Spark机器学习（四） Local matrix -- Data Types
Local matrix
A local matrix has integer-typed row and column indices and double-typed values, stored on a single machine. MLlib supports dense matrices, whose entry values are stored in a single double array in column-major order, and sparse matrices, whose non-zero entry values are stored in the Compressed Sparse Column (CSC) format in column-major order. For example, the following dense matrix
局部矩阵由整数型行和列的索引和浮点数类型的值组成，存储在一个单独节点上。MLlib支持密集矩阵，entry值被存储在一个一维浮点数数组，以列为排序主键。而稀疏矩阵，non-zero entry值，以Compressed Sparse Column (CSC) 格式存储，以列主键排序。例如，下面的密集矩阵
|1.0 2.0|
|3.0 4.0|
|5.0 6.0|
is stored in a one-dimensional array [1.0, 3.0, 5.0, 2.0, 4.0, 6.0] with the matrix size (3, 2).
被存储在一个一维数组 [1.0, 3.0, 5.0, 2.0, 4.0, 6.0]里，矩阵的size为(3,2)
Scala
The base class of local matrices is Matrix, and we provide two implementations: DenseMatrix, and SparseMatrix. We recommend using the factory methods implemented in Matrices to create local matrices. Remember, local matrices in MLlib are stored in column-major order.
局部矩阵的基类是Matrix，我们提供了两种实现：DenseMatrix, and SparseMatrix.
我们推荐使用Matrices 已经实现的工厂方法来创建局部矩阵。
记住，局部矩阵在MLlib中是以列排序存储的。
Refer to the Matrix Scala docs and Matrices Scala docs for details on the API.
更多信息请参见Matrix Scala docs and Matrices Scala docs API。
import org.apache.spark.mllib.linalg.{Matrix, Matrices}
// Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
val sm: Matrix = Matrices.sparse(3, 2, Array(0, 1, 3), Array(0, 2, 1), Array(9, 6, 8))