我应该在同一个类中实现这两个接口吗

Question

我有两个接口：Normalizer和ScoringSummary，如下所示：

1. 规格化器：

   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);
   }
   

2. 评分汇总：

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

这里有一个来自TDD:的函数

@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？我需要任何依赖项或其他框架来解析CSV吗？

Answer 1

您不一定需要依赖项或框架来处理CSV数据。然而，使用现有的库要比自己实现所有内容容易得多。

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

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, 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
}