LinearDiscriminantAnalysis

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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8. def computeLDA(dataAndLabels: Array[(Int, DenseVector[Double])]): LinearMapper

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

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10. def equals(arg0: Any): Boolean

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11. def finalize(): Unit

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12. def fit(data: RDD[DenseVector[Double]], labels: RDD[Int]): LinearMapper

    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

    LinearDiscriminantAnalysis → LabelEstimator

13. final def getClass(): Class[_]

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14. def hashCode(): Int

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15. final def isInstanceOf[T0]: Boolean

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16. def label: String

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17. final def ne(arg0: AnyRef): Boolean

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18. final def notify(): Unit

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19. final def notifyAll(): Unit

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20. final def synchronized[T0](arg0: ⇒ T0): T0

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21. def toString(): String

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22. final def wait(): Unit

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23. final def wait(arg0: Long, arg1: Int): Unit

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24. final def wait(arg0: Long): Unit

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25. def withData(data: RDD[DenseVector[Double]], labels: RDD[Int]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline from a single label estimator and training data.

    Constructs a pipeline from a single label estimator and training data. Equivalent to Pipeline() andThen (estimator, data, labels)

    data

    The training data

    labels

    The training labels

    Definition Classes

    LabelEstimator