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Diffstat (limited to 'docs/mllib-guide.md')
| -rw-r--r-- | docs/mllib-guide.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 4236b0c8b6..0963a99881 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -36,15 +36,15 @@ The following links provide a detailed explanation of the methods and usage exam # Data Types Most MLlib algorithms operate on RDDs containing vectors. In Java and Scala, the -[Vector](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) class is used to +[Vector](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) class is used to represent vectors. You can create either dense or sparse vectors using the -[Vectors](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) factory. +[Vectors](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) factory. In Python, MLlib can take the following vector types: * [NumPy](http://www.numpy.org) arrays * Standard Python lists (e.g. `[1, 2, 3]`) -* The MLlib [SparseVector](api/pyspark/pyspark.mllib.linalg.SparseVector-class.html) class +* The MLlib [SparseVector](api/python/pyspark.mllib.linalg.SparseVector-class.html) class * [SciPy sparse matrices](http://docs.scipy.org/doc/scipy/reference/sparse.html) For efficiency, we recommend using NumPy arrays over lists, and using the @@ -52,8 +52,8 @@ For efficiency, we recommend using NumPy arrays over lists, and using the for SciPy matrices, or MLlib's own SparseVector class. Several other simple data types are used throughout the library, e.g. the LabeledPoint -class ([Java/Scala](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint), -[Python](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html)) for labeled data. +class ([Java/Scala](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint), +[Python](api/python/pyspark.mllib.regression.LabeledPoint-class.html)) for labeled data. # Dependencies MLlib uses the [jblas](https://github.com/mikiobraun/jblas) linear algebra library, which itself |