Github user dusenberrymw commented on a diff in the pull request:
https://github.com/apache/spark/pull/6958#discussion_r33078634
--- Diff: docs/mllib-data-types.md ---
@@ -226,7 +226,8 @@ examples = MLUtils.loadLibSVMFile(sc,
"data/mllib/sample_libsvm_data.txt")
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. For example, the following matrix `\[ \begin{pmatrix}
+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 `\[ \begin{pmatrix}
--- End diff --
Well I think documenting that we are using CSC representation gives the
user some reasoning for how the `colPtrs`, `rowIndices`, and `values`
parameters interact, and how to use them properly. Now whether we should
*require* that only those parameters be used, as opposed to `values` & `(row,
col)` tuples, for example, is another question, and may be worth exploring.
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