Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def $[T](param: Param[T]): T

   An alias for getOrDefault().

   An alias for getOrDefault().

   Attributes

   protected

   Definition Classes

   Params

4.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

5.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

6.  final def clear(param: Param[_]): GaussianMixtureModel.this.type

   Clears the user-supplied value for the input param.

   Clears the user-supplied value for the input param.

   Definition Classes

   Params

7.  def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @native() @throws( ... )

8.  def copy(extra: ParamMap): GaussianMixtureModel

   Creates a copy of this instance with the same UID and some extra params.

   Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

   Definition Classes

   GaussianMixtureModel → Model → Transformer → PipelineStage → Params

   Annotations

   @Since( "2.0.0" )

9.  def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

   Copies param values from this instance to another instance for params shared by them.

   Copies param values from this instance to another instance for params shared by them.

   This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

   to

   the target instance, which should work with the same set of default Params as this source instance

   extra

   extra params to be copied to the target's paramMap

   returns

   the target instance with param values copied

   Attributes

   protected

   Definition Classes

   Params

10.  final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes

    protected

    Definition Classes

    Params

11.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

12.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

13.  def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes

    Params

14.  def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes

    Params

15.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

16.  final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes

    Params

17.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

18.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

19.  val gaussians: Array[MultivariateGaussian]

    Annotations

    @Since( "2.0.0" )

20.  def gaussiansDF: DataFrame

    Retrieve Gaussian distributions as a DataFrame.

    Retrieve Gaussian distributions as a DataFrame. Each row represents a Gaussian Distribution. Two columns are defined: mean and cov. Schema:

    root
     |-- mean: vector (nullable = true)
     |-- cov: matrix (nullable = true)

    Annotations

    @Since( "2.0.0" )

21.  final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes

    Params

22.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

23.  final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes

    Params

24.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

25.  def getK: Int
    Definition Classes

    GaussianMixtureParams

    Annotations

    @Since( "2.0.0" )

26.  final def getMaxIter: Int
    Definition Classes

    HasMaxIter

27.  final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes

    Params

28.  def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

29.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

30.  final def getProbabilityCol: String
    Definition Classes

    HasProbabilityCol

31.  final def getSeed: Long
    Definition Classes

    HasSeed

32.  final def getTol: Double
    Definition Classes

    HasTol

33.  final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes

    Params

34.  def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes

    Params

35.  def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes

    Model

36.  def hasSummary: Boolean

    Return true if there exists summary of model.

    Return true if there exists summary of model.

    Annotations

    @Since( "2.0.0" )

37.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

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

    Attributes

    protected

    Definition Classes

    Logging

39.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes

    protected

    Definition Classes

    Logging

40.  final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes

    Params

41.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

42.  final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

43.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

44.  final val k: IntParam

    Number of independent Gaussians in the mixture model.

    Number of independent Gaussians in the mixture model. Must be greater than 1. Default: 2.

    Definition Classes

    GaussianMixtureParams

    Annotations

    @Since( "2.0.0" )

45.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

47.  def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

49.  def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

51.  def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

52.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

54.  def logTrace(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

56.  def logWarning(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

57.  final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes

    HasMaxIter

58.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

59.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

60.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

61.  lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes

    Params

    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

62.  var parent: Estimator[GaussianMixtureModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes

    Model

    Note

    For ensembles' component Models, this value can be null.

63.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

64.  final val probabilityCol: Param[String]

    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

    Definition Classes

    HasProbabilityCol

65.  def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes

    MLWritable

    Annotations

    @Since( "1.6.0" ) @throws( ... )

66.  final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes

    HasSeed

67.  final def set(paramPair: ParamPair[_]): GaussianMixtureModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

68.  final def set(param: String, value: Any): GaussianMixtureModel.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

69.  final def set[T](param: Param[T], value: T): GaussianMixtureModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes

    Params

70.  final def setDefault(paramPairs: ParamPair[_]*): GaussianMixtureModel.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes

    protected

    Definition Classes

    Params

71.  final def setDefault[T](param: Param[T], value: T): GaussianMixtureModel.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes

    protected

    Definition Classes

    Params

72.  def setFeaturesCol(value: String): GaussianMixtureModel.this.type
    Annotations

    @Since( "2.1.0" )

73.  def setParent(parent: Estimator[GaussianMixtureModel]): GaussianMixtureModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes

    Model

74.  def setPredictionCol(value: String): GaussianMixtureModel.this.type
    Annotations

    @Since( "2.1.0" )

75.  def setProbabilityCol(value: String): GaussianMixtureModel.this.type
    Annotations

    @Since( "2.1.0" )

76.  def summary: GaussianMixtureSummary

    Gets summary of model on training set.

    Gets summary of model on training set. An exception is thrown if trainingSummary == None.

    Annotations

    @Since( "2.0.0" )

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

    Definition Classes

    AnyRef

78.  def toString(): String

    Definition Classes

    Identifiable → AnyRef → Any

79.  final val tol: DoubleParam

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Definition Classes

    HasTol

80.  def transform(dataset: Dataset[_]): DataFrame

    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes

    GaussianMixtureModel → Transformer

    Annotations

    @Since( "2.0.0" )

81.  def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes

    Transformer

    Annotations

    @Since( "2.0.0" )

82.  def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes

    Transformer

    Annotations

    @Since( "2.0.0" ) @varargs()

83.  def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes

    GaussianMixtureModel → PipelineStage

    Annotations

    @Since( "2.0.0" )

84.  def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes

    protected

    Definition Classes

    PipelineStage

    Annotations

    @DeveloperApi()

85.  val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes

    GaussianMixtureModel → Identifiable

    Annotations

    @Since( "2.0.0" )

86.  def validateAndTransformSchema(schema: StructType): StructType

    Validates and transforms the input schema.

    Validates and transforms the input schema.

    schema

    input schema

    returns

    output schema

    Attributes

    protected

    Definition Classes

    GaussianMixtureParams

87.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

89.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @native() @throws( ... )

90.  val weights: Array[Double]

    Annotations

    @Since( "2.0.0" )

91.  def write: MLWriter

    Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

    Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

    For GaussianMixtureModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

    Definition Classes

    GaussianMixtureModel → MLWritable

    Annotations

    @Since( "2.0.0" )