Class/Object

io.github.mandar2812.dynaml.models

KernelizedModel

Related Docs: object KernelizedModel | package models

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abstract class KernelizedModel[G, L, T <: Tensor[K1, Double], Q <: Tensor[K2, Double], R, K1, K2] extends LinearModel[G, T, Q, R, L] with GloballyOptimizable with EvaluableModel[T, R]

Linear Supertypes
EvaluableModel[T, R], GloballyOptimizable, LinearModel[G, T, Q, R, L], ParameterizedLearner[G, T, Q, R, L], Model[G, Q, R], AnyRef, Any
Known Subclasses
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Inherited
  1. KernelizedModel
  2. EvaluableModel
  3. GloballyOptimizable
  4. LinearModel
  5. ParameterizedLearner
  6. Model
  7. AnyRef
  8. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new KernelizedModel(task: String)

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Abstract Value Members

  1. abstract def applyFeatureMap: Unit

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  2. abstract def clearParameters(): Unit

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    Definition Classes
    LinearModel
  3. abstract def energy(h: Map[String, Double], options: Map[String, String] = Map()): Double

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    Calculates the energy of the configuration, in most global optimization algorithms we aim to find an approximate value of the hyper-parameters such that this function is minimized.

    Calculates the energy of the configuration, in most global optimization algorithms we aim to find an approximate value of the hyper-parameters such that this function is minimized.

    h

    The value of the hyper-parameters in the configuration space

    options

    Optional parameters about configuration

    returns

    Configuration Energy E(h)

    Definition Classes
    GloballyOptimizable
  4. abstract def evaluate(config: Map[String, String]): Metrics[R]

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    Definition Classes
    EvaluableModel
  5. abstract def evaluateFold(params: T)(test_data_set: L)(task: String): Metrics[Double]

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  6. abstract val g: G

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    The training data

    The training data

    Attributes
    protected
    Definition Classes
    Model
  7. abstract def getXYEdges: L

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  8. abstract def initParams(): T

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    Definition Classes
    ParameterizedLearner
  9. abstract def learn(): Unit

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    Learn the parameters of the model.

    Learn the parameters of the model.

    Definition Classes
    ParameterizedLearner
  10. abstract val nPoints: Long

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    Attributes
    protected
  11. abstract val optimizer: RegularizedOptimizer[T, Q, R, L]

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    Attributes
    protected
    Definition Classes
    ParameterizedLearner
  12. abstract def optimumSubset(M: Int): Unit

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    Calculate an approximation to the subset of size M with the maximum entropy.

  13. abstract val params: T

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    Attributes
    protected
    Definition Classes
    ParameterizedLearner
  14. abstract def predict(point: Q): R

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    Predict the value of the target variable given a point.

    Predict the value of the target variable given a point.

    Definition Classes
    Model
  15. abstract def trainTest(test: List[Long]): (L, L)

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Concrete Value Members

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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. def _current_state: Map[String, Double]

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    Definition Classes
    GloballyOptimizable
  5. def _hyper_parameters: List[String]

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    Definition Classes
    GloballyOptimizable
  6. def applyKernel(kernel: SVMKernel[DenseMatrix[Double]], M: Int = math.sqrt(nPoints).toInt): Unit

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    Implements the changes in the model after application of a given kernel.

    Implements the changes in the model after application of a given kernel.

    It calculates

    1) Eigen spectrum of the kernel

    2) Calculates an approximation to the non linear feature map induced by the application of the kernel

    kernel

    A kernel object.

    M

    The number of prototypes to select in order to approximate the kernel matrix.

  7. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def crossvalidate(folds: Int, reg: Double, optionalStateFlag: Boolean = false): (Double, Double, Double)

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  10. var current_state: Map[String, Double]

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    A Map which stores the current state of the system.

    A Map which stores the current state of the system.

    Attributes
    protected
    Definition Classes
    KernelizedModelGloballyOptimizable
  11. def data: G

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    Definition Classes
    Model
  12. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  14. var featureMap: (Q) ⇒ Q

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    The non linear feature mapping implicitly defined by the kernel applied, this is initialized to an identity map.

    The non linear feature mapping implicitly defined by the kernel applied, this is initialized to an identity map.

    Definition Classes
    LinearModel
  15. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  17. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  18. var hyper_parameters: List[String]

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    Stores the names of the hyper-parameters

    Stores the names of the hyper-parameters

    Attributes
    protected
    Definition Classes
    KernelizedModelGloballyOptimizable
  19. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  20. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  21. final def notify(): Unit

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    Definition Classes
    AnyRef
  22. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  23. def npoints: Long

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  24. def parameters(): T

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    Get the value of the parameters of the model.

    Get the value of the parameters of the model.

    Definition Classes
    ParameterizedLearner
  25. def persist(state: Map[String, Double]): Unit

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    Definition Classes
    GloballyOptimizable
  26. var points: List[Long]

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    This variable stores the indexes of the prototype points of the data set.

    This variable stores the indexes of the prototype points of the data set.

    Attributes
    protected
  27. def setBatchFraction(f: Double): KernelizedModel.this.type

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    Definition Classes
    ParameterizedLearner
  28. def setLearningRate(alpha: Double): KernelizedModel.this.type

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    Definition Classes
    ParameterizedLearner
  29. def setMaxIterations(i: Int): KernelizedModel.this.type

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    Definition Classes
    ParameterizedLearner
  30. def setRegParam(r: Double): KernelizedModel.this.type

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    Definition Classes
    ParameterizedLearner
  31. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  32. val task: String

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    Attributes
    protected
  33. def toString(): String

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    Definition Classes
    AnyRef → Any
  34. def updateParameters(param: T): Unit

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    Definition Classes
    ParameterizedLearner
  35. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from EvaluableModel[T, R]

Inherited from GloballyOptimizable

Inherited from LinearModel[G, T, Q, R, L]

Inherited from ParameterizedLearner[G, T, Q, R, L]

Inherited from Model[G, Q, R]

Inherited from AnyRef

Inherited from Any

Ungrouped