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Educational library for machine learning challenges

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package de.edux.data.provider;

import java.util.List;

public class DataNormalizer implements Normalizer {

  @Override
  public List normalize(List dataset) {
    if (dataset == null || dataset.isEmpty()) {
      return dataset;
    }

    int columnCount = dataset.get(0).length;

    double[] minValues = new double[columnCount];
    double[] maxValues = new double[columnCount];
    boolean[] isNumericColumn = new boolean[columnCount];

    for (int i = 0; i < columnCount; i++) {
      minValues[i] = Double.MAX_VALUE;
      maxValues[i] = -Double.MAX_VALUE;
      isNumericColumn[i] = true;
    }

    for (String[] row : dataset) {
      for (int colIndex = 0; colIndex < columnCount; colIndex++) {
        try {
          double numValue = Double.parseDouble(row[colIndex]);

          if (numValue < minValues[colIndex]) {
            minValues[colIndex] = numValue;
          }
          if (numValue > maxValues[colIndex]) {
            maxValues[colIndex] = numValue;
          }
        } catch (NumberFormatException e) {
          isNumericColumn[colIndex] = false;
        }
      }
    }

    for (String[] row : dataset) {
      for (int colIndex = 0; colIndex < columnCount; colIndex++) {
        if (isNumericColumn[colIndex]) {
          double numValue = Double.parseDouble(row[colIndex]);
          double range = maxValues[colIndex] - minValues[colIndex];

          if (range != 0.0) {
            double normalized = (numValue - minValues[colIndex]) / range;
            row[colIndex] = String.valueOf(normalized);
          } else {
            row[colIndex] = "0";
          }
        }
      }
    }

    return dataset;
  }
}