我有两个接口:Normalizer
和 ScoringSummary
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);
}
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?
答案 0 :(得分:1)
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:
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
}
}
Normalizer
和 ScoringSummary
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
}