Packages

c

scalation.analytics

LassoRegression

class LassoRegression[MatT <: MatriD, VecT <: VectoD] extends Predictor with Error

The LassoRegression class supports multiple linear regression. In this case, 'x' is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector 'b' in the regression equation

y = b dot x + e = b_0 + b_1 * x_1 + ... b_k * x_k + e

where 'e' represents the residuals (the part not explained by the model).

See also

see.stanford.edu/materials/lsoeldsee263/05-ls.pdf

Linear Supertypes
Error, Predictor, AnyRef, Any
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  1. LassoRegression
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Instance Constructors

  1. new LassoRegression(x: MatT, y: VecT, λ0: Double = 0.01)

    x

    the input/design m-by-n matrix

    y

    the response vector

    λ0

    the initial value for the regularization weight

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. val b: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  6. def build(x: MatriD, y: VectoD): Predictor
    Definition Classes
    Predictor
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  8. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  9. def diagnose(yy: VectoD): Unit

    Compute diagostics for the regression model.

    Compute diagostics for the regression model.

    yy

    the response vector

    Attributes
    protected
    Definition Classes
    LassoRegressionPredictor
  10. val e: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def eval(yy: VectoD = y): Unit

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics.

    yy

    the response vector

    Definition Classes
    LassoRegressionPredictor
  14. def f(yy: VectoD)(b: VectoD): Double

    Compute the sum of squares error + λ * sum of the magnitude of coefficients.

    Compute the sum of squares error + λ * sum of the magnitude of coefficients. This is the objective function to be minimized.

    yy

    the response vector

    b

    the vector of coefficients/parameters

  15. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. def fit: VectoD

    Return the quality of fit.

    Return the quality of fit.

    Definition Classes
    LassoRegressionPredictor
  17. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    LassoRegressionPredictor
  18. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  19. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  20. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  21. val index_rSq: Int
    Definition Classes
    Predictor
  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. val mae: Double
    Attributes
    protected
    Definition Classes
    Predictor
  24. def metrics: Map[String, Any]

    Build a map of selected quality of fit measures/metrics.

    Build a map of selected quality of fit measures/metrics.

    Definition Classes
    Predictor
  25. val mse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  26. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  28. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  29. def predict(z: MatT): VectoD

    Predict the value of y = f(z) by evaluating the formula y = b dot z for each row of matrix z.

    Predict the value of y = f(z) by evaluating the formula y = b dot z for each row of matrix z.

    z

    the new matrix to predict

  30. def predict(z: VectoD): Double

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    z

    the new vector to predict

    Definition Classes
    LassoRegressionPredictor
  31. def predict(z: VectoI): Double

    Given a new discrete data vector z, predict the y-value of f(z).

    Given a new discrete data vector z, predict the y-value of f(z).

    z

    the vector to use for prediction

    Definition Classes
    Predictor
  32. val rSq: Double
    Attributes
    protected
    Definition Classes
    Predictor
  33. def report(): Unit

    Print results and diagnostics for each predictor 'x_j' and the overall quality of fit.

  34. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  35. val rmse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  36. val sse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  37. val ssr: Double
    Attributes
    protected
    Definition Classes
    Predictor
  38. val sst: Double
    Attributes
    protected
    Definition Classes
    Predictor
  39. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  40. def toString(): String
    Definition Classes
    AnyRef → Any
  41. def train(yy: VectoD = y): LassoRegression[MatT, VecT]

    Train the predictor by fitting the parameter vector (b-vector) in the multiple regression equation

    Train the predictor by fitting the parameter vector (b-vector) in the multiple regression equation

    y = b dot x + e = [b_0, ... b_k] dot [1, x_1 , ... x_k] + e

    regularized by the sum of magnitudes of the coefficients.

    yy

    the response vector

    Definition Classes
    LassoRegressionPredictor
    See also

    scalation.minima.LassoAdmm

    pdfs.semanticscholar.org/969f/077a3a56105a926a3b0c67077a57f3da3ddf.pdf

  42. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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