Stats

Value Members

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. def aboutEq(a: DenseMatrix[Double], b: DenseMatrix[Double]): Boolean

7. def aboutEq(a: DenseMatrix[Double], b: DenseMatrix[Double], thresh: Double): Boolean

   Compares two matrices for approximate equality.

   Compares two matrices for approximate equality.

   a

   A matrix.

   b

   A second matrix.

   thresh

   equality threshold

   returns

   True iff all corresponding elements of the two matrices are within thresh of each other.

8. def aboutEq(as: DenseVector[Double], bs: DenseVector[Double]): Boolean

9. def aboutEq(as: DenseVector[Double], bs: DenseVector[Double], thresh: Double): Boolean

   Compares two arrays for approximate equality.

   Compares two arrays for approximate equality. Modify margin by setting Stats.thresh.

   as

   A array of numbers.

   bs

   A second array of numbers.

   thresh

   equality threshold

   returns

   True if the two numbers are within thresh of each other.

10. def aboutEq(a: Double, b: Double): Boolean

11. def aboutEq(a: Double, b: Double, thresh: Double): Boolean

    Compares two numbers for approximate equality

    Compares two numbers for approximate equality

    a

    A number.

    b

    A second number.

    thresh

    equality threshold

    returns

    True if the two numbers are within thresh of each other.

12. final def asInstanceOf[T0]: T0

    Definition Classes

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13. def classificationError(predictions: RDD[DenseVector[Double]], actuals: RDD[DenseVector[Double]], k: Int = 1): Double

    Computes top-k classification error based on a distribution of predictions.

    Computes top-k classification error based on a distribution of predictions.

    predictions

    Distribution of predictions.

    actuals

    Distribution of actuals.

    k

    How many classes to include.

    returns

    The error percent.

14. def clone(): AnyRef

    Attributes

    protected[java.lang]

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

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16. def equals(arg0: Any): Boolean

    Definition Classes

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17. def finalize(): Unit

    Attributes

    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes

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19. def getErrPercent(predicted: RDD[Array[Int]], actual: RDD[Array[Int]], num: Long): Double

    Computes error percent based on array of predicted labels and array of actual labels.

    Computes error percent based on array of predicted labels and array of actual labels.

    predicted

    The predicted labels.

    actual

    The actual labels.

    num

    Total number of examples.

    returns

    Error percent.

20. def hashCode(): Int

    Definition Classes

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21. final def isInstanceOf[T0]: Boolean

    Definition Classes

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

    Definition Classes

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23. def normalizeRows(mat: DenseMatrix[Double], alpha: Double = 1.0): DenseMatrix[Double]

    Given a local matrix, compute the mean and variance of each row.

    Given a local matrix, compute the mean and variance of each row. Subtract the row mean from each row and divide by the sqrt(variance + alpha).

    mat

    Input matrix.

    alpha

    Alpha for the denominator.

    returns

    Normalized Matrix.

24. final def notify(): Unit

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25. final def notifyAll(): Unit

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

    Definition Classes

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27. val thresh: Double

    Margin to use for comparing numerical values.

28. def toString(): String

    Definition Classes

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29. final def wait(): Unit

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    @throws( ... )

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

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    @throws( ... )

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

    Definition Classes

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