GradientGaussianMixture

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

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

   

   Definition Classes

   AnyRef → Any

4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. var batchSize: Option[Int]

   

   Minibatch size for each iteration in the ascent procedure.

   Minibatch size for each iteration in the ascent procedure. If None, it performs full-batch optimization

   Attributes

   protected

   Definition Classes

   Optimizable

6. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

7. var converged: Boolean

   

   convergence flag for local optimisation

   convergence flag for local optimisation

   Attributes

   protected

   Definition Classes

   Optimizable

8. var convergenceTol: Double

   

   Error tolerance in log-likelihood for the stopping criteria

   Error tolerance in log-likelihood for the stopping criteria

   Attributes

   protected

   Definition Classes

   Optimizable

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

   

   Definition Classes

   AnyRef

10. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

11. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

12. var gaussians: Array[UpdatableGaussianComponent]

    

    Definition Classes

    UpdatableGaussianMixture

13. def getBatchSize: Option[Int]

    

    Definition Classes

    Optimizable

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

    

    Definition Classes

    AnyRef → Any

15. def getConvergenceTol: Double

    

    Definition Classes

    Optimizable

16. def getGaussians: Array[UpdatableGaussianComponent]

    

    Definition Classes

    UpdatableGaussianMixture

17. def getLoss: Double

    

    Definition Classes

    Optimizable

18. def getMaxIter: Int

    

    Definition Classes

    Optimizable

19. def getOptim: Optimizer

    

    Definition Classes

    Optimizable

20. def getSeed: Long

    

    Definition Classes

    Optimizable

21. def getWeights: Array[Double]

    

    Definition Classes

    UpdatableGaussianMixture

22. implicit var globalIterCounter: Long

    

    this prevents the seed from repeating every time step() is called which would cause the same samples being taken

    this prevents the seed from repeating every time step() is called which would cause the same samples being taken

    Attributes

    protected

    Definition Classes

    Optimizable

23. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

24. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

25. def k: Int

    

    number of componenrs

    number of componenrs

    Definition Classes

    UpdatableGaussianMixture

26. var lossValue: Double

    

    Current loss value

    Current loss value

    Attributes

    protected

    Definition Classes

    Optimizable

27. implicit val matrixOps: ParameterOperations[DenseMatrix[Double]]

    

    Attributes

    protected

    Definition Classes

    Optimizable

28. var maxIter: Int

    

    Maximum number of iterations allowed

    Maximum number of iterations allowed

    Attributes

    protected

    Definition Classes

    Optimizable

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

    

    Definition Classes

    AnyRef

30. final def notify(): Unit

    

    Definition Classes

    AnyRef

31. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

32. var optim: Optimizer

    

    Optimization object

    Optimization object

    Definition Classes

    GradientGaussianMixture → Optimizable

33. def predict(data: DStream[Vector]): DStream[Int]

    

    predict cluster membership

    predict cluster membership

    data

    Streaming data

    Definition Classes

    UpdatableGaussianMixture

34. def predict(point: DenseVector[Double]): Int

    

    predict cluster membership

    predict cluster membership

    returns

    vector membership label

    Definition Classes

    UpdatableGaussianMixture

35. def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

    

    predict cluster membership

    predict cluster membership

    returns

    RDD with the points' labels

    Definition Classes

    UpdatableGaussianMixture

36. def predict(point: Vector): Int

    

    predict cluster membership

    predict cluster membership

    returns

    vector membership label

    Definition Classes

    UpdatableGaussianMixture

37. def predict(points: RDD[Vector]): RDD[Int]

    

    predict cluster membership

    predict cluster membership

    returns

    RDD with the points' labels

    Definition Classes

    UpdatableGaussianMixture

38. def predictSoft(data: DStream[Vector]): DStream[Array[Double]]

    

    predict soft cluster membership

    predict soft cluster membership

    data

    Streaming data

    Definition Classes

    UpdatableGaussianMixture

39. def predictSoft(point: DenseVector[Double]): Array[Double]

