• 设为首页
  • 点击收藏
  • 手机版
    手机扫一扫访问
    迪恩网络手机版
  • 关注官方公众号
    微信扫一扫关注
    公众号
登陆 注册

Java Vector类代码示例

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

本文整理汇总了Java中org.apache.spark.ml.linalg.Vector类的典型用法代码示例。如果您正苦于以下问题：Java Vector类的具体用法？Java Vector怎么用？Java Vector使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。



Vector类属于org.apache.spark.ml.linalg包，在下文中一共展示了Vector类的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Java代码示例。

示例1: encodeFeatures

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder){
	IDFModel transformer = getTransformer();

	List<Feature> features = encoder.getFeatures(transformer.getInputCol());

	Vector idf = transformer.idf();
	if(idf.size() != features.size()){
		throw new IllegalArgumentException();
	}

	List<Feature> result = new ArrayList<>();

	for(int i = 0; i < features.size(); i++){
		Feature feature = features.get(i);

		TermFeature termFeature = (TermFeature)feature;

		result.add(termFeature.toWeightedTermFeature(idf.apply(i)));
	}

	return result;
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:24，代码来源:IDFModelConverter.java


示例2: encodeModel

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Override
public ClusteringModel encodeModel(Schema schema){
	KMeansModel model = getTransformer();

	List<Cluster> clusters = new ArrayList<>();

	Vector[] clusterCenters = model.clusterCenters();
	for(int i = 0; i < clusterCenters.length; i++){
		Cluster cluster = new Cluster()
			.setId(String.valueOf(i))
			.setArray(PMMLUtil.createRealArray(VectorUtil.toList(clusterCenters[i])));

		clusters.add(cluster);
	}

	ComparisonMeasure comparisonMeasure = new ComparisonMeasure(ComparisonMeasure.Kind.DISTANCE)
		.setCompareFunction(CompareFunction.ABS_DIFF)
		.setMeasure(new SquaredEuclidean());

	return new ClusteringModel(MiningFunction.CLUSTERING, ClusteringModel.ModelClass.CENTER_BASED, clusters.size(), ModelUtil.createMiningSchema(schema.getLabel()), comparisonMeasure, ClusteringModelUtil.createClusteringFields(schema.getFeatures()), clusters);
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:22，代码来源:KMeansModelConverter.java


示例3: testDataFrameSumDMLVectorWithIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumDMLVectorWithIDColumn() {
	System.out.println("MLContextTest - DataFrame sum DML, vector with ID column");

	List<Tuple2<Double, Vector>> list = new ArrayList<Tuple2<Double, Vector>>();
	list.add(new Tuple2<Double, Vector>(1.0, Vectors.dense(1.0, 2.0, 3.0)));
	list.add(new Tuple2<Double, Vector>(2.0, Vectors.dense(4.0, 5.0, 6.0)));
	list.add(new Tuple2<Double, Vector>(3.0, Vectors.dense(7.0, 8.0, 9.0)));
	JavaRDD<Tuple2<Double, Vector>> javaRddTuple = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddTuple.map(new DoubleVectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField(RDDConverterUtils.DF_ID_COLUMN, DataTypes.DoubleType, true));
	fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR_WITH_INDEX);

	Script script = dml("print('sum: ' + sum(M));").in("M", dataFrame, mm);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:24，代码来源:MLContextTest.java


示例4: testDataFrameSumPYDMLVectorWithIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumPYDMLVectorWithIDColumn() {
	System.out.println("MLContextTest - DataFrame sum PYDML, vector with ID column");

	List<Tuple2<Double, Vector>> list = new ArrayList<Tuple2<Double, Vector>>();
	list.add(new Tuple2<Double, Vector>(1.0, Vectors.dense(1.0, 2.0, 3.0)));
	list.add(new Tuple2<Double, Vector>(2.0, Vectors.dense(4.0, 5.0, 6.0)));
	list.add(new Tuple2<Double, Vector>(3.0, Vectors.dense(7.0, 8.0, 9.0)));
	JavaRDD<Tuple2<Double, Vector>> javaRddTuple = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddTuple.map(new DoubleVectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField(RDDConverterUtils.DF_ID_COLUMN, DataTypes.DoubleType, true));
	fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR_WITH_INDEX);

