diff options
Diffstat (limited to 'mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala | 10 |
1 files changed, 7 insertions, 3 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala index 068f11a2a5..9f3d2ca6db 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala @@ -76,11 +76,15 @@ private[feature] trait MinMaxScalerParams extends Params with HasInputCol with H /** * Rescale each feature individually to a common range [min, max] linearly using column summary * statistics, which is also known as min-max normalization or Rescaling. The rescaled value for - * feature E is calculated as, + * feature E is calculated as: * - * `Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min` + * <p><blockquote> + * $$ + * Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min + * $$ + * </blockquote></p> * - * For the case `E_{max} == E_{min}`, `Rescaled(e_i) = 0.5 * (max + min)`. + * For the case $E_{max} == E_{min}$, $Rescaled(e_i) = 0.5 * (max + min)$. * Note that since zero values will probably be transformed to non-zero values, output of the * transformer will be DenseVector even for sparse input. */ |