Java Vectors类代码示例

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本文整理汇总了Java中org.apache.spark.ml.linalg.Vectors类的典型用法代码示例。如果您正苦于以下问题：Java Vectors类的具体用法？Java Vectors怎么用？Java Vectors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。

Vectors类属于org.apache.spark.ml.linalg包，在下文中一共展示了Vectors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: fieldCall

import org.apache.spark.ml.linalg.Vectors; //导入依赖的package包/类
/**
 * String[] -> Obj
 *
 * @param value
 * @return
 * @throws Exception
 */
public Object fieldCall(FieldInfo info, String[] value) throws Exception {
    switch (info.getDataType()) {
        case FieldInfo.STRING_DATATYPE: {
            return value;
        }
        case FieldInfo.DOUBLE_DATATYPE:
        case FieldInfo.INTEGER_DATATYPE:
        case FieldInfo.LONG_DATATYPE: {
            double[] vect = new double[value.length];
            try {
                for (int i = 0; i < value.length; i++) {
                    vect[i] = Double.valueOf(value[i]);
                }
            } catch (Exception e) {
                throw new CantConverException(e.getMessage());
            }
            return Vectors.dense(vect);
        }
        default:
            throw new CantConverException("不合法类型");
    }
}

开发者ID:hays2hong，项目名称:stonk，代码行数:30，代码来源:LineParse.java

示例2: testDataFrameSumDMLVectorWithIDColumn

import org.apache.spark.ml.linalg.Vectors; //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3: testDataFrameSumPYDMLVectorWithIDColumn

import org.apache.spark.ml.linalg.Vectors; //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4: testDataFrameSumDMLMllibVectorWithIDColumn

import org.apache.spark.ml.linalg.Vectors; //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5: testDataFrameSumPYDMLMllibVectorWithIDColumn

import org.apache.spark.ml.linalg.Vectors; //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6: testDataFrameSumDMLVectorWithNoIDColumn

import org.apache.spark.ml.linalg.Vectors; //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7: testDataFrameSumPYDMLVectorWithNoIDColumn

import org.apache.spark.ml.linalg.Vectors; //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8: testDataFrameSumDMLMllibVectorWithNoIDColumn

import org.apache.spark.ml.linalg.Vectors; //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9: testDataFrameSumPYDMLMllibVectorWithNoIDColumn

import org.apache.spark.ml.linalg.Vectors; //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10: testDataFrameSumDMLVectorWithIDColumnNoFormatSpecified

import org.apache.spark.ml.linalg.Vectors; //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11: testDataFrameSumPYDMLVectorWithIDColumnNoFormatSpecified

import org.apache.spark.ml.linalg.Vectors; //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12: testDataFrameSumDMLVectorWithNoIDColumnNoFormatSpecified

import org.apache.spark.ml.linalg.Vectors; //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13: testDataFrameSumPYDMLVectorWithNoIDColumnNoFormatSpecified

import org.apache.spark.ml.linalg.Vectors; //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14: testMinMaxScaler

import org.apache.spark.ml.linalg.Vectors; //导入依赖的package包/类
@Test
public void testMinMaxScaler() {
    //prepare data
    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
            RowFactory.create(1.0, Vectors.dense(data[0])),
            RowFactory.create(2.0, Vectors.dense(data[1])),
            RowFactory.create(3.0, Vectors.dense(data[2])),
            RowFactory.create(4.0, Vectors.dense(data[3]))
    ));

    StructType schema = new StructType(new StructField[]{
            new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
            new StructField("features", new VectorUDT(), false, Metadata.empty())
    });

    Dataset<Row> df = spark.createDataFrame(jrdd, schema);

    //train model in spark
    MinMaxScalerModel sparkModel = new MinMaxScaler()
            .setInputCol("features")
            .setOutputCol("scaled")
            .setMin(-5)
            .setMax(5)
            .fit(df);


    //Export model, import it back and get transformer
    byte[] exportedModel = ModelExporter.export(sparkModel);
    final Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    //compare predictions
    List<Row> sparkOutput = sparkModel.transform(df).orderBy("label").select("features", "scaled").collectAsList();
    assertCorrectness(sparkOutput, expected, transformer);
}

开发者ID:flipkart-incubator，项目名称:spark-transformers，代码行数:35，代码来源:MinMaxScalerBridgeTest.java

示例15: testStandardScaler

import org.apache.spark.ml.linalg.Vectors; //导入依赖的package包/类
@Test
public void testStandardScaler() {


    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
            RowFactory.create(1.0, Vectors.dense(data[0])),
            RowFactory.create(2.0, Vectors.dense(data[1])),
            RowFactory.create(3.0, Vectors.dense(data[2]))
    ));