	Script script = pydml("print('sum: ' + sum(M))").in("M", dataFrame, mm);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:24，代码来源:MLContextTest.java


示例5: testDataFrameSumDMLMllibVectorWithIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumDMLMllibVectorWithIDColumn() {
	System.out.println("MLContextTest - DataFrame sum DML, mllib vector with ID column");

	List<Tuple2<Double, org.apache.spark.mllib.linalg.Vector>> list = new ArrayList<Tuple2<Double, org.apache.spark.mllib.linalg.Vector>>();
	list.add(new Tuple2<Double, org.apache.spark.mllib.linalg.Vector>(1.0,
			org.apache.spark.mllib.linalg.Vectors.dense(1.0, 2.0, 3.0)));
	list.add(new Tuple2<Double, org.apache.spark.mllib.linalg.Vector>(2.0,
			org.apache.spark.mllib.linalg.Vectors.dense(4.0, 5.0, 6.0)));
	list.add(new Tuple2<Double, org.apache.spark.mllib.linalg.Vector>(3.0,
			org.apache.spark.mllib.linalg.Vectors.dense(7.0, 8.0, 9.0)));
	JavaRDD<Tuple2<Double, org.apache.spark.mllib.linalg.Vector>> javaRddTuple = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddTuple.map(new DoubleMllibVectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField(RDDConverterUtils.DF_ID_COLUMN, DataTypes.DoubleType, true));
	fields.add(DataTypes.createStructField("C1", new org.apache.spark.mllib.linalg.VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR_WITH_INDEX);

	Script script = dml("print('sum: ' + sum(M));").in("M", dataFrame, mm);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:27，代码来源:MLContextTest.java


示例6: testDataFrameSumPYDMLMllibVectorWithIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumPYDMLMllibVectorWithIDColumn() {
	System.out.println("MLContextTest - DataFrame sum PYDML, mllib vector with ID column");

	List<Tuple2<Double, org.apache.spark.mllib.linalg.Vector>> list = new ArrayList<Tuple2<Double, org.apache.spark.mllib.linalg.Vector>>();
	list.add(new Tuple2<Double, org.apache.spark.mllib.linalg.Vector>(1.0,
			org.apache.spark.mllib.linalg.Vectors.dense(1.0, 2.0, 3.0)));
	list.add(new Tuple2<Double, org.apache.spark.mllib.linalg.Vector>(2.0,
			org.apache.spark.mllib.linalg.Vectors.dense(4.0, 5.0, 6.0)));
	list.add(new Tuple2<Double, org.apache.spark.mllib.linalg.Vector>(3.0,
			org.apache.spark.mllib.linalg.Vectors.dense(7.0, 8.0, 9.0)));
	JavaRDD<Tuple2<Double, org.apache.spark.mllib.linalg.Vector>> javaRddTuple = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddTuple.map(new DoubleMllibVectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField(RDDConverterUtils.DF_ID_COLUMN, DataTypes.DoubleType, true));
	fields.add(DataTypes.createStructField("C1", new org.apache.spark.mllib.linalg.VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR_WITH_INDEX);

	Script script = pydml("print('sum: ' + sum(M))").in("M", dataFrame, mm);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:27，代码来源:MLContextTest.java


示例7: testDataFrameSumDMLVectorWithNoIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumDMLVectorWithNoIDColumn() {
	System.out.println("MLContextTest - DataFrame sum DML, vector with no ID column");

	List<Vector> list = new ArrayList<Vector>();
	list.add(Vectors.dense(1.0, 2.0, 3.0));
	list.add(Vectors.dense(4.0, 5.0, 6.0));
	list.add(Vectors.dense(7.0, 8.0, 9.0));
	JavaRDD<Vector> javaRddVector = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddVector.map(new VectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR);

	Script script = dml("print('sum: ' + sum(M));").in("M", dataFrame, mm);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:23，代码来源:MLContextTest.java


示例8: testDataFrameSumPYDMLVectorWithNoIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumPYDMLVectorWithNoIDColumn() {
	System.out.println("MLContextTest - DataFrame sum PYDML, vector with no ID column");

