use
-[`RidgeRegressionWithSGD`](api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD)
-and [`LassoWithSGD`](api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD).
+[`RidgeRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD)
+and [`LassoWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.LassoWithSGD).
 
 </div>
 
@@ -380,11 +380,11 @@ all three possible regularizations (none, L1 or L2).
 
 Algorithms are all implemented in Scala:
 
-* [SVMWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD)
-* [LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD)
-* [LinearRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD)
-* [RidgeRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD)
-* [LassoWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD)
+* [SVMWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.SVMWithSGD)
+* [LogisticRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD)
+* [LinearRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD)
+* [RidgeRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD)
+* [LassoWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.LassoWithSGD)
 
 Python calls the Scala implementation via
-[PythonMLLibAPI](api/mllib/index.html#org.apache.spark.mllib.api.python.PythonMLLibAPI).
+[PythonMLLibAPI](api/scala/index.html#org.apache.spark.mllib.api.python.PythonMLLibAPI).
diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md
@@ -27,11 +27,11 @@ sparsity. Since the training data is only used once, it is not necessary to cach
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
+[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
 multinomial naive Bayes. It takes an RDD of
-[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional
+[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional
 smoothing parameter `lambda` as input, and output a
-[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
+[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
 can be used for evaluation and prediction.
 
 {% highlight scala %}
@@ -59,11 +59,11 @@ val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test
 
 <div data-lang="java" markdown="1">
 
-[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
+[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
 multinomial naive Bayes. It takes a Scala RDD of
-[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an
+[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an
 optionally smoothing parameter `lambda` as input, and output a
-[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
+[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
 can be used for evaluation and prediction.
 
 {% highlight java %}
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
@@ -170,17 +170,17 @@ each iteration, to compute the gradient direction.
 
 Available algorithms for gradient descent:
 
-* [GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
+* [GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent)
 
 ### L-BFGS
 L-BFGS is currently only a low-level optimization primitive in `MLlib`. If you want to use L-BFGS in various 
 ML algorithms such as Linear Regression, and Logistic Regression, you have to pass the gradient of objective
 function, and updater into optimizer yourself instead of using the training APIs like 
-[LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
+[LogisticRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
 See the example below. It will be addressed in the next release. 
 
 The L1 regularization by using 
-[L1Updater](api/mllib/index.html#org.apache.spark.mllib.optimization.L1Updater) will not work since the 
+[L1Updater](api/scala/index.html#org.apache.spark.mllib.optimization.L1Updater) will not work since the 
 soft-thresholding logic in L1Updater is designed for gradient descent. See the developer's note.
 
 The L-BFGS method
@@ -274,4 +274,4 @@ the actual gradient descent step. However, we're able to take the gradient and
 loss of objective function of regularization for L-BFGS by ignoring the part of logic
 only for gradient decent such as adaptive step size stuff. We will refactorize
 this into regularizer to replace updater to separate the logic between 
-regularization and step update later. 
+regularization and step update later. 
