server/src/main/resources/eclairjs/mllib/linalg/distributed/RowMatrix.js
function(rows,nRows,nCols) {
        this.logger = Logger.getLogger("RowMatrix_js");
        var jvmObject;
        if (arguments[0] instanceof org.apache.spark.mllib.linalg.distributed.RowMatrix) {
            jvmObject = arguments[0];
        } else if (arguments.length === 3) {
            jvmObject = new org.apache.spark.mllib.linalg.distributed.RowMatrix(Utils.unwrapObject(rows).rdd(),nRows,nCols);
        } else {
            jvmObject = new org.apache.spark.mllib.linalg.distributed.RowMatrix(Utils.unwrapObject(rows).rdd());
        }


         DistributedMatrix.call(this, jvmObject);

    }
function(rows,nRows,nCols) {
        this.logger = Logger.getLogger("RowMatrix_js");
        var jvmObject;
        if (arguments[0] instanceof org.apache.spark.mllib.linalg.distributed.RowMatrix) {
            jvmObject = arguments[0];
        } else if (arguments.length === 3) {
            jvmObject = new org.apache.spark.mllib.linalg.distributed.RowMatrix(Utils.unwrapObject(rows).rdd(),nRows,nCols);
        } else {
            jvmObject = new org.apache.spark.mllib.linalg.distributed.RowMatrix(Utils.unwrapObject(rows).rdd());
        }


         DistributedMatrix.call(this, jvmObject);

    }
function ( rows , nRows , nCols ) { this . logger = Logger . getLogger ( "RowMatrix_js" ) ; var jvmObject ; if ( arguments [ 0 ] instanceof org . apache . spark . mllib . linalg . distributed . RowMatrix ) { jvmObject = arguments [ 0 ] ; } else if ( arguments . length === 3 ) { jvmObject = new org . apache . spark . mllib . linalg . distributed . RowMatrix ( Utils . unwrapObject ( rows ) . rdd ( ) , nRows , nCols ) ; } else { jvmObject = new org . apache . spark . mllib . linalg . distributed . RowMatrix ( Utils . unwrapObject ( rows ) . rdd ( ) ) ; } DistributedMatrix . call ( this , jvmObject ) ; }
Represents a row-oriented distributed Matrix with no meaningful row indices.
@memberof module:eclairjs/mllib/linalg/distributed
@classdesc
@param {module:eclairjs.RDD} rows stored as an RDD[Vector]
@param {number} [nRows] number of rows. A non-positive value means unknown, and then the number of rows will
be determined by the number of records in the RDD `rows`.
@param {number} [nCols] number of columns. A non-positive value means unknown, and then the number of
columns will be determined by the size of the first row.
@class
@extends module:eclairjs/mllib/linalg/distributed.DistributedMatrix
@example
var RowMatrix = require('eclairjs/mllib/linalg/distributed/RowMatrix');
var Vectors = require("'eclairjs/mllib/linalg/Vectors");;
var rowsList = [Vectors.dense([1.12, 2.05, 3.12]), Vectors.dense([5.56, 6.28, 8.94]), Vectors.dense([10.2, 8.0, 20.5])];
var rows = sc.parallelize(rowsList);
var mat = new RowMatrix(rows);
Represents a row - oriented distributed Matrix with no meaningful row indices .