public AbstractModel(Context[] params, String[] predLabels, IndexHashTable<String> pmap, String[] outcomeNames) { this.pmap = pmap; this.outcomeNames = outcomeNames; this.evalParams = new EvalParameters(params,outcomeNames.length); }
public AbstractModel(Context[] params, String[] predLabels, IndexHashTable<String> pmap, String[] outcomeNames) { this.pmap = pmap; this.outcomeNames = outcomeNames; this.evalParams = new EvalParameters(params,outcomeNames.length); }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames, int correctionConstant,double correctionParam) { init(predLabels,outcomeNames); this.evalParams = new EvalParameters(params,correctionParam,correctionConstant,outcomeNames.length); }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames) { init(predLabels,outcomeNames); this.evalParams = new EvalParameters(params,outcomeNames.length); }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames) { init(predLabels,outcomeNames); this.evalParams = new EvalParameters(params,outcomeNames.length); }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames, int correctionConstant,double correctionParam) { init(predLabels,outcomeNames); this.evalParams = new EvalParameters(params,correctionParam,correctionConstant,outcomeNames.length); }
public QNModel(LogLikelihoodFunction monitor, double[] parameters) { super(null, monitor.getPredLabels(), monitor.getOutcomeLabels()); int[][] outcomePatterns = monitor.getOutcomePatterns(); Context[] params = new Context[monitor.getPredLabels().length]; for (int ci = 0; ci < params.length; ci++) { int[] outcomePattern = outcomePatterns[ci]; double[] alpha = new double[outcomePattern.length]; for (int oi = 0; oi < outcomePattern.length; oi++) { alpha[oi] = parameters[ci + (outcomePattern[oi] * monitor.getPredLabels().length)]; } params[ci] = new Context(outcomePattern, alpha); } this.evalParams = new EvalParameters(params, monitor.getOutcomeLabels().length); this.prior = new UniformPrior(); this.modelType = ModelType.MaxentQn; this.parameters = parameters; }
public QNModel(LogLikelihoodFunction monitor, double[] parameters) { super(null, monitor.getPredLabels(), monitor.getOutcomeLabels()); int[][] outcomePatterns = monitor.getOutcomePatterns(); Context[] params = new Context[monitor.getPredLabels().length]; for (int ci = 0; ci < params.length; ci++) { int[] outcomePattern = outcomePatterns[ci]; double[] alpha = new double[outcomePattern.length]; for (int oi = 0; oi < outcomePattern.length; oi++) { alpha[oi] = parameters[ci + (outcomePattern[oi] * monitor.getPredLabels().length)]; } params[ci] = new Context(outcomePattern, alpha); } this.evalParams = new EvalParameters(params, monitor.getOutcomeLabels().length); this.prior = new UniformPrior(); this.modelType = ModelType.MaxentQn; this.parameters = parameters; }
evalParams = new EvalParameters(params, 0, 1, labels.length); int[] activeOutcomes = new int[labels.length]; int[] labelPattern = new int[labels.length];
EvalParameters evalParams = new EvalParameters(params,numOutcomes);
EvalParameters evalParams = new EvalParameters(params,numOutcomes);
evalParams = new EvalParameters(params,0,1,numOutcomes); int[] activeOutcomes = new int[numOutcomes]; int[] outcomePattern;
evalParams = new EvalParameters(params, 0, 1, numOutcomes); int[] activeOutcomes = new int[numOutcomes]; int[] outcomePattern;
evalParams = new EvalParameters(params,0,1,numOutcomes); int[] activeOutcomes = new int[numOutcomes]; int[] outcomePattern;