com.alpine.model.pack.ml

LinearRegressionTransformer

class LinearRegressionTransformer extends RegressionTransformer

Applies a Linear Regression model specified by the coefficients and intercept to a row of numeric data.

Note that in the input row is wrapped in CastedDoubleSeq, so the input elements must be castable as java.lang.Number.

Linear Supertypes
RegressionTransformer, MLTransformer[RealResult], Transformer, Serializable, Serializable, AnyRef, Any
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Inherited
  1. LinearRegressionTransformer
  2. RegressionTransformer
  3. MLTransformer
  4. Transformer
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
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Instance Constructors

  1. new LinearRegressionTransformer(coefficients: Seq[Double], intercept: Double = 0)

Type Members

  1. type Row = Seq[Any]

    Shorthand for the input / output type of the Row) method.

    Shorthand for the input / output type of the Row) method. Equivalent to Seq[Any].

    Definition Classes
    Transformer

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. def allowNullValues: Boolean

    Allows the transformer to specify if the apply method can handle null values in the input row.

    Allows the transformer to specify if the apply method can handle null values in the input row.

    e.g. a Null value replacement transformer, or a Naive Bayes transformer would naturally handle null values, but a Linear Regression transformer would not.

    Default value is false.

    returns

    Boolean indicating tolerance for null values in input.

    Definition Classes
    Transformer
  7. def apply(row: Row): Row

    This method should be implemented with speed and garbage collection in mind, as it called once per row on potentially huge data-sets.

    This method should be implemented with speed and garbage collection in mind, as it called once per row on potentially huge data-sets.

    This method is not required to be thread-safe.

    row

    The row of input to be scored.

    returns

    The result from applying the trained model to the row.

    Definition Classes
    RegressionTransformerTransformer
  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  16. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  17. final def notify(): Unit

    Definition Classes
    AnyRef
  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  19. def predict(row: Row): Double

  20. def score(row: Row): RealResult

    Definition Classes
    RegressionTransformerMLTransformer
  21. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  22. def toString(): String

    Definition Classes
    AnyRef → Any
  23. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  24. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  25. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from RegressionTransformer

Inherited from MLTransformer[RealResult]

Inherited from Transformer

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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