向量元素两两相乘 (VectorInteractionStreamOp)

Java 类名：com.alibaba.alink.operator.stream.dataproc.vector.VectorInteractionStreamOp

Python 类名：VectorInteractionStreamOp

功能介绍

对于输入的两个vector中的元素两两相乘，并组成一个新的向量。

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
outputCol	输出结果列列名	输出结果列列名，必选	String	✓
selectedCols	选择的列名	计算列对应的列名列表	String[]	✓	所选列类型为 [DENSE_VECTOR, SPARSE_VECTOR, STRING, VECTOR]
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

备注：选择列的数目必须为两列

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
    ["$8$1:3,2:4,4:7", "$8$1:3,2:4,4:7"],
    ["$8$0:3,5:5", "$8$1:2,2:4,4:7"],
    ["$8$2:4,4:5", "$8$1:3,2:3,4:7"]
])

data = StreamOperator.fromDataframe(df, schemaStr="vec1 string, vec2 string")
vecInter = VectorInteractionStreamOp().setSelectedCols(["vec1","vec2"]).setOutputCol("vec_product")
data.link(vecInter).print()
StreamOperator.execute()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.vector.VectorInteractionStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

import java.util.Arrays;
import java.util.List;

public class VectorInteractionStreamOpTest {
	@Test
	public void testVectorInteractionStreamOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of("$8$1:3,2:4,4:7", "$8$1:3,2:4,4:7"),
			Row.of("$8$0:3,5:5", "$8$1:2,2:4,4:7"),
			Row.of("$8$2:4,4:5", "$8$1:3,2:3,4:7")
		);
		StreamOperator <?> data = new MemSourceStreamOp(df, "vec1 string, vec2 string");
		StreamOperator <?> vecInter = new VectorInteractionStreamOp().setSelectedCols("vec1", "vec2").setOutputCol(
			"vec_product");
		data.link(vecInter).print();
		StreamOperator.execute();
	}
}

运行结果

vec1	vec2	vec_product
$8$2:4,4:5	$8$1:3,2:3,4:7	$64$10:12.0 12:15.0 18:12.0 20:15.0 34:28.0 36:35.0
$8$1:3,2:4,4:7	$8$1:3,2:4,4:7	$64$9:9.0 10:12.0 12:21.0 17:12.0 18:16.0 20:28.0 33:21.0 34:28.0 36:49.0
$8$0:3,5:5	$8$1:2,2:4,4:7	$64$8:6.0 13:10.0 16:12.0 21:20.0 32:21.0 37:35.0