@Override public int maxValueIndex() { return delegate.maxValueIndex(); }
@Override public int maxValueIndex() { return delegate.maxValueIndex(); }
assertEquals(-1.0, max, 0.0); int idx = vec1.maxValueIndex(); assertEquals(0, idx); assertEquals(0.0, max, 0.0); idx = vec1.maxValueIndex(); assertEquals(1, idx); assertEquals(0.0, max, 0.0); idx = vec1.maxValueIndex(); assertEquals(1, idx); assertEquals(0.0, max, 0.0); idx = vec1.maxValueIndex(); assertEquals(1, idx);
assertEquals(1.0, max, 0.0); int idx = vec1.maxValueIndex(); assertEquals(1, idx); assertEquals(0.0, max, 0.0); idx = vec1.maxValueIndex(); assertEquals(1, idx); assertEquals(0.0, max, 0.0); idx = vec1.maxValueIndex(); assertEquals(1, idx); assertEquals(0.0, max, 0.0); idx = vec1.maxValueIndex(); assertEquals(1, idx);
@Override public int maxValueIndex() { return delegate.maxValueIndex(); }
@Override public int maxValueIndex() { return delegate.maxValueIndex(); }
assertEquals(dv1.maxValueIndex(), v1.maxValueIndex());
@Override public Vector select(Vector probabilities) { int maxValueIndex = probabilities.maxValueIndex(); Vector weights = new SequentialAccessSparseVector(probabilities.size()); weights.set(maxValueIndex, 1.0); return weights; }
@Override public Vector select(Vector probabilities) { int maxValueIndex = probabilities.maxValueIndex(); Vector weights = new SequentialAccessSparseVector(probabilities.size()); weights.set(maxValueIndex, 1.0); return weights; }
@Override public Vector select(Vector probabilities) { int maxValueIndex = probabilities.maxValueIndex(); Vector weights = new SequentialAccessSparseVector(probabilities.size()); weights.set(maxValueIndex, 1.0); return weights; }
@Override public Vector select(Vector probabilities) { int maxValueIndex = probabilities.maxValueIndex(); Vector weights = new SequentialAccessSparseVector(probabilities.size()); weights.set(maxValueIndex, 1.0); return weights; }
@Override public Vector select(Vector probabilities) { int maxValueIndex = probabilities.maxValueIndex(); Vector weights = new SequentialAccessSparseVector(probabilities.size()); weights.set(maxValueIndex, 1.0); return weights; }
@Override public Vector select(Vector probabilities) { int maxValueIndex = probabilities.maxValueIndex(); Vector weights = new SequentialAccessSparseVector(probabilities.size()); weights.set(maxValueIndex, 1.0); return weights; }
public String predict(Map<String, Object> features) { Vector v = encoder.getVector(features); int o = learn.classifyFull(v).maxValueIndex(); return encoder.outputIntToString(o); }
private static void classifyAndWrite(List<Cluster> clusterModels, Double clusterClassificationThreshold, boolean emitMostLikely, SequenceFile.Writer writer, VectorWritable vw, Vector pdfPerCluster) throws IOException { Map<Text, Text> props = Maps.newHashMap(); if (emitMostLikely) { int maxValueIndex = pdfPerCluster.maxValueIndex(); WeightedPropertyVectorWritable weightedPropertyVectorWritable = new WeightedPropertyVectorWritable(pdfPerCluster.maxValue(), vw.get(), props); write(clusterModels, writer, weightedPropertyVectorWritable, maxValueIndex); } else { writeAllAboveThreshold(clusterModels, clusterClassificationThreshold, writer, vw, pdfPerCluster); } }
private static void classifyAndWrite(List<Cluster> clusterModels, Double clusterClassificationThreshold, boolean emitMostLikely, SequenceFile.Writer writer, VectorWritable vw, Vector pdfPerCluster) throws IOException { Map<Text, Text> props = new HashMap<>(); if (emitMostLikely) { int maxValueIndex = pdfPerCluster.maxValueIndex(); WeightedPropertyVectorWritable weightedPropertyVectorWritable = new WeightedPropertyVectorWritable(pdfPerCluster.maxValue(), vw.get(), props); write(clusterModels, writer, weightedPropertyVectorWritable, maxValueIndex); } else { writeAllAboveThreshold(clusterModels, clusterClassificationThreshold, writer, vw, pdfPerCluster); } }
private static void classifyAndWrite(List<Cluster> clusterModels, Double clusterClassificationThreshold, boolean emitMostLikely, SequenceFile.Writer writer, VectorWritable vw, Vector pdfPerCluster) throws IOException { Map<Text, Text> props = Maps.newHashMap(); if (emitMostLikely) { int maxValueIndex = pdfPerCluster.maxValueIndex(); WeightedPropertyVectorWritable weightedPropertyVectorWritable = new WeightedPropertyVectorWritable(pdfPerCluster.maxValue(), vw.get(), props); write(clusterModels, writer, weightedPropertyVectorWritable, maxValueIndex); } else { writeAllAboveThreshold(clusterModels, clusterClassificationThreshold, writer, vw, pdfPerCluster); } }
@Override public Vector classify(Vector instance) { Vector result = classifyNoLink(instance); // Find the max value's index. int max = result.maxValueIndex(); result.assign(0); result.setQuick(max, 1.0); return result.viewPart(1, result.size() - 1); }
@Override public Vector classify(Vector instance) { Vector result = classifyNoLink(instance); // Find the max value's index. int max = result.maxValueIndex(); result.assign(0); result.setQuick(max, 1.0); return result.viewPart(1, result.size() - 1); }
@Override public Vector classify(Vector instance) { Vector result = classifyNoLink(instance); // Find the max value's index. int max = result.maxValueIndex(); result.assign(0); result.setQuick(max, 1.0); return result.viewPart(1, result.size() - 1); }