Packages

o

org.apache.spark.ml.stat

KolmogorovSmirnovTest

object KolmogorovSmirnovTest

Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a continuous distribution. By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution. For more information on KS Test:

Annotations
@Since( "2.4.0" )
Source
KolmogorovSmirnovTest.scala
See also

Kolmogorov-Smirnov test (Wikipedia)

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. KolmogorovSmirnovTest
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @IntrinsicCandidate()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  9. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  10. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  11. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  13. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  14. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  15. def test(dataset: Dataset[_], sampleCol: String, distName: String, params: Double*): DataFrame

    Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution equality.

    Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution equality. Currently supports the normal distribution, taking as parameters the mean and standard deviation.

    dataset

    A Dataset or a DataFrame containing the sample of data to test

    sampleCol

    Name of sample column in dataset, of any numerical type

    distName

    a String name for a theoretical distribution, currently only support "norm".

    params

    Double* specifying the parameters to be used for the theoretical distribution. For "norm" distribution, the parameters includes mean and variance.

    returns

    DataFrame containing the test result for the input sampled data. This DataFrame will contain a single Row with the following fields:

    • pValue: Double
    • statistic: Double
    Annotations
    @Since( "2.4.0" ) @varargs()
  16. def test(dataset: Dataset[_], sampleCol: String, cdf: Function[Double, Double]): DataFrame

    Java-friendly version of test(dataset: DataFrame, sampleCol: String, cdf: Double => Double)

    Java-friendly version of test(dataset: DataFrame, sampleCol: String, cdf: Double => Double)

    Annotations
    @Since( "2.4.0" )
  17. def test(dataset: Dataset[_], sampleCol: String, cdf: (Double) ⇒ Double): DataFrame

    Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a continuous distribution.

    Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a continuous distribution. By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution.

    dataset

    A Dataset or a DataFrame containing the sample of data to test

    sampleCol

    Name of sample column in dataset, of any numerical type

    cdf

    a Double => Double function to calculate the theoretical CDF at a given value

    returns

    DataFrame containing the test result for the input sampled data. This DataFrame will contain a single Row with the following fields:

    • pValue: Double
    • statistic: Double
    Annotations
    @Since( "2.4.0" )
  18. def toString(): String
    Definition Classes
    AnyRef → Any
  19. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  20. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  21. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from AnyRef

Inherited from Any

Members