	List<Vector> list = new ArrayList<Vector>();
	list.add(Vectors.dense(1.0, 2.0, 3.0));
	list.add(Vectors.dense(4.0, 5.0, 6.0));
	list.add(Vectors.dense(7.0, 8.0, 9.0));
	JavaRDD<Vector> javaRddVector = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddVector.map(new VectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR);

	Script script = pydml("print('sum: ' + sum(M))").in("M", dataFrame, mm);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:23，代码来源:MLContextTest.java


示例9: testDataFrameSumDMLMllibVectorWithNoIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumDMLMllibVectorWithNoIDColumn() {
	System.out.println("MLContextTest - DataFrame sum DML, mllib vector with no ID column");

	List<org.apache.spark.mllib.linalg.Vector> list = new ArrayList<org.apache.spark.mllib.linalg.Vector>();
	list.add(org.apache.spark.mllib.linalg.Vectors.dense(1.0, 2.0, 3.0));
	list.add(org.apache.spark.mllib.linalg.Vectors.dense(4.0, 5.0, 6.0));
	list.add(org.apache.spark.mllib.linalg.Vectors.dense(7.0, 8.0, 9.0));
	JavaRDD<org.apache.spark.mllib.linalg.Vector> javaRddVector = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddVector.map(new MllibVectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField("C1", new org.apache.spark.mllib.linalg.VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR);

	Script script = dml("print('sum: ' + sum(M));").in("M", dataFrame, mm);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:23，代码来源:MLContextTest.java


示例10: testDataFrameSumPYDMLMllibVectorWithNoIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumPYDMLMllibVectorWithNoIDColumn() {
	System.out.println("MLContextTest - DataFrame sum PYDML, mllib vector with no ID column");

	List<org.apache.spark.mllib.linalg.Vector> list = new ArrayList<org.apache.spark.mllib.linalg.Vector>();
	list.add(org.apache.spark.mllib.linalg.Vectors.dense(1.0, 2.0, 3.0));
	list.add(org.apache.spark.mllib.linalg.Vectors.dense(4.0, 5.0, 6.0));
	list.add(org.apache.spark.mllib.linalg.Vectors.dense(7.0, 8.0, 9.0));
	JavaRDD<org.apache.spark.mllib.linalg.Vector> javaRddVector = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddVector.map(new MllibVectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField("C1", new org.apache.spark.mllib.linalg.VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR);

	Script script = pydml("print('sum: ' + sum(M))").in("M", dataFrame, mm);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:23，代码来源:MLContextTest.java


示例11: testOutputDataFrameDMLVectorWithIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testOutputDataFrameDMLVectorWithIDColumn() {
	System.out.println("MLContextTest - output DataFrame DML, vector with ID column");

	String s = "M = matrix('1 2 3 4', rows=2, cols=2);";
	Script script = dml(s).out("M");
	MLResults results = ml.execute(script);
	Dataset<Row> dataFrame = results.getDataFrameVectorWithIDColumn("M");
	List<Row> list = dataFrame.collectAsList();

	Row row1 = list.get(0);
	Assert.assertEquals(1.0, row1.getDouble(0), 0.0);
	Assert.assertArrayEquals(new double[] { 1.0, 2.0 }, ((Vector) row1.get(1)).toArray(), 0.0);

	Row row2 = list.get(1);
	Assert.assertEquals(2.0, row2.getDouble(0), 0.0);
	Assert.assertArrayEquals(new double[] { 3.0, 4.0 }, ((Vector) row2.get(1)).toArray(), 0.0);
}
开发者ID:apache，项目名称:systemml，代码行数:19，代码来源:MLContextTest.java


示例12: testOutputDataFramePYDMLVectorWithIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testOutputDataFramePYDMLVectorWithIDColumn() {
	System.out.println("MLContextTest - output DataFrame PYDML, vector with ID column");

	String s = "M = full('1 2 3 4', rows=2, cols=2)";
	Script script = pydml(s).out("M");
	MLResults results = ml.execute(script);
	Dataset<Row> dataFrame = results.getDataFrameVectorWithIDColumn("M");
	List<Row> list = dataFrame.collectAsList();

