Java 类名：com.alibaba.alink.pipeline.feature.DCT
Python 类名：DCT

功能介绍

对数据进行离散余弦变换。

参数说明

名称 中文名称 描述 类型 是否必须？ 取值范围 默认值
selectedCol 选中的列名 计算列对应的列名 String ✓
inverse 是否为逆变换 是否为逆变换，false表示正变换，true表示逆变换。默认正变换。 Boolean false
outputCol 输出结果列 输出结果列列名，可选，默认null String null
reservedCols 算法保留列名 算法保留列 String[] null
numThreads 组件多线程线程个数 组件多线程线程个数 Integer 1

代码示例

Python 代码

  1. from pyalink.alink import *
  3. import pandas as pd
  5. useLocalEnv(1)
  7. df_data = pd.DataFrame([
  8.     ["-0.6264538 0.1836433"],
  9.     ["-0.8356286 1.5952808"],
  10.     ["0.3295078 -0.8204684"],
  11.     ["0.4874291 0.7383247"],
  12.     ["0.5757814 -0.3053884"],
  13.     ["1.5117812 0.3898432"],
  14.     ["-0.6212406 -2.2146999"],
  15.     ["11.1249309 9.9550664"],
  16.     ["9.9838097 10.9438362"],
  17.     ["10.8212212 10.5939013"],
  18.     ["10.9189774 10.7821363"],
  19.     ["10.0745650 8.0106483"],
  20.     ["10.6198257 9.9438713"],
  21.     ["9.8442045 8.5292476"],
  22.     ["9.5218499 10.4179416"],
  23. ])
  25. data = BatchOperator.fromDataframe(df_data, schemaStr='features string')
  27. dct = DCT().setSelectedCol("features").setOutputCol("result")
  29. dct.transform(data).print()

Java 代码

  1. import org.apache.flink.types.Row;
  3. import com.alibaba.alink.operator.batch.BatchOperator;
  4. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  5. import com.alibaba.alink.pipeline.feature.DCT;
  6. import org.junit.Test;
  8. import java.util.Arrays;
  9. import java.util.List;
  11. public class DCTTest {
  12.     @Test
  13.     public void testDCT() throws Exception {
  14.         List <Row> df_data = Arrays.asList(
  15.             Row.of("-0.6264538 0.1836433"),
  16.             Row.of("-0.8356286 1.5952808"),
  17.             Row.of("0.3295078 -0.8204684"),
  18.             Row.of("0.4874291 0.7383247"),
  19.             Row.of("0.5757814 -0.3053884"),
  20.             Row.of("1.5117812 0.3898432"),
  21.             Row.of("-0.6212406 -2.2146999"),
  22.             Row.of("11.1249309 9.9550664"),
  23.             Row.of("9.9838097 10.9438362"),
  24.             Row.of("10.8212212 10.5939013"),
  25.             Row.of("10.9189774 10.7821363"),
  26.             Row.of("10.0745650 8.0106483"),
  27.             Row.of("10.6198257 9.9438713"),
  28.             Row.of("9.8442045 8.5292476"),
  29.             Row.of("9.5218499 10.4179416")
  30.         );
  31.         BatchOperator <?> data = new MemSourceBatchOp(df_data, "features string");
  32.         DCT dct = new DCT().setSelectedCol("features").setOutputCol("result");
  33.         dct.transform(data).print();
  34.     }
  35. }

运行结果

| features | result | | —- | —- |

| -0.6264538 0.1836433 | -0.31311430733060563 -0.5728251528295567 |

| -0.8356286 1.5952808 | 0.5371552219632794 -1.7189125211901217 |

| 0.3295078 -0.8204684 | -0.34716156955541605 0.8131559692231375 |

| 0.4874291 0.7383247 | 0.866738824045179 -0.17740998012986753 |

| 0.5757814 -0.3053884 | 0.19119672388537412 0.6230811409567939 |

| 1.5117812 0.3898432 | 1.3446515085097996 0.7933299678708727 |

| -0.6212406 -2.2146999 | -2.005312758591568 1.126745876574769 |

| 11.1249309 9.9550664 | 14.905809038224113 0.8272191210194105 |

| 9.9838097 10.9438362 | 14.798080330160849 -0.6788412482687869 |

| 10.8212212 10.5939013 | 15.142778339690611 0.1607394427886475 |

| 10.9189774 10.7821363 | 15.345004656570287 0.09676126975502636 |

| 10.0745650 8.0106483 | 12.788176963635138 1.4594094943741613 |

| 10.6198257 9.9438713 | 14.54072959496546 0.4779719400128842 |

| 9.8442045 8.5292476 | 12.991992573716212 0.9298149409580412 |

| 9.5218499 10.4179416 | 14.099561785095878 -0.6336325176349823 |