org.gersteinlab.regulation.model
Class RateModelSqrDiff
java.lang.Object
org.gersteinlab.regulation.numeric.MultivariateFunction
org.gersteinlab.regulation.numeric.DifferentiableMultivariateFunction
org.gersteinlab.regulation.numeric.DoublyDifferentiableMultivariateFunction
org.gersteinlab.regulation.model.RateModelSqrDiff
public class RateModelSqrDiff
- extends DoublyDifferentiableMultivariateFunction
A function that equals the total squared difference between the
predicted value of a differential equation rate model and some target
values at a set of time points, treating the parameters of the model as
the variables.
- Version:
- 1.0 (September 1, 2008)
Change History:
1.0 - Initial version
- Author:
- Kevin Yuk-Lap Yip
Constructor Summary |
RateModelSqrDiff(double[] tIn,
double[][] yjIn,
double[][][][] yksIn,
double[][][][] dykdtsIn,
java.lang.Class<? extends RateModel> modelClass)
Create the function with the given set of points and target
values and differentials. |
Method Summary |
double |
f()
Evaluate the total squared difference at the specified parameter
values. |
double[] |
gf()
Evaluate the gradient of the function at the specified parameter
values. |
double[][] |
hf()
Evaluate the Hessian of the function at the specified parameter
values. |
void |
set(double[] a)
Set the parameter values of the model. |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
RateModelSqrDiff
public RateModelSqrDiff(double[] tIn,
double[][] yjIn,
double[][][][] yksIn,
double[][][][] dykdtsIn,
java.lang.Class<? extends RateModel> modelClass)
throws java.lang.InstantiationException,
java.lang.IllegalAccessException,
java.lang.IllegalArgumentException,
java.lang.reflect.InvocationTargetException,
java.lang.NoSuchMethodException
- Create the function with the given set of points and target
values and differentials.
- Parameters:
tIn
- The sets of pointsyjIn
- The sets of target values
First dimension: experiment
Second dimension: time pointyksIn
- The sets of predictors
First dimention: predictor set
Second dimension: predictor
Third dimension: experiment
Fourth dimension: time pointdykdtsIn
- The sets of differentials of the predictors
First dimension: predictor set
Second dimension: predictor
Third dimension: experiment
Fourth dimension: time pointmodelClass
- The class of the rate model
- Throws:
java.lang.InstantiationException
java.lang.IllegalAccessException
java.lang.IllegalArgumentException
java.lang.reflect.InvocationTargetException
java.lang.NoSuchMethodException
set
public void set(double[] a)
- Set the parameter values of the model.
- Overrides:
set
in class MultivariateFunction
- Parameters:
a
- The parameter values
f
public double f()
- Evaluate the total squared difference at the specified parameter
values.
- Overrides:
f
in class MultivariateFunction
- Returns:
- The squred difference
gf
public double[] gf()
- Evaluate the gradient of the function at the specified parameter
values.
- Overrides:
gf
in class DifferentiableMultivariateFunction
- Returns:
- The gradient
hf
public double[][] hf()
- Evaluate the Hessian of the function at the specified parameter
values.
- Overrides:
hf
in class DoublyDifferentiableMultivariateFunction
- Returns:
- The Hessian