public double[] eval(String[] context, double[] probs) { return eval(context, null, probs); }
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()]); }
public double[] eval(String[] context, double[] probs) { return eval(context, null, probs); }
/** * 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.argmax(probs); if (outcome == outcomeList[ei]) { nCorrect += nEventsSeen[ei]; } nTotalEvents += nEventsSeen[ei]; } return (double) nCorrect / nTotalEvents; } }
public double[] eval(String[] context, double[] probs) { return eval(context, null, probs); }
@Test public void testInTinyDevSet() throws Exception { // given RealValueFileEventStream rvfes1 = new RealValueFileEventStream( "src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt"); testDataIndexer.index(rvfes1);; // when QNModel trainedModel = new QNTrainer(15, true).trainModel(ITERATIONS, testDataIndexer); String[] features2Classify = new String[] { "feature2","feature3", "feature3", "feature3","feature3", "feature3", "feature3","feature3", "feature3", "feature3","feature3", "feature3"}; double[] eval = trainedModel.eval(features2Classify); // then Assert.assertNotNull(eval); }
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()]); }
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()]); }
/** * 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; } }
/** * 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.argmax(probs); if (outcome == outcomeList[ei]) { nCorrect += nEventsSeen[ei]; } nTotalEvents += nEventsSeen[ei]; } return (double) nCorrect / nTotalEvents; } }