contextParameters[k-1] = readDouble(); params[pid] = new Context(outcomePattern,contextParameters); pid++;
contextParameters[k-1] = readDouble(); params[pid] = new Context(outcomePattern,contextParameters); pid++;
contextParameters[k - 1] = readDouble(); params[pid] = new Context(outcomePattern, contextParameters); pid++;
contextParameters[k - 1] = readDouble(); params[pid] = new Context(outcomePattern, contextParameters); pid++;
protected Context[] getParameters() throws java.io.IOException { int numContext = readInt(); Context[] params = new Context[numContext]; for (int i = 0; i < numContext; i++) { int[] outcomePattern = getIntArrayParams(); double[] parameters = getDoubleArrayParams(); params[i] = new Context(outcomePattern, parameters); } return params; } }
protected Context[] getParameters() throws java.io.IOException { int numContext = readInt(); Context[] params = new Context[numContext]; for (int i = 0; i < numContext; i++) { int[] outcomePattern = getIntArrayParams(); double[] parameters = getDoubleArrayParams(); params[i] = new Context(outcomePattern, parameters); } return params; } }
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; }