protected AbstractModel(Context[] params, String[] predLabels, Map<String, Context> pmap, String[] outcomeNames) { this.pmap = pmap; this.outcomeNames = outcomeNames; this.evalParams = new EvalParameters(params,outcomeNames.length); }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames) { init(predLabels, params, outcomeNames); this.evalParams = new EvalParameters(params, outcomeNames.length); }
private MutableContext[] findParameters() { int[] allOutcomesPattern = new int[numOutcomes]; for (int oi = 0; oi < numOutcomes; oi++) allOutcomesPattern[oi] = oi; /* Stores the estimated parameter value of each predicate during iteration. */ MutableContext[] params = new MutableContext[numPreds]; for (int pi = 0; pi < numPreds; pi++) { params[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]); for (int aoi = 0; aoi < numOutcomes; aoi++) params[pi].setParameter(aoi, 0.0); } EvalParameters evalParams = new EvalParameters(params, numOutcomes); double stepSize = 1; for (int ei = 0; ei < numUniqueEvents; ei++) { int targetOutcome = outcomeList[ei]; for (int ni = 0; ni < this.numTimesEventsSeen[ei]; ni++) { for (int ci = 0; ci < contexts[ei].length; ci++) { int pi = contexts[ei][ci]; if (values == null) { params[pi].updateParameter(targetOutcome, stepSize); } else { params[pi].updateParameter(targetOutcome, stepSize * values[ei][ci]); } } } } // Output the final training stats. trainingStats(evalParams); return params; }
protected AbstractModel(Context[] params, String[] predLabels, Map<String, Context> pmap, String[] outcomeNames) { this.pmap = pmap; this.outcomeNames = outcomeNames; this.evalParams = new EvalParameters(params,outcomeNames.length); }
protected AbstractModel(Context[] params, String[] predLabels, Map<String, Context> pmap, String[] outcomeNames) { this.pmap = pmap; this.outcomeNames = outcomeNames; this.evalParams = new EvalParameters(params,outcomeNames.length); }
EvalParameters evalParams = new EvalParameters(params, numOutcomes);
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames) { init(predLabels, params, outcomeNames); this.evalParams = new EvalParameters(params, outcomeNames.length); }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames) { init(predLabels, params, outcomeNames); this.evalParams = new EvalParameters(params, outcomeNames.length); }
evalParams = new EvalParameters(params, numOutcomes); int[] activeOutcomes = new int[numOutcomes]; int[] outcomePattern;
private MutableContext[] findParameters() { int[] allOutcomesPattern = new int[numOutcomes]; for (int oi = 0; oi < numOutcomes; oi++) allOutcomesPattern[oi] = oi; /* Stores the estimated parameter value of each predicate during iteration. */ MutableContext[] params = new MutableContext[numPreds]; for (int pi = 0; pi < numPreds; pi++) { params[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]); for (int aoi = 0; aoi < numOutcomes; aoi++) params[pi].setParameter(aoi, 0.0); } EvalParameters evalParams = new EvalParameters(params, numOutcomes); double stepSize = 1; for (int ei = 0; ei < numUniqueEvents; ei++) { int targetOutcome = outcomeList[ei]; for (int ni = 0; ni < this.numTimesEventsSeen[ei]; ni++) { for (int ci = 0; ci < contexts[ei].length; ci++) { int pi = contexts[ei][ci]; if (values == null) { params[pi].updateParameter(targetOutcome, stepSize); } else { params[pi].updateParameter(targetOutcome, stepSize * values[ei][ci]); } } } } // Output the final training stats. trainingStats(evalParams); return params; }
private MutableContext[] findParameters() { int[] allOutcomesPattern = new int[numOutcomes]; for (int oi = 0; oi < numOutcomes; oi++) allOutcomesPattern[oi] = oi; /* Stores the estimated parameter value of each predicate during iteration. */ MutableContext[] params = new MutableContext[numPreds]; for (int pi = 0; pi < numPreds; pi++) { params[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]); for (int aoi = 0; aoi < numOutcomes; aoi++) params[pi].setParameter(aoi, 0.0); } EvalParameters evalParams = new EvalParameters(params, numOutcomes); double stepSize = 1; for (int ei = 0; ei < numUniqueEvents; ei++) { int targetOutcome = outcomeList[ei]; for (int ni = 0; ni < this.numTimesEventsSeen[ei]; ni++) { for (int ci = 0; ci < contexts[ei].length; ci++) { int pi = contexts[ei][ci]; if (values == null) { params[pi].updateParameter(targetOutcome, stepSize); } else { params[pi].updateParameter(targetOutcome, stepSize * values[ei][ci]); } } } } // Output the final training stats. trainingStats(evalParams); return params; }
EvalParameters evalParams = new EvalParameters(params, numOutcomes);
EvalParameters evalParams = new EvalParameters(params, numOutcomes);
evalParams = new EvalParameters(params, numOutcomes); int[] activeOutcomes = new int[numOutcomes]; int[] outcomePattern;
evalParams = new EvalParameters(params, numOutcomes); int[] activeOutcomes = new int[numOutcomes]; int[] outcomePattern;