# MaxAbScaler

MaxAbsScaler transforms a dataset of Vector rows, rescaling each feature to range \[-1, 1] by dividing through the maximum absolute value in each feature. It does not shift/center the data, and thus does not destroy any sparsity.

MaxAbsScaler computes summary statistics on a data set and produces a MaxAbsScalerModel. The model can then transform each feature individually to range \[-1, 1].

```
import org.apache.spark.ml.feature.MaxAbsScaler
import org.apache.spark.ml.linalg.Vectors
val dataFrame = spark.createDataFrame(Seq(
(0, Vectors.dense(1.0, 0.1, -8.0)),
(1, Vectors.dense(2.0, 1.0, -4.0)),
(2, Vectors.dense(4.0, 10.0, 8.0))
)).toDF("id", "features")
val scaler = new MaxAbsScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
// Compute summary statistics and generate MaxAbsScalerModel
val scalerModel = scaler.fit(dataFrame)
// rescale each feature to range [-1, 1]
val scaledData = scalerModel.transform(dataFrame)
scaledData.select("features", "scaledFeatures").show()

/*
Output:
+--------------+----------------+
|      features|  scaledFeatures|
+--------------+----------------+
|[1.0,0.1,-8.0]|[0.25,0.01,-1.0]|
|[2.0,1.0,-4.0]|  [0.5,0.1,-0.5]|
|[4.0,10.0,8.0]|   [1.0,1.0,1.0]|
+--------------+----------------+
*/
```


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