/
KMeansExample.java
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/
KMeansExample.java
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package com.alibaba.alink;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.source.CsvSourceBatchOp;
import com.alibaba.alink.pipeline.Pipeline;
import com.alibaba.alink.pipeline.clustering.KMeans;
import com.alibaba.alink.pipeline.dataproc.vector.VectorAssembler;
/**
* Example for KMeans.
*/
public class KMeansExample {
public static void main(String[] args) throws Exception {
String URL = "https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/iris.csv";
String SCHEMA_STR = "sepal_length double, sepal_width double, petal_length double, petal_width double, category string";
BatchOperator data = new CsvSourceBatchOp().setFilePath(URL).setSchemaStr(SCHEMA_STR);
VectorAssembler va = new VectorAssembler()
.setSelectedCols(new String[]{"sepal_length", "sepal_width", "petal_length", "petal_width"})
.setOutputCol("features");
KMeans kMeans = new KMeans().setVectorCol("features").setK(3)
.setPredictionCol("prediction_result")
.setPredictionDetailCol("prediction_detail")
.setReservedCols("category")
.setMaxIter(100);
Pipeline pipeline = new Pipeline().add(va).add(kMeans);
pipeline.fit(data).transform(data).print();
}
}