/**
* Provides the fundamental data structures which encode the maxent model information. This method
* will usually only be needed by GISModelWriters. The following values are held in the Object
* array which is returned by this method:
* <li>index 0: opennlp.maxent.Context[] containing the model parameters
* <li>index 1: java.util.Map containing the mapping of model predicates to unique integers
* <li>index 2: java.lang.String[] containing the names of the outcomes, stored in the index of
* the array which represents their unique ids in the model.
* <li>index 3: java.lang.Integer containing the value of the models correction constant
* <li>index 4: java.lang.Double containing the value of the models correction parameter
*
* @return An Object[] with the values as described above.
*/
public final Object[] getDataStructures() {
Object[] data = new Object[5];
data[0] = evalParams.getParams();
data[1] = pmap;
data[2] = outcomeNames;
data[3] = (int) evalParams.getCorrectionConstant();
data[4] = evalParams.getCorrectionParam();
return data;
}
/**
* 预测
*
* @param context 环境
* @param prior 先验概率
* @param model 特征函数
* @return
*/
public static double[] eval(int[] context, double[] prior, EvalParameters model) {
Context[] params = model.getParams();
int numfeats[] = new int[model.getNumOutcomes()];
int[] activeOutcomes;
double[] activeParameters;
double value = 1;
for (int ci = 0; ci < context.length; ci++) {
if (context[ci] >= 0) {
Context predParams = params[context[ci]];
activeOutcomes = predParams.getOutcomes();
activeParameters = predParams.getParameters();
for (int ai = 0; ai < activeOutcomes.length; ai++) {
int oid = activeOutcomes[ai];
numfeats[oid]++;
prior[oid] += activeParameters[ai] * value;
}
}
}
double normal = 0.0;
for (int oid = 0; oid < model.getNumOutcomes(); oid++) {
if (model.getCorrectionParam() != 0) {
prior[oid] =
Math.exp(
prior[oid] * model.getConstantInverse()
+ ((1.0 - ((double) numfeats[oid] / model.getCorrectionConstant()))
* model.getCorrectionParam()));
} else {
prior[oid] = Math.exp(prior[oid] * model.getConstantInverse());
}
normal += prior[oid];
}
for (int oid = 0; oid < model.getNumOutcomes(); oid++) {
prior[oid] /= normal;
}
return prior;
}
public int getNumOutcomes() {
return (evalParams.getNumOutcomes());
}
/**
* 预测分布
*
* @param context 环境
* @return 概率数组
*/
public final double[] eval(String[] context) {
return (eval(context, new double[evalParams.getNumOutcomes()]));
}