	Row row1 = list.get(0);
	Assert.assertEquals(1.0, row1.getDouble(0), 0.0);
	Assert.assertArrayEquals(new double[] { 1.0, 2.0 }, ((Vector) row1.get(1)).toArray(), 0.0);

	Row row2 = list.get(1);
	Assert.assertEquals(2.0, row2.getDouble(0), 0.0);
	Assert.assertArrayEquals(new double[] { 3.0, 4.0 }, ((Vector) row2.get(1)).toArray(), 0.0);
}
开发者ID:apache，项目名称:systemml，代码行数:19，代码来源:MLContextTest.java


示例13: testOutputDataFrameDMLVectorNoIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testOutputDataFrameDMLVectorNoIDColumn() {
	System.out.println("MLContextTest - output DataFrame DML, vector no ID column");

	String s = "M = matrix('1 2 3 4', rows=2, cols=2);";
	Script script = dml(s).out("M");
	MLResults results = ml.execute(script);
	Dataset<Row> dataFrame = results.getDataFrameVectorNoIDColumn("M");
	List<Row> list = dataFrame.collectAsList();

	Row row1 = list.get(0);
	Assert.assertArrayEquals(new double[] { 1.0, 2.0 }, ((Vector) row1.get(0)).toArray(), 0.0);

	Row row2 = list.get(1);
	Assert.assertArrayEquals(new double[] { 3.0, 4.0 }, ((Vector) row2.get(0)).toArray(), 0.0);
}
开发者ID:apache，项目名称:systemml，代码行数:17，代码来源:MLContextTest.java


示例14: testOutputDataFramePYDMLVectorNoIDColumn

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testOutputDataFramePYDMLVectorNoIDColumn() {
	System.out.println("MLContextTest - output DataFrame PYDML, vector no ID column");

	String s = "M = full('1 2 3 4', rows=2, cols=2)";
	Script script = pydml(s).out("M");
	MLResults results = ml.execute(script);
	Dataset<Row> dataFrame = results.getDataFrameVectorNoIDColumn("M");
	List<Row> list = dataFrame.collectAsList();

	Row row1 = list.get(0);
	Assert.assertArrayEquals(new double[] { 1.0, 2.0 }, ((Vector) row1.get(0)).toArray(), 0.0);

	Row row2 = list.get(1);
	Assert.assertArrayEquals(new double[] { 3.0, 4.0 }, ((Vector) row2.get(0)).toArray(), 0.0);
}
开发者ID:apache，项目名称:systemml，代码行数:17，代码来源:MLContextTest.java


示例15: testDataFrameSumDMLVectorWithIDColumnNoFormatSpecified

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumDMLVectorWithIDColumnNoFormatSpecified() {
	System.out.println("MLContextTest - DataFrame sum DML, vector with ID column, no format specified");

	List<Tuple2<Double, Vector>> list = new ArrayList<Tuple2<Double, Vector>>();
	list.add(new Tuple2<Double, Vector>(1.0, Vectors.dense(1.0, 2.0, 3.0)));
	list.add(new Tuple2<Double, Vector>(2.0, Vectors.dense(4.0, 5.0, 6.0)));
	list.add(new Tuple2<Double, Vector>(3.0, Vectors.dense(7.0, 8.0, 9.0)));
	JavaRDD<Tuple2<Double, Vector>> javaRddTuple = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddTuple.map(new DoubleVectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField(RDDConverterUtils.DF_ID_COLUMN, DataTypes.DoubleType, true));
	fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	Script script = dml("print('sum: ' + sum(M));").in("M", dataFrame);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:22，代码来源:MLContextTest.java


示例16: testDataFrameSumPYDMLVectorWithIDColumnNoFormatSpecified

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumPYDMLVectorWithIDColumnNoFormatSpecified() {
	System.out.println("MLContextTest - DataFrame sum PYDML, vector with ID column, no format specified");

	List<Tuple2<Double, Vector>> list = new ArrayList<Tuple2<Double, Vector>>();
	list.add(new Tuple2<Double, Vector>(1.0, Vectors.dense(1.0, 2.0, 3.0)));
	list.add(new Tuple2<Double, Vector>(2.0, Vectors.dense(4.0, 5.0, 6.0)));
	list.add(new Tuple2<Double, Vector>(3.0, Vectors.dense(7.0, 8.0, 9.0)));
	JavaRDD<Tuple2<Double, Vector>> javaRddTuple = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddTuple.map(new DoubleVectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField(RDDConverterUtils.DF_ID_COLUMN, DataTypes.DoubleType, true));
	fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	Script script = dml("print('sum: ' + sum(M))").in("M", dataFrame);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:22，代码来源:MLContextTest.java