    StructType schema = new StructType(new StructField[]{
            new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
            new StructField("features", new VectorUDT(), false, Metadata.empty())
    });

    Dataset<Row> df = spark.createDataFrame(jrdd, schema);

    //train model in spark
    StandardScalerModel sparkModelNone = new StandardScaler()
            .setInputCol("features")
            .setOutputCol("scaledOutput")
            .setWithMean(false)
            .setWithStd(false)
            .fit(df);

    StandardScalerModel sparkModelWithMean = new StandardScaler()
            .setInputCol("features")
            .setOutputCol("scaledOutput")
            .setWithMean(true)
            .setWithStd(false)
            .fit(df);

    StandardScalerModel sparkModelWithStd = new StandardScaler()
            .setInputCol("features")
            .setOutputCol("scaledOutput")
            .setWithMean(false)
            .setWithStd(true)
            .fit(df);

    StandardScalerModel sparkModelWithBoth = new StandardScaler()
            .setInputCol("features")
            .setOutputCol("scaledOutput")
            .setWithMean(true)
            .setWithStd(true)
            .fit(df);


    //Export model, import it back and get transformer
    byte[] exportedModel = ModelExporter.export(sparkModelNone);
    final Transformer transformerNone = ModelImporter.importAndGetTransformer(exportedModel);

    exportedModel = ModelExporter.export(sparkModelWithMean);
    final Transformer transformerWithMean = ModelImporter.importAndGetTransformer(exportedModel);

    exportedModel = ModelExporter.export(sparkModelWithStd);
    final Transformer transformerWithStd = ModelImporter.importAndGetTransformer(exportedModel);

    exportedModel = ModelExporter.export(sparkModelWithBoth);
    final Transformer transformerWithBoth = ModelImporter.importAndGetTransformer(exportedModel);


    //compare predictions
    List<Row> sparkNoneOutput = sparkModelNone.transform(df).orderBy("label").select("features", "scaledOutput").collectAsList();
    assertCorrectness(sparkNoneOutput, data, transformerNone);

    List<Row> sparkWithMeanOutput = sparkModelWithMean.transform(df).orderBy("label").select("features", "scaledOutput").collectAsList();
    assertCorrectness(sparkWithMeanOutput, resWithMean, transformerWithMean);

    List<Row> sparkWithStdOutput = sparkModelWithStd.transform(df).orderBy("label").select("features", "scaledOutput").collectAsList();
    assertCorrectness(sparkWithStdOutput, resWithStd, transformerWithStd);

    List<Row> sparkWithBothOutput = sparkModelWithBoth.transform(df).orderBy("label").select("features", "scaledOutput").collectAsList();
    assertCorrectness(sparkWithBothOutput, resWithBoth, transformerWithBoth);

}

开发者ID:flipkart-incubator，项目名称:spark-transformers，代码行数:76，代码来源:StandardScalerBridgeTest.java

示例16: createVector

import org.apache.spark.ml.linalg.Vectors; //导入依赖的package包/类
private static Vector createVector(MatrixBlock row) {
	if( row.isEmptyBlock(false) ) //EMPTY SPARSE ROW
		return Vectors.sparse(row.getNumColumns(), new int[0], new double[0]);
	else if( row.isInSparseFormat() ) //SPARSE ROW
		return Vectors.sparse(row.getNumColumns(), 
				row.getSparseBlock().indexes(0), row.getSparseBlock().values(0));
	else // DENSE ROW
		return Vectors.dense(row.getDenseBlockValues());
}

开发者ID:apache，项目名称:systemml，代码行数:10，代码来源:RDDConverterUtils.java

示例17: call

import org.apache.spark.ml.linalg.Vectors; //导入依赖的package包/类
@Override
public Vector call(BigDecimal t1) throws Exception {
  double d = t1.doubleValue();
  return Vectors.dense(d);
}

开发者ID:jgperrin，项目名称:net.jgp.labs.informix2spark，代码行数:6，代码来源:VectorBuilderBigDecimal.java

示例18: call

import org.apache.spark.ml.linalg.Vectors; //导入依赖的package包/类
@Override
public Vector call(Integer t1) throws Exception {
  double d = t1.doubleValue();
  return Vectors.dense(d);
}

开发者ID:jgperrin，项目名称:net.jgp.labs.informix2spark，代码行数:6，代码来源:VectorBuilderInteger.java

