Class

io.github.mandar2812.dynaml.models.lm

RegularizedGLM

Related Doc: package lm

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class RegularizedGLM extends GeneralizedLinearModel[(DenseMatrix[Double], DenseVector[Double])] with GloballyOptimizable

Linear Supertypes
GeneralizedLinearModel[(DenseMatrix[Double], DenseVector[Double])], GloballyOptimizable, GenericGLM[Stream[(DenseVector[Double], Double)], (DenseMatrix[Double], DenseVector[Double])], LinearModel[Stream[(DenseVector[Double], Double)], DenseVector[Double], DenseVector[Double], Double, (DenseMatrix[Double], DenseVector[Double])], ParameterizedLearner[Stream[(DenseVector[Double], Double)], DenseVector[Double], DenseVector[Double], Double, (DenseMatrix[Double], DenseVector[Double])], Model[Stream[(DenseVector[Double], Double)], DenseVector[Double], Double], AnyRef, Any
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Inherited
  1. RegularizedGLM
  2. GeneralizedLinearModel
  3. GloballyOptimizable
  4. GenericGLM
  5. LinearModel
  6. ParameterizedLearner
  7. Model
  8. AnyRef
  9. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new RegularizedGLM(data: Stream[(DenseVector[Double], Double)], numPoints: Int, map: (DenseVector[Double]) ⇒ DenseVector[Double] = identity[DenseVector[Double]])

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    data

    The training data as a stream of tuples

    numPoints

    The number of training data points

    map

    The basis functions used to map the input features to a possible higher dimensional space

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. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  7. def clearParameters(): Unit

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    Definition Classes
    GenericGLM → LinearModel

  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  9. 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
    GeneralizedLinearModel → GloballyOptimizable

  10. def data: Stream[(DenseVector[Double], Double)]

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    Definition Classes
    Model

  11. def dimensions: Int

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    Definition Classes
    GeneralizedLinearModel

  12. def energy(h: Map[String, Double], options: Map[String, String]): 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
    GeneralizedLinearModel → GloballyOptimizable

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

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    Definition Classes
    AnyRef

  14. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  15. var featureMap: (DenseVector[Double]) ⇒ DenseVector[Double]

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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

  16. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  17. val g: Stream[(DenseVector[Double], Double)]

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    The training data

    The training data

    Attributes
    protected
    Definition Classes
    GeneralizedLinearModel → GenericGLM → Model

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

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    Definition Classes
    AnyRef → Any

  19. val h: (Double) ⇒ Double

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    Definition Classes
    GeneralizedLinearModel → GenericGLM

  20. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  21. 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
    GeneralizedLinearModel → GloballyOptimizable

  22. def initParams(): DenseVector[Double]

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    Initialize parameters to a vector of ones.

    Initialize parameters to a vector of ones.

    Definition Classes
    GeneralizedLinearModel → ParameterizedLearner

  23. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  24. def learn(): Unit

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    Learn the parameters of the model.

    Learn the parameters of the model.

    Definition Classes
    GenericGLM → ParameterizedLearner

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

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    Definition Classes
    AnyRef

  26. final def notify(): Unit

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    Definition Classes
    AnyRef

  27. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  28. val optimizer: RegularizedOptimizer[DenseVector[Double], DenseVector[Double], Double, (DenseMatrix[Double], DenseVector[Double])]

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    Attributes
    protected
    Definition Classes
    RegularizedGLM → ParameterizedLearner

  29. def parameters(): DenseVector[Double]

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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

  30. var params: DenseVector[Double]

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    Attributes
    protected
    Definition Classes
    GeneralizedLinearModel → ParameterizedLearner

  31. def persist(state: Map[String, Double]): Unit

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    Definition Classes
    GloballyOptimizable

  32. def predict(point: DenseVector[Double]): Double

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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
    GenericGLM → Model

  33. def prepareData(d: Stream[(DenseVector[Double], Double)]): (DenseMatrix[Double], DenseVector[Double])

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    Definition Classes
    RegularizedGLM → GenericGLM

  34. def setBatchFraction(f: Double): RegularizedGLM.this.type

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    Definition Classes
    ParameterizedLearner

  35. def setLearningRate(alpha: Double): RegularizedGLM.this.type

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    Definition Classes
    ParameterizedLearner

  36. def setMaxIterations(i: Int): RegularizedGLM.this.type

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    Definition Classes
    ParameterizedLearner

  37. def setRegParam(r: Double): RegularizedGLM.this.type

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    Definition Classes
    ParameterizedLearner

  38. def setState(s: Map[String, Double]): RegularizedGLM.this.type

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    Set the model "state" which contains values of its hyper-parameters with respect to the covariance and noise kernels.

    Set the model "state" which contains values of its hyper-parameters with respect to the covariance and noise kernels.

    Definition Classes
    GeneralizedLinearModel

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

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    Definition Classes
    AnyRef

  40. val task: String

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    Definition Classes
    RegularizedGLM → GeneralizedLinearModel

  41. def toString(): String

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    Definition Classes
    AnyRef → Any

  42. def updateParameters(param: DenseVector[Double]): Unit

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    Definition Classes
    ParameterizedLearner

  43. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from GeneralizedLinearModel[(DenseMatrix[Double], DenseVector[Double])]

Inherited from GloballyOptimizable

Inherited from GenericGLM[Stream[(DenseVector[Double], Double)], (DenseMatrix[Double], DenseVector[Double])]

Inherited from LinearModel[Stream[(DenseVector[Double], Double)], DenseVector[Double], DenseVector[Double], Double, (DenseMatrix[Double], DenseVector[Double])]

Inherited from ParameterizedLearner[Stream[(DenseVector[Double], Double)], DenseVector[Double], DenseVector[Double], Double, (DenseMatrix[Double], DenseVector[Double])]

Inherited from Model[Stream[(DenseVector[Double], Double)], DenseVector[Double], Double]

Inherited from AnyRef

Inherited from Any

Ungrouped