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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Instance Constructors
- 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
- final def !=(arg0: Any): Boolean
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- def analyze(x_tr: MatriD, y_tr: VectoD, x_te: MatriD, y_te: VectoD): NLS_ODE
Analyze a dataset using this model using ordinary training with the 'train' method.
- final def asInstanceOf[T0]: T0
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- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- def corrMatrix(xx: MatriD): MatriD
Return the correlation matrix for the columns in data matrix 'xx'.
Return the correlation matrix for the columns in data matrix 'xx'.
- xx
the data matrix shose correlation matrix is sought
- Definition Classes
- Predictor
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
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- def eval(yy: VectoD): NLS_ODE
Compute the error and useful diagnostics.
Compute the error and useful diagnostics.
- yy
the test response/output vector
- def eval(xx: MatriD, yy: VectoD): NLS_ODE
Compute the error and useful diagnostics.
- def fit: VectorD
Return the quality of fit.
- def fitLabel: Seq[String]
Return the labels for the fit.
- def fitMap: Map[String, String]
Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered). Override to add more quality of fit measures. - final def flaw(method: String, message: String): Unit
- Definition Classes
- Error
- def forwardSel(cols: Set[Int], index_q: Int = index_rSqBar): (Int, NLS_ODE)
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model.
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly.
- cols
the columns of matrix x currently included in the existing model
- index_q
index of Quality of Fit (QoF) to use for comparing quality
- final def getClass(): Class[_ <: AnyRef]
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- @native() @HotSpotIntrinsicCandidate()
- def getX: MatriD
Return the 'used' data matrix 'x'.
- def getY: VectoD
Return the 'used' response vector 'y'.
- def hashCode(): Int
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- def hparameter: HyperParameter
Return the hyper-parameters.
- def init(_objectiveF: FunctionV_2S, _y0: Double): Unit
Initialize
NLS-ODEwith the objective function and initial value/condition.Initialize
NLS-ODEwith the objective function and initial value/condition.- _objectiveF
the objective function indicating departure from observation
- final def isInstanceOf[T0]: Boolean
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- val modelConcept: URI
An optional reference to an ontological concept
An optional reference to an ontological concept
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- Model
- def modelName: String
An optional name for the model (or modeling technique)
An optional name for the model (or modeling technique)
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- Model
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- final def notifyAll(): Unit
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- def parameter: VectoD
Return the vector of parameter/coefficient values.
- def predict(zz: VectoD): Double
Predict the value of 'y = f(zz)'.
- def predict(z: MatriD): VectoD
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.
- def predict(z: VectoI): Double
Given a new discrete data/input vector 'z', predict the 'y'-value of 'f(z)'.
Given a new discrete data/input vector 'z', predict the 'y'-value of 'f(z)'.
- z
the vector to use for prediction
- Definition Classes
- Predictor
- def report: String
Return a basic report on the trained model.
- def residual: VectoD
Return the vector of residuals/errors.
- final def synchronized[T0](arg0: => T0): T0
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- def test(modelName: String, doPlot: Boolean = true): Unit
Test the model on the full dataset (i.e., train and evaluate on full dataset).
Test the model on the full dataset (i.e., train and evaluate on full dataset).
- modelName
the name of the model being tested
- doPlot
whether to plot the actual vs. predicted response
- Definition Classes
- Predictor
- def toString(): String
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- def train(): NLS_ODE
Train the predictor by fitting the parameter vector (b-vector) using a non-linear least squares method.
- def train(xx: MatriD, yy: VectoD): NLS_ODE
Train the predictor by fitting the parameter vector (b-vector) using a non-linear least squares method.
- final def wait(arg0: Long, arg1: Int): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
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- final def wait(): Unit
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- def wsseF(dy_dt: Derivative): Double
Function to compute the Weighted Sum of Squares Error 'SSE' for given values for parameter vector 'b'.
Deprecated Value Members
- def finalize(): Unit
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- Deprecated