我应该在同一个类中实现这两个接口吗？

如何解决我应该在同一个类中实现这两个接口吗？

我有两个接口：Normalizer 和 ScoringSummary 如下：

1. 标准化器：

   public interface Normalizer {
   
         /**
         * Accepts a <code>csvPath</code> for a CSV file,perform a Z-Score normalization against
         * <code>colToStandardize</code>,then generate the result file with additional scored column to
         * <code>destPath</code>.
         *
         * @param csvPath          path of CSV file to read
         * @param destPath         path to which the scaled CSV file should be written
         * @param colToStandardize the name of the column to normalize
         * @return
         */
        ScoringSummary zscore(Path csvPath,Path destPath,String colToStandardize);
   
        /**
         * Accepts a <code>csvPath</code> for a CSV file,perform a Min-Max normalization against
         * <code>colToNormalize</code>,then generate the result file with additional scored column to
         * <code>destPath</code>.
         *
         * @param csvPath          path of CSV file to read
         * @param destPath         path to which the scaled CSV file should be written
         * @param colToNormalize the name of the column to normalize
         * @return
         */
        ScoringSummary minMaxScaling(Path csvPath,String colToNormalize);
    }
   

2. 评分总结：

   public interface ScoringSummary {
   
       public BigDecimal mean();
   
       public BigDecimal standardDeviation();
   
       public BigDecimal variance();
   
       public BigDecimal median();
   
       public BigDecimal min();
   
       public BigDecimal max();
   }
   

这是来自 TDD 的一个函数：

@Test
    public void givenMarksCSVFileToScale_whenMarkColumnIsZScored_thenNewCSVFileIsGeneratedWithAdditionalZScoreColumn() throws IOException {
        String filename = "marks.csv";
        Path induction = Files.createTempDirectory("induction");
        String columnName = "mark";
        Path csvPath = induction.resolve(filename);
        Path destPath = induction.resolve("marks_scaled.csv");
        copyFile("/marks.csv",csvPath);
        Assertions.assertTrue(Files.exists(csvPath));

        Normalizer normalizer = normalizer();
        ScoringSummary summary = normalizer.zscore(csvPath,destPath,columnName);
        Assertions.assertNotNull(summary,"the returned summary is null");

        Assertions.assertEquals(new BigDecimal("66.00"),summary.mean(),"invalid mean");
        Assertions.assertEquals(new BigDecimal("16.73"),summary.standardDeviation(),"invalid standard deviation");
        Assertions.assertEquals(new BigDecimal("280.00"),summary.variance(),"invalid variance");
        Assertions.assertEquals(new BigDecimal("65.00"),summary.median(),"invalid median");
        Assertions.assertEquals(new BigDecimal("40.00"),summary.min(),"invalid min value");
        Assertions.assertEquals(new BigDecimal("95.00"),summary.max(),"invalid maximum value");

        Assertions.assertTrue(Files.exists(destPath),"the destination file does not exists");
        Assertions.assertFalse(Files.isDirectory(destPath),"the destination is not a file");

        List<String> generatedLines = Files.readAllLines(destPath);
        Path assertionPath = copyFile("/marks_z.csv",induction.resolve("marks_z.csv"));
        List<String> expectedLines = Files.readAllLines(assertionPath);
        assertLines(generatedLines,expectedLines);
    }

如何在一个java类中实现这两个接口？ 我是否需要任何依赖项或其他框架来解析 CSV？

解决方法

您不一定需要依赖项或框架来处理 CSV 数据。但是，使用现有库比自己实现所有内容要容易得多。

实现这两个接口有很多不同的方法。您的实施只需要履行他们的合同。以下是一些示例：

两个独立的类

public class NormalizerImplSplit implements Normalizer {

    @Override
    public ScoringSummary zscore(Path csvPath,Path destPath,String colToStandardize) {
        // process CSV and store summary results
        ScoringSummaryImpl summary = new ScoringSummaryImpl();
        summary.setMean(new BigDecimal("66.00"));

        // return summary object
        return summary;
    }

    // other method of Normalizer

}

public class ScoringSummaryImpl implements ScoringSummary {

    private BigDecimal mean;

    public void setMean(BigDecimal mean) {
        this.mean = mean;
    }

    @Override
    public BigDecimal mean() {
        return this.mean;
    }

    // other methods of ScoringSummary
}

Normalizer 实现与嵌套的 ScoringSummary 实现

public class NormalizerImplNested implements Normalizer {

    @Override
    public ScoringSummary zscore(Path csvPath,String colToStandardize) {
        // process CSV and store summary results
        ScoringSummaryImpl summary = new ScoringSummaryImpl();
        summary.setMean(new BigDecimal("66.00"));

        // return summary object
        return summary;
    }

    // other method of Normalizer

    public static class ScoringSummaryImpl implements ScoringSummary {

        private BigDecimal mean;

        private void setMean(BigDecimal mean) {
            this.mean = mean;
        }

        @Override
        public BigDecimal mean() {
            return this.mean;
        }

        // other methods of ScoringSummary
    }
}

单个类实现 Normalizer 和 ScoringSummary

public class NormalizerImpl implements Normalizer,ScoringSummary {

    private BigDecimal mean;

    @Override
    public ScoringSummary zscore(Path csvPath,String colToStandardize) {
        // process CSV and store summary results
        this.mean = new BigDecimal("66.00");

        // return this instance since ScoringSummary is also implemented
        return this;
    }

    @Override
    public BigDecimal mean() {
        return this.mean;
    }

    // other methods of the two interfaces

}