class NormContinuous extends NumericFieldExpression
Normalization provides a basic framework for mapping input values to specific value ranges, usually the numeric range [0 .. 1]. Normalization is used, e.g., in neural networks and clustering models.
Defines how to normalize an input field by piecewise linear interpolation. The mapMissingTo attribute defines the value the output is to take if the input is missing. If the mapMissingTo attribute is not specified, then missing input values produce a missing result.
Linear Supertypes
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- NormContinuous
- NumericFieldExpression
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- PmmlElement
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Visibility
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Instance Constructors
- new NormContinuous(linearNorms: Array[LinearNorm], field: Field, mapMissingTo: Option[Double], outliers: OutlierTreatmentMethod = OutlierTreatmentMethod.asIs)
Value Members
- final def !=(arg0: Any): Boolean
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- def child: FieldRef
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- def clone(): AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- def deeval(input: Any): Double
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- NormContinuous → Expression
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
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- def eval(series: Series): Double
Returns the result of evaluating this expression on a given input Series
Returns the result of evaluating this expression on a given input Series
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- NormContinuous → NumericFieldExpression → FieldExpression → Expression → Evaluator
- def eval(value: Any): Any
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- Expression
- def extensions: Seq[Extension]
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- val field: Field
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- NormContinuous → FieldExpression
- def finalize(): Unit
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- def getDataField: Option[Field]
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- def hashCode(): Int
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- final def isInstanceOf[T0]: Boolean
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- val linearNorms: Array[LinearNorm]
- val mapMissingTo: Option[Double]
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- final def notifyAll(): Unit
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- val outliers: OutlierTreatmentMethod
- final def synchronized[T0](arg0: => T0): T0
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