package ML.DataType;


import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.linalg.*;
import org.apache.spark.mllib.linalg.distributed.*;
import org.apache.spark.mllib.regression.LabeledPoint;
 
import org.apache.spark.mllib.util.MLUtils;

import java.util.Arrays;

/**
 * TODO
 *
 * @ClassName: DataType
 * @author: DingH
 * @since: 2019/4/3 10:06
 */
public class DataType {
    public static void main(String[] args) {

        SparkConf conf = new SparkConf().setMaster("local").setAppName("Datatype");
        JavaSparkContext javaSparkContext 
 
= new JavaSparkContext(conf);

        /**
         * @Title: vectors.dense方法生成向量，sparse生成稀疏向量。第一個3是向量的大小，第二個列表是不為0的下表，第三個是對應的value.
         */
        Vector dense = Vectors.dense(1.0, 0.0, 3.0);
        Vector sparse = Vectors.sparse(3, new int[]{0, 2}, new double[]{1.0, 3.0});

        /**
         * @Title: 對向量進行標記，1.0為正，0.0為負
         
 
*/
        LabeledPoint labeledPoint = new LabeledPoint(1.0, dense);
        LabeledPoint labeledPoint1 = new LabeledPoint(0.0, sparse);

        /**
         * @Title: libSVM文件： lable1  index1:value1  index2:value2
         */
        JavaRDD<LabeledPoint> labeledPointJavaRDD = MLUtils.loadLibSVMFile(javaSparkContext.sc(), "/data...").toJavaRDD();

        /**
         * @Title: matricex.dense生成矩陣。3*2的矩陣
         * [1.0 3.0
         * 5.0 2.0
         * 4.0 6.0]
         */
        Matrix dense1 = Matrices.dense(3, 2, new double[]{1.0, 3.0, 5.0, 2.0, 4.0, 6.0});

        /**
         * @Title: matricex.sparse生成稀疏矩陣。3*2的矩陣。第三個參數和第四個參數對應為不為0的元素。
         * [9 0
         * 0 6
         * 8 0]
         */
        Matrix sparse1 = Matrices.sparse(3, 2, new int[]{0, 1, 3}, new int[]{0, 2, 1}, new double[]{9, 6, 8});

        /**
         * @Title: Rowmatrix
         */
        JavaRDD<Vector> parallelize = javaSparkContext.parallelize(Arrays.asList(
                Vectors.dense(1, 2, 3),
                Vectors.dense(2, 3, 4),
                Vectors.dense(3, 4, 5)
        ));
        RowMatrix rowMatrix = new RowMatrix(parallelize.rdd());
        long l = rowMatrix.numRows();
        long l1 = rowMatrix.numCols();
        QRDecomposition<RowMatrix, Matrix> rowMatrixMatrixQRDecomposition = rowMatrix.tallSkinnyQR(true);

        /**
         * @Title: IndexedRowMatrix
         */
        JavaRDD<IndexedRow> parallelize1 = javaSparkContext.parallelize(Arrays.asList(
                new IndexedRow(1, dense),
                new IndexedRow(2, dense),
                new IndexedRow(3, dense)
        ));
        IndexedRowMatrix indexedRowMatrix = new IndexedRowMatrix(parallelize1.rdd());
        long l2 = indexedRowMatrix.numCols();
        long l3 = indexedRowMatrix.numRows();
        RowMatrix rowMatrix1 = indexedRowMatrix.toRowMatrix();

        /**
         * @Title: CoordinateMatrix
         */
        JavaRDD<MatrixEntry> parallelize2 = javaSparkContext.parallelize(Arrays.asList(
                new MatrixEntry(0, 1, 3),
                new MatrixEntry(1, 3, 1),
                new MatrixEntry(2, 1, 1)
        ));
        CoordinateMatrix coordinateMatrix = new CoordinateMatrix(parallelize2.rdd());
        long l4 = coordinateMatrix.numCols();
        long l5 = coordinateMatrix.numRows();
        IndexedRowMatrix indexedRowMatrix1 = coordinateMatrix.toIndexedRowMatrix();

        /**
         * @Title: BlocakMatrix
         */
        BlockMatrix cache = indexedRowMatrix.toBlockMatrix().cache();
        BlockMatrix cache1 = coordinateMatrix.toBlockMatrix().cache();
        cache.validate();
        BlockMatrix multiply = cache.transpose().multiply(cache);
    }
}

spark MLlib DataType ML中的數據類型