diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md index 4288ebc03895c..a69e41e2a1936 100644 --- a/docs/mllib-feature-extraction.md +++ b/docs/mllib-feature-extraction.md @@ -405,7 +405,7 @@ Note that the user can also construct a `ChiSqSelectorModel` by hand by providin #### Example -The following example shows the basic use of ChiSqSelector. The data set used has a feature matrix consisting of greyscale values that vary from 0 - 255 for each feature. +The following example shows the basic use of ChiSqSelector. The data set used has a feature matrix consisting of greyscale values that vary from 0 to 255 for each feature.
@@ -423,7 +423,7 @@ val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") val discretizedData = data.map { lp => LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => (x / 16).floor } ) ) } -// Create ChiSqSelector that will select top 50 of 692 features +// Create ChiSqSelector that will select top 50 of 692 features val selector = new ChiSqSelector(50) // Create ChiSqSelector model (selecting features) val transformer = selector.fit(discretizedData) @@ -459,13 +459,13 @@ JavaRDD discretizedData = points.map( public LabeledPoint call(LabeledPoint lp) { final double[] discretizedFeatures = new double[lp.features().size()]; for (int i = 0; i < lp.features().size(); ++i) { - discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16); + discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16); } return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures)); } }); -// Create ChiSqSelector that will select top 50 of 692 features +// Create ChiSqSelector that will select top 50 of 692 features ChiSqSelector selector = new ChiSqSelector(50); // Create ChiSqSelector model (selecting features) final ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd());