StatFunctions

Value Members

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

   

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2. final def ##(): Int

   

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

   

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4. final def asInstanceOf[T0]: T0

   

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5. def calculateCov(df: DataFrame, cols: Seq[String]): Double

   

   Calculate the covariance of two numerical columns of a DataFrame.

   Calculate the covariance of two numerical columns of a DataFrame.

   df

   The DataFrame

   cols

   the column names

   returns

   the covariance of the two columns.

6. def clone(): AnyRef

   

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7. def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame

   

   Generate a table of frequencies for the elements of two columns.

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

   

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9. def equals(arg0: Any): Boolean

   

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10. def finalize(): Unit

    

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

    

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12. def hashCode(): Int

    

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13. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

    

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14. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    

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15. final def isInstanceOf[T0]: Boolean

    

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16. def isTraceEnabled(): Boolean

    

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17. def log: Logger

    

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18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    

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19. def logDebug(msg: ⇒ String): Unit

    

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20. def logError(msg: ⇒ String, throwable: Throwable): Unit

    

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21. def logError(msg: ⇒ String): Unit

    

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22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    

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23. def logInfo(msg: ⇒ String): Unit

    

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24. def logName: String

    

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25. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    

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26. def logTrace(msg: ⇒ String): Unit

    

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27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    

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28. def logWarning(msg: ⇒ String): Unit

    

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29. def multipleApproxQuantiles(df: DataFrame, cols: Seq[String], probabilities: Seq[Double], relativeError: Double): Seq[Seq[Double]]

    

    Calculates the approximate quantiles of multiple numerical columns of a DataFrame in one pass.

    Calculates the approximate quantiles of multiple numerical columns of a DataFrame in one pass.

    The result of this algorithm has the following deterministic bound: If the DataFrame has N elements and if we request the quantile at probability p up to error err, then the algorithm will return a sample x from the DataFrame so that the *exact* rank of x is close to (p * N). More precisely,

    floor((p - err) * N) <= rank(x) <= ceil((p + err) * N).

    This method implements a variation of the Greenwald-Khanna algorithm (with some speed optimizations). The algorithm was first present in Space-efficient Online Computation of Quantile Summaries by Greenwald and Khanna.

    df

    the dataframe

    cols

    numerical columns of the dataframe

    probabilities

    a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the minimum, 0.5 is the median, 1 is the maximum.

    relativeError

    The relative target precision to achieve (>= 0). If set to zero, the exact quantiles are computed, which could be very expensive. Note that values greater than 1 are accepted but give the same result as 1.

    returns

    for each column, returns the requested approximations

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

    

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31. final def notify(): Unit

    

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32. final def notifyAll(): Unit

    

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33. def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double

    

    Calculate the Pearson Correlation Coefficient for the given columns

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

    

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35. def toString(): String

    

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36. final def wait(): Unit

    

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

    

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

    

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