diff options
| -rw-r--r-- | docs/mllib-basics.md | 8 | ||||
| -rw-r--r-- | docs/mllib-linear-methods.md | 2 |
2 files changed, 5 insertions, 5 deletions
diff --git a/docs/mllib-basics.md b/docs/mllib-basics.md index 704308802d..aa9321a547 100644 --- a/docs/mllib-basics.md +++ b/docs/mllib-basics.md @@ -184,7 +184,7 @@ After loading, the feature indices are converted to zero-based. <div class="codetabs"> <div data-lang="scala" markdown="1"> -[`MLUtils.loadLibSVMData`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training +[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training examples stored in LIBSVM format. {% highlight scala %} @@ -192,12 +192,12 @@ import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD -val training: RDD[LabeledPoint] = MLUtils.loadLibSVMData(sc, "mllib/data/sample_libsvm_data.txt") +val training: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt") {% endhighlight %} </div> <div data-lang="java" markdown="1"> -[`MLUtils.loadLibSVMData`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training +[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training examples stored in LIBSVM format. {% highlight java %} @@ -205,7 +205,7 @@ import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.rdd.RDDimport; -RDD<LabeledPoint> training = MLUtils.loadLibSVMData(jsc, "mllib/data/sample_libsvm_data.txt"); +RDD<LabeledPoint> training = MLUtils.loadLibSVMFile(jsc, "mllib/data/sample_libsvm_data.txt"); {% endhighlight %} </div> </div> diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index 40b7a7f807..eff617d864 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -186,7 +186,7 @@ import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.MLUtils // Load training data in LIBSVM format. -val data = MLUtils.loadLibSVMData(sc, "mllib/data/sample_libsvm_data.txt") +val data = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt") // Split data into training (60%) and test (40%). val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L) |