keystoneml.nodes.learning

LinearDiscriminantAnalysis

class LinearDiscriminantAnalysis extends LabelEstimator[DenseVector[Double], DenseVector[Double], Int]

An Estimator that fits Linear Discriminant Analysis (currently not calculated in a distributed fashion), and returns a transformer that projects into the new space

Solves multi-class LDA via Eigenvector decomposition

Linear Supertypes
LabelEstimator[DenseVector[Double], DenseVector[Double], Int], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. LinearDiscriminantAnalysis
  2. LabelEstimator
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Instance Constructors

  1. new LinearDiscriminantAnalysis(numDimensions: Int)

    numDimensions

    number of output dimensions to project to

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. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def computeLDA(dataAndLabels: Array[(Int, DenseVector[Double])]): LinearMapper[DenseVector[Double]]

  9. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  11. def execute(deps: Seq[Expression]): TransformerExpression

    Definition Classes
    EstimatorOperator → Operator
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. def fit(data: RDD[DenseVector[Double]], labels: RDD[Int]): LinearMapper[DenseVector[Double]]

    Currently this method works only on data that fits in local memory.

    Currently this method works only on data that fits in local memory. Hard limit of up to ~4B bytes of feature data due to max Java array length

    Solves multi-class LDA via Eigenvector decomposition

    "multi-class Linear Discriminant Analysis" or "Multiple Discriminant Analysis" by C. R. Rao in 1948 (The utilization of multiple measurements in problems of biological classification) http://www.jstor.org/discover/10.2307/2983775?uid=3739560&uid=2&uid=4&uid=3739256&sid=21106766791933

    Python implementation reference at: http://sebastianraschka.com/Articles/2014_python_lda.html

    data

    to train on.

    labels

    Input class labels.

    returns

    A PipelineNode which can be called on new data.

    Definition Classes
    LinearDiscriminantAnalysisLabelEstimator
  14. final def getClass(): Class[_]

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

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

    Definition Classes
    Any
  17. def label: String

    Definition Classes
    Operator
  18. final def ne(arg0: AnyRef): Boolean

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

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

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

    Definition Classes
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  22. def toString(): String

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

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  26. final def withData(data: PipelineDataset[DenseVector[Double]], labels: PipelineDataset[Int]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator
  27. final def withData(data: RDD[DenseVector[Double]], labels: RDD[Int]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator
  28. final def withData(data: PipelineDataset[DenseVector[Double]], labels: RDD[Int]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator
  29. final def withData(data: RDD[DenseVector[Double]], labels: PipelineDataset[Int]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

Inherited from LabelEstimator[DenseVector[Double], DenseVector[Double], Int]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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