示例19: main

import org.apache.spark.ml.linalg.Vectors; //导入依赖的package包/类
public static void main(String[] args) {
   SparkSession spark = SparkSession
     .builder().master("local").config("spark.sql.warehouse.dir", "file:///C:/Users/sumit.kumar/Downloads/bin/warehouse")
     .appName("JavaEstimatorTransformerParamExample")
     .getOrCreate();
   Logger rootLogger = LogManager.getRootLogger();
rootLogger.setLevel(Level.WARN);
   // $example on$
   // Prepare training data.
   List<Row> dataTraining = Arrays.asList(
       RowFactory.create(1.0, Vectors.dense(0.0, 1.1, 0.1)),
       RowFactory.create(0.0, Vectors.dense(2.0, 1.0, -1.0)),
       RowFactory.create(0.0, Vectors.dense(2.0, 1.3, 1.0)),
       RowFactory.create(1.0, Vectors.dense(0.0, 1.2, -0.5))
   );
   StructType schema = new StructType(new StructField[]{
       new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
       new StructField("features", new VectorUDT(), false, Metadata.empty())
   });
   Dataset<Row> training = spark.createDataFrame(dataTraining, schema);

   // Create a LogisticRegression instance. This instance is an Estimator.
   LogisticRegression lr = new LogisticRegression();
   // Print out the parameters, documentation, and any default values.
   System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n");

   // We may set parameters using setter methods.
   lr.setMaxIter(10).setRegParam(0.01);

   // Learn a LogisticRegression model. This uses the parameters stored in lr.
   LogisticRegressionModel model1 = lr.fit(training);
   // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
   // we can view the parameters it used during fit().
   // This prints the parameter (name: value) pairs, where names are unique IDs for this
   // LogisticRegression instance.
   System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());

   // We may alternatively specify parameters using a ParamMap.
   ParamMap paramMap = new ParamMap()
     .put(lr.maxIter().w(20))  // Specify 1 Param.
     .put(lr.maxIter(), 30)  // This overwrites the original maxIter.
     .put(lr.regParam().w(0.1), lr.threshold().w(0.55));  // Specify multiple Params.

   // One can also combine ParamMaps.
   ParamMap paramMap2 = new ParamMap()
     .put(lr.probabilityCol().w("myProbability"));  // Change output column name
   ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);

   // Now learn a new model using the paramMapCombined parameters.
   // paramMapCombined overrides all parameters set earlier via lr.set* methods.
   LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
   System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());

   // Prepare test documents.
   List<Row> dataTest = Arrays.asList(
       RowFactory.create(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
       RowFactory.create(0.0, Vectors.dense(3.0, 2.0, -0.1)),
       RowFactory.create(1.0, Vectors.dense(0.0, 2.2, -1.5))
   );
   Dataset<Row> test = spark.createDataFrame(dataTest, schema);

   // Make predictions on test documents using the Transformer.transform() method.
   // LogisticRegression.transform will only use the 'features' column.
   // Note that model2.transform() outputs a 'myProbability' column instead of the usual
   // 'probability' column since we renamed the lr.probabilityCol parameter previously.
   Dataset<Row> results = model2.transform(test);
   Dataset<Row> rows = results.select("features", "label", "myProbability", "prediction");
   for (Row r: rows.collectAsList()) {
     System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
       + ", prediction=" + r.get(3));
   }
   // $example off$

   spark.stop();
 }

开发者ID:PacktPublishing，项目名称:Apache-Spark-2x-for-Java-Developers，代码行数:76，代码来源:JavaEstimatorTransformerParamExample.java

示例20: call

import org.apache.spark.ml.linalg.Vectors; //导入依赖的package包/类
@Override
public Vector call(Double t1) throws Exception {
	return Vectors.dense(t1);
}

开发者ID:jgperrin，项目名称:net.jgp.labs.spark，代码行数:5，代码来源:VectorBuilder.java

注：本文中的org.apache.spark.ml.linalg.Vectors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。

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Java Vectors类代码示例

示例1: fieldCall

示例2: testDataFrameSumDMLVectorWithIDColumn

示例3: testDataFrameSumPYDMLVectorWithIDColumn

示例4: testDataFrameSumDMLMllibVectorWithIDColumn

示例5: testDataFrameSumPYDMLMllibVectorWithIDColumn

示例6: testDataFrameSumDMLVectorWithNoIDColumn

示例7: testDataFrameSumPYDMLVectorWithNoIDColumn

示例8: testDataFrameSumDMLMllibVectorWithNoIDColumn

示例9: testDataFrameSumPYDMLMllibVectorWithNoIDColumn

示例10: testDataFrameSumDMLVectorWithIDColumnNoFormatSpecified

示例11: testDataFrameSumPYDMLVectorWithIDColumnNoFormatSpecified

示例12: testDataFrameSumDMLVectorWithNoIDColumnNoFormatSpecified

示例13: testDataFrameSumPYDMLVectorWithNoIDColumnNoFormatSpecified

示例14: testMinMaxScaler

示例15: testStandardScaler

示例16: createVector

示例17: call

示例18: call

示例19: main

示例20: call

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