object Normalizer extends SettingsBuilder with Serializable

Transform vector features by normalizing each vector to have unit norm. Parameter p specifies the p-norm used for normalization (default 2).

Missing values are transformed to zero vectors.

When using aggregated feature summary from a previous session, vectors of different dimensions are transformed to zero vectors and FeatureRejection.WrongDimension rejections are reported.

Source
Normalizer.scala
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  4. def apply(name: String, p: Double = 2.0, expectedLength: Int = 0): Transformer[Array[Double], Int, Int]

    Create a new Normalizer instance.

    Create a new Normalizer instance.

    p

    normalization in Lp space, must be greater than or equal to 1.0

    expectedLength

    expected length of the input vectors, or 0 to infer from data

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  10. def fromSettings(setting: Settings): Transformer[Array[Double], Int, Int]

    Create a new OneHotEncoder from a settings object

    Create a new OneHotEncoder from a settings object

    setting

    Settings object

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    NormalizerSettingsBuilder
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