Packages

class NLS_ODE extends Predictor with Error

Given an Ordinary Differential Equation 'ODE' parameterized using the vector 'b' with Initial Value 'IV' 'y0', estimate the parameter values 'b' for the ODE using weighted Non-linear Least Squares 'NLS'.

ODE: dy/dt = f(t, y) IV: y(t0) = y0

Times series data: z(t0), z(t1), ... z(tn)

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Error, Predictor, AnyRef, Any
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  1. NLS_ODE
  2. Error
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Instance Constructors

  1. new NLS_ODE(z: VectorD, ts: VectorD, b_init: VectorD, w: VectorD = null)

    z

    the observed values

    ts

    the time points of the observations

    b_init

    the initial guess for the parameter values 'b'

    w

    the optional weights

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 clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  7. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  8. val e: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. def eval(): Unit

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics. FIX

    Definition Classes
    NLS_ODE → Predictor
  12. def eval(xx: MatriD, yy: VectoD): Unit

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    xx

    the test data matrix

    yy

    the test response vector FIX - implement in classes

    Definition Classes
    Predictor
  13. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. def fit: VectorD

    Return the quality of fit.

  15. def fitLabels: Seq[String]

    Return the labels for the fit.

  16. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  17. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  18. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  19. def init(_objectiveF: FunctionV_2S, _y0: Double): Unit

    Initialize NLS-ODE with the objective function and initial value/condition.

    Initialize NLS-ODE with the objective function and initial value/condition.

    _objectiveF

    the objective function indicating departure from observation

  20. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  21. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  22. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  23. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  24. def predict(zz: VectoD): Double

    Predict the value of 'y = f(zz)'.

    Predict the value of 'y = f(zz)'.

    zz

    the new vector to predict

    Definition Classes
    NLS_ODE → Predictor
  25. 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
  26. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  27. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  28. def toString(): String
    Definition Classes
    AnyRef → Any
  29. def train(): NLS_ODE

    Train the predictor by fitting the parameter vector (b-vector) using a non-linear least squares method.

  30. def train(yy: VectoD): NLS_ODE

    Train the predictor by fitting the parameter vector (b-vector) using a non-linear least squares method.

    Train the predictor by fitting the parameter vector (b-vector) using a non-linear least squares method.

    yy

    the response vector

    Definition Classes
    NLS_ODE → Predictor
  31. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  32. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  33. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  34. def wsseF(dy_dt: Derivative): Double

    Function to compute the Weighted Sum of Squares Error 'SSE' for given values for parameter vector 'b'.

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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