    

    predict soft cluster membership

    predict soft cluster membership

    returns

    Array giving the membership probabilities for each cluster

    Definition Classes

    UpdatableGaussianMixture

40. def predictSoft(point: Vector): Array[Double]

    

    predict soft cluster membership

    predict soft cluster membership

    returns

    RDD with arrays giving the membership probabilities for each cluster

    Definition Classes

    UpdatableGaussianMixture

41. def predictSoft(points: RDD[Vector]): RDD[Array[Double]]

    

    predict soft cluster membership

    predict soft cluster membership

    returns

    RDD with arrays giving the membership probabilities for each cluster

    Definition Classes

    UpdatableGaussianMixture

42. def predictSoft(points: JavaRDD[Vector]): JavaRDD[Array[Double]]

    

    predict soft cluster membership

    predict soft cluster membership

    returns

    RDD with arrays giving the membership probabilities for each cluster

    Definition Classes

    UpdatableGaussianMixture

43. var regularizer: Option[Regularizer]

    

    Optional regularization term

    Optional regularization term

    Attributes

    protected

    Definition Classes

    Optimizable

44. var seed: Long

    

    random seed for mini-batch sampling

    random seed for mini-batch sampling

    Attributes

    protected

    Definition Classes

    Optimizable

45. def setBatchSize(n: Option[Int]): GradientGaussianMixture.this.type

    

    Definition Classes

    Optimizable

46. def setBatchSize(n: Int): GradientGaussianMixture.this.type

    

    Definition Classes

    Optimizable

47. def setConvergenceTol(x: Double): GradientGaussianMixture.this.type

    

    Definition Classes

    Optimizable

48. def setMaxIter(m: Int): GradientGaussianMixture.this.type

    

    Definition Classes

    Optimizable

49. def setOptim(optim: Optimizer): GradientGaussianMixture.this.type

    

    Definition Classes

    Optimizable

50. def setRegularizer(r: Option[Regularizer]): GradientGaussianMixture.this.type

    

    Definition Classes

    Optimizable

51. def setRegularizer(r: Regularizer): GradientGaussianMixture.this.type

    

    Definition Classes

    Optimizable

52. def setSeed(s: Long): GradientGaussianMixture.this.type

    

    Definition Classes

    Optimizable

53. def step(data: Array[DenseVector[Double]]): GradientGaussianMixture.this.type

    

    Perform local sequential gradient descent optimisation

    Perform local sequential gradient descent optimisation

    data

    Training data as an Array of Breeze vectors

54. def step(data: RDD[Vector]): GradientGaussianMixture.this.type

    

    Perform a gradient-based optimization step

    Perform a gradient-based optimization step

    data

    Data to fit the model

    Definition Classes

    GradientGaussianMixture → Optimizable

55. def step(data: DStream[Vector]): GradientGaussianMixture.this.type

    

    Update model parameters using streaming data

    Update model parameters using streaming data

    data

    Streaming data

    Definition Classes

    Optimizable

56. def step(data: JavaRDD[Vector]): Unit

    

    Perform a gradient-based optimization step

    Perform a gradient-based optimization step

    data

    Data to fit the model

    Definition Classes

    Optimizable

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

    

    Definition Classes

    AnyRef

58. def toSparkGMM: GaussianMixtureModel

    

    Returns a Spark's Gaussian Mixture Model with the current parameters initialized with the current parameters

59. def toString(): String

    

    Definition Classes

    AnyRef → Any

60. implicit val vectorOps: ParameterOperations[DenseVector[Double]]

    

    Linear Algebra operations necessary for computing updates for the parameters

    Linear Algebra operations necessary for computing updates for the parameters

    This is to avoid duplicating code for Gaussian and Weights updates in the optimization algorithms' classes

    Attributes

    protected

    Definition Classes

    Optimizable

61. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

64. var weights: UpdatableWeights

    

    Definition Classes

    UpdatableGaussianMixture