我应该在同一个类中实现这两个接口吗?

vddsk6oq  于 2021-08-20  发布在  Java
关注(0)|答案(1)|浏览(402)

我有两个接口: NormalizerScoringSummary 详情如下:
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, Path destPath, String colToNormalize);
 }

轻蔑摘要:

public interface ScoringSummary {

    public BigDecimal mean();

    public BigDecimal standardDeviation();

    public BigDecimal variance();

    public BigDecimal median();

    public BigDecimal min();

    public BigDecimal max();
}

这是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?我是否需要依赖项或其他框架来解析csv?

waxmsbnn

waxmsbnn1#

处理csv数据不一定需要依赖项或框架。但是,使用现有库要比自己实现所有功能容易得多。
有许多不同的方法来实现这两个接口。您的实现只需要履行他们的合同。以下是一些例子:

两个独立的班级
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
}
带有嵌套scoringsummary实现的normalizer实现
public class NormalizerImplNested 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 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
    }
}
实现normalizer和scoringsummary的单个类
public class NormalizerImpl implements Normalizer, ScoringSummary {

    private BigDecimal mean;

    @Override
    public ScoringSummary zscore(Path csvPath,Path destPath,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

}

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