public static double max(double[] x) { int maxIdx = maxIdx(x); return x[maxIdx]; }
/** * Evaluate the current model on training data set * @return model's training accuracy */ @Override public double evaluate(double[] parameters) { int[][] contexts = indexer.getContexts(); float[][] values = indexer.getValues(); int[] nEventsSeen = indexer.getNumTimesEventsSeen(); int[] outcomeList = indexer.getOutcomeList(); int nOutcomes = indexer.getOutcomeLabels().length; int nPredLabels = indexer.getPredLabels().length; int nCorrect = 0; int nTotalEvents = 0; for (int ei = 0; ei < contexts.length; ei++) { int[] context = contexts[ei]; float[] value = values == null ? null : values[ei]; double[] probs = new double[nOutcomes]; QNModel.eval(context, value, probs, nOutcomes, nPredLabels, parameters); int outcome = ArrayMath.maxIdx(probs); if (outcome == outcomeList[ei]) { nCorrect += nEventsSeen[ei]; } nTotalEvents += nEventsSeen[ei]; } return (double) nCorrect / nTotalEvents; } }