diff options
Diffstat (limited to 'docs/mllib-data-types.md')
| -rw-r--r-- | docs/mllib-data-types.md | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md index fe6c1bf7bf..4f2a2f7104 100644 --- a/docs/mllib-data-types.md +++ b/docs/mllib-data-types.md @@ -78,13 +78,13 @@ MLlib recognizes the following types as dense vectors: and the following as sparse vectors: -* MLlib's [`SparseVector`](api/python/pyspark.mllib.linalg.SparseVector-class.html). +* MLlib's [`SparseVector`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.SparseVector). * SciPy's [`csc_matrix`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html#scipy.sparse.csc_matrix) with a single column We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented -in [`Vectors`](api/python/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors. +in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vector) to create sparse vectors. {% highlight python %} import numpy as np @@ -151,7 +151,7 @@ LabeledPoint neg = new LabeledPoint(1.0, Vectors.sparse(3, new int[] {0, 2}, new <div data-lang="python" markdown="1"> A labeled point is represented by -[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html). +[`LabeledPoint`](api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint). {% highlight python %} from pyspark.mllib.linalg import SparseVector @@ -211,7 +211,7 @@ JavaRDD<LabeledPoint> examples = </div> <div data-lang="python" markdown="1"> -[`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.util.MLUtils-class.html) reads training +[`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) reads training examples stored in LIBSVM format. {% highlight python %} |