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* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
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* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package opennlp.model;
/**
* This class encapsulates the varibales used in producing probabilities from a model
* and facilitaes passing these variables to the eval method.
*/
public class EvalParameters {
/** Mapping between outcomes and paramater values for each context.
* The integer representation of the context can be found using pmap
.*/
private Context[] params;
/** The number of outcomes being predicted. */
private final int numOutcomes;
/** The maximum number of feattures fired in an event. Usually refered to a C.
* This is used to normalize the number of features which occur in an event. */
private double correctionConstant;
/** Stores inverse of the correction constant, 1/C. */
private final double constantInverse;
/** The correction parameter of the model. */
private double correctionParam;
/**
* Creates a set of paramters which can be evaulated with the eval method.
* @param params The parameters of the model.
* @param correctionParam The correction paramter.
* @param correctionConstant The correction constant.
* @param numOutcomes The number of outcomes.
*/
public EvalParameters(Context[] params, double correctionParam, double correctionConstant, int numOutcomes) {
this.params = params;
this.correctionParam = correctionParam;
this.numOutcomes = numOutcomes;
this.correctionConstant = correctionConstant;
this.constantInverse = 1.0 / correctionConstant;
}
public EvalParameters(Context[] params, int numOutcomes) {
this(params,0,0,numOutcomes);
}
/* (non-Javadoc)
* @see opennlp.model.EvalParameters#getParams()
*/
public Context[] getParams() {
return params;
}
/* (non-Javadoc)
* @see opennlp.model.EvalParameters#getNumOutcomes()
*/
public int getNumOutcomes() {
return numOutcomes;
}
public double getCorrectionConstant() {
return correctionConstant;
}
public double getConstantInverse() {
return constantInverse;
}
public double getCorrectionParam() {
return correctionParam;
}
public void setCorrectionParam(double correctionParam) {
this.correctionParam = correctionParam;
}
}
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