public static double[] eval(int[] context, double[] prior, EvalParameters model) { return eval(context,null,prior,model,true); }
public double[] eval(String[] context, double[] probs) { return eval(context,null,probs); }
public double[] eval(String[] context, float[] values,double[] outsums) { Context[] scontexts = new Context[context.length]; java.util.Arrays.fill(outsums, 0); for (int i = 0; i < context.length; i++) { scontexts[i] = pmap.get(context[i]); } return eval(scontexts,values,outsums,evalParams,true); }
static double[] eval(int[] context, float[] values, double[] prior, EvalParameters model, boolean normalize) { Context[] scontexts = new Context[context.length]; for (int i = 0; i < context.length; i++) { scontexts[i] = model.getParams()[context[i]]; } return eval(scontexts, values, prior, model, normalize); }
public double[] eval(String[] context, float[] values) { return eval(context,values,new double[evalParams.getNumOutcomes()]); }
public double[] eval(String[] context) { return eval(context,new double[evalParams.getNumOutcomes()]); }
private double trainingStats(EvalParameters evalParams) { int numCorrect = 0; for (int ei = 0; ei < numUniqueEvents; ei++) { for (int ni = 0; ni < this.numTimesEventsSeen[ei]; ni++) { double[] modelDistribution = new double[numOutcomes]; if (values != null) PerceptronModel.eval(contexts[ei], values[ei], modelDistribution, evalParams,false); else PerceptronModel.eval(contexts[ei], null, modelDistribution, evalParams, false); int max = ArrayMath.argmax(modelDistribution); if (max == outcomeList[ei]) numCorrect++; } } double trainingAccuracy = (double) numCorrect / numEvents; display("Stats: (" + numCorrect + "/" + numEvents + ") " + trainingAccuracy + "\n"); return trainingAccuracy; }
public static double[] eval(int[] context, double[] prior, EvalParameters model) { return eval(context,null,prior,model,true); }
public double[] eval(String[] context, double[] probs) { return eval(context,null,probs); }
public double[] eval(String[] context, double[] probs) { return eval(context,null,probs); }
public static double[] eval(int[] context, double[] prior, EvalParameters model) { return eval(context,null,prior,model,true); }
public double[] eval(String[] context, float[] values,double[] outsums) { Context[] scontexts = new Context[context.length]; java.util.Arrays.fill(outsums, 0); for (int i = 0; i < context.length; i++) { scontexts[i] = pmap.get(context[i]); } return eval(scontexts,values,outsums,evalParams,true); }
public double[] eval(String[] context, float[] values,double[] outsums) { Context[] scontexts = new Context[context.length]; java.util.Arrays.fill(outsums, 0); for (int i = 0; i < context.length; i++) { scontexts[i] = pmap.get(context[i]); } return eval(scontexts,values,outsums,evalParams,true); }
static double[] eval(int[] context, float[] values, double[] prior, EvalParameters model, boolean normalize) { Context[] scontexts = new Context[context.length]; for (int i = 0; i < context.length; i++) { scontexts[i] = model.getParams()[context[i]]; } return eval(scontexts, values, prior, model, normalize); }
static double[] eval(int[] context, float[] values, double[] prior, EvalParameters model, boolean normalize) { Context[] scontexts = new Context[context.length]; for (int i = 0; i < context.length; i++) { scontexts[i] = model.getParams()[context[i]]; } return eval(scontexts, values, prior, model, normalize); }
public double[] eval(String[] context, float[] values) { return eval(context,values,new double[evalParams.getNumOutcomes()]); }
public double[] eval(String[] context) { return eval(context,new double[evalParams.getNumOutcomes()]); }
public double[] eval(String[] context) { return eval(context,new double[evalParams.getNumOutcomes()]); }
public double[] eval(String[] context, float[] values) { return eval(context,values,new double[evalParams.getNumOutcomes()]); }