K - Type of a key in upstream data.V - Type of a value in upstream data.public final class StandardScalerPreprocessor<K,V> extends Object implements Preprocessor<K,V>, DeployableObject
mean equal to 0
and variance equal to 1. From mathematical point of view it's the following function which is applied
to every element in a dataset:
a_i = (a_i - mean_i) / sigma_i for all i,
where i is a number of column, mean_i is the mean value this column and sigma_i is the
standard deviation in this column.| Constructor and Description |
|---|
StandardScalerPreprocessor(double[] means,
double[] sigmas,
Preprocessor<K,V> basePreprocessor)
Constructs a new instance of standardscaling preprocessor.
|
| Modifier and Type | Method and Description |
|---|---|
LabeledVector |
apply(K k,
V v)
Applies this preprocessor.
|
List<Object> |
getDependencies()
Returns dependencies of this object that can be object with class defined by client side and unknown for server.
|
double[] |
getMeans() |
double[] |
getSigmas() |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitmapandThenandThenpublic StandardScalerPreprocessor(double[] means,
double[] sigmas,
Preprocessor<K,V> basePreprocessor)
means - Means of each column.sigmas - Standard deviations in each column.basePreprocessor - Base preprocessor.public LabeledVector apply(K k, V v)
apply in interface BiFunction<K,V,LabeledVector>k - Key.v - Value.public double[] getMeans()
public double[] getSigmas()
public List<Object> getDependencies()
getDependencies in interface DeployableObject
GridGain In-Memory Computing Platform : ver. 8.9.26 Release Date : October 16 2025