spark mllib als 参数

在一定范围内按照排列组合方式对rank，iterations，lambda进行交叉评估（根据均方根误差），
找到最小误差的组合，用于建立矩阵分解模型。

Signature:
ALS.train(
    ratings,
    rank,
    iterations=5,
    lambda_=0.01,
    blocks=-1,
    nonnegative=False,
    seed=None,
)
Docstring:
Train a matrix factorization model given an RDD of ratings by users
for a subset of products. The ratings matrix is approximated as the
product of two lower-rank matrices of a given rank (number of
features). To solve for these features, ALS is run iteratively with
a configurable level of parallelism.

:param ratings:
  RDD of `Rating` or (userID, productID, rating) tuple.
:param rank:               #矩阵分解秩
  Number of features to use (also referred to as the number of latent factors).
:param iterations:    #迭代次数
  Number of iterations of ALS.
  (default: 5)
:param lambda_:  #正则系数
  Regularization parameter.
  (default: 0.01)
:param blocks:
  Number of blocks used to parallelize the computation. A value
  of -1 will use an auto-configured number of blocks.
  (default: -1)
:param nonnegative:
  A value of True will solve least-squares with nonnegativity
  constraints.
  (default: False)
:param seed:
  Random seed for initial matrix factorization model. A value
  of None will use system time as the seed.
  (default: None)

.. versionadded:: 0.9.0
File:      f:\anaconda\lib\site-packages\pyspark\mllib\recommendation.py
Type:      method