/** * 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()])); }