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))