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
巴特西

spark mllib als 参数

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