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package optimization.gradientBasedMethods;
/**
* Defines an optimization objective:
*
*
* @author javg
*
*/
public abstract class Objective {
protected int functionCalls = 0;
protected int gradientCalls = 0;
protected int updateCalls = 0;
protected double[] parameters;
//Contains a cache with the gradient
public double[] gradient;
int debugLevel = 0;
public void setDebugLevel(int level){
debugLevel = level;
}
public int getNumParameters() {
return parameters.length;
}
public double getParameter(int index) {
return parameters[index];
}
public double[] getParameters() {
return parameters;
}
public abstract double[] getGradient( );
public void setParameter(int index, double value) {
parameters[index]=value;
}
public void setParameters(double[] params) {
if(parameters == null){
parameters = new double[params.length];
}
updateCalls++;
System.arraycopy(params, 0, parameters, 0, params.length);
}
public int getNumberFunctionCalls() {
return functionCalls;
}
public int getNumberGradientCalls() {
return gradientCalls;
}
public int getNumberUpdateCalls() {
return updateCalls;
}
public String finalInfoString() {
return "FE: " + functionCalls + " GE " + gradientCalls + " Params updates" +
updateCalls;
}
public void printParameters() {
System.out.println(toString());
}
public abstract String toString();
public abstract double getValue ();
/**
* Sets the initial objective parameters
* For unconstrained models this just sets the objective params = argument no copying
* For a constrained objective project the parameters and then set
* @param params
*/
public void setInitialParameters(double[] params){
parameters = params;
}
}
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