private StandardScalerData computeSum()

in modules/ml-ext/ml/src/main/java/org/apache/ignite/ml/preprocessing/standardscaling/StandardScalerTrainer.java [56:104]

44 lines of code
7 McCabe index (conditional complexity)


    private StandardScalerData computeSum(LearningEnvironmentBuilder envBuilder,
        DatasetBuilder<K, V> datasetBuilder,
        Preprocessor<K, V> basePreprocessor) {
        try (Dataset<EmptyContext, StandardScalerData> dataset = datasetBuilder.build(
            envBuilder,
            (env, upstream, upstreamSize) -> new EmptyContext(),
            (env, upstream, upstreamSize, ctx) -> {
                double[] sum = null;
                double[] squaredSum = null;
                long cnt = 0;

                while (upstream.hasNext()) {
                    UpstreamEntry<K, V> entity = upstream.next();
                    Vector row = basePreprocessor.apply(entity.getKey(), entity.getValue()).features();

                    if (sum == null) {
                        sum = new double[row.size()];
                        squaredSum = new double[row.size()];
                    }
                    else {
                        assert sum.length == row.size() : "Base preprocessor must return exactly " + sum.length
                            + " features";
                    }

                    ++cnt;
                    for (int i = 0; i < row.size(); i++) {
                        double x = row.get(i);
                        sum[i] += x;
                        squaredSum[i] += x * x;
                    }
                }
                return new StandardScalerData(sum, squaredSum, cnt);
            }, learningEnvironment(basePreprocessor)
        )) {

            return dataset.compute(data -> data,
                (a, b) -> {
                    if (a == null)
                        return b;
                    if (b == null)
                        return a;

                    return a.merge(b);
                });
        }
        catch (Exception e) {
            throw new RuntimeException(e);
        }
    }