示例17: testDataFrameSumDMLVectorWithNoIDColumnNoFormatSpecified

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumDMLVectorWithNoIDColumnNoFormatSpecified() {
	System.out.println("MLContextTest - DataFrame sum DML, vector with no ID column, no format specified");

	List<Vector> list = new ArrayList<Vector>();
	list.add(Vectors.dense(1.0, 2.0, 3.0));
	list.add(Vectors.dense(4.0, 5.0, 6.0));
	list.add(Vectors.dense(7.0, 8.0, 9.0));
	JavaRDD<Vector> javaRddVector = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddVector.map(new VectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	Script script = dml("print('sum: ' + sum(M));").in("M", dataFrame);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:21，代码来源:MLContextTest.java


示例18: testDataFrameSumPYDMLVectorWithNoIDColumnNoFormatSpecified

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
@Test
public void testDataFrameSumPYDMLVectorWithNoIDColumnNoFormatSpecified() {
	System.out.println("MLContextTest - DataFrame sum PYDML, vector with no ID column, no format specified");

	List<Vector> list = new ArrayList<Vector>();
	list.add(Vectors.dense(1.0, 2.0, 3.0));
	list.add(Vectors.dense(4.0, 5.0, 6.0));
	list.add(Vectors.dense(7.0, 8.0, 9.0));
	JavaRDD<Vector> javaRddVector = sc.parallelize(list);

	JavaRDD<Row> javaRddRow = javaRddVector.map(new VectorRow());
	List<StructField> fields = new ArrayList<StructField>();
	fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
	StructType schema = DataTypes.createStructType(fields);
	Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);

	Script script = dml("print('sum: ' + sum(M))").in("M", dataFrame);
	setExpectedStdOut("sum: 45.0");
	ml.execute(script);
}
开发者ID:apache，项目名称:systemml，代码行数:21，代码来源:MLContextTest.java


示例19: toList

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
static
public List<Double> toList(Vector vector){
	DenseVector denseVector = vector.toDense();

	double[] values = denseVector.values();

	return Doubles.asList(values);
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:9，代码来源:VectorUtil.java


示例20: assertCorrectness

import org.apache.spark.ml.linalg.Vector; //导入依赖的package包/类
private void assertCorrectness(List<Row> sparkOutput, double[][] expected, Transformer transformer) {
    for (int i = 0; i < 3; i++) {
        double[] input = ((Vector) sparkOutput.get(i).get(0)).toArray();

        Map<String, Object> data = new HashMap<String, Object>();
        data.put("features", input);
        transformer.transform(data);
        double[] transformedOp = (double[]) data.get("scaled");

        double[] sparkOp = ((Vector) sparkOutput.get(i).get(1)).toArray();
        assertArrayEquals(transformedOp, sparkOp, 0.01);
        assertArrayEquals(transformedOp, expected[i], 0.01);
    }
}
开发者ID:flipkart-incubator，项目名称:spark-transformers，代码行数:15，代码来源:MinMaxScalerBridgeTest.java



注：本文中的org.apache.spark.ml.linalg.Vector类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

鲜花
握手
雷人
路过
鸡蛋
该文章已有0人参与评论

请发表评论

全部评论

专题导读
More+
上一篇：
Java Align类代码示例发布时间：2022-05-23
下一篇：
Java ComboContentAdapter类代码示例发布时间：2022-05-23
热门推荐
More+
热门话题
More+
阅读排行榜

扫描微信二维码

查看手机版网站

随时了解更新最新资讯

139-2527-9053

在线客服（服务时间 9:00～18:00）

在线QQ客服
地址：深圳市南山区西丽大学城创智工业园
电邮：jeky_zhao#qq.com
移动电话：139-2527-9053

Powered by 互联科技 X3.4© 2001-2213 极客世界.|Sitemap