Well the 3 in this case is the size of the sparse vector. This equates to the number of features, which for CountVectorizer (I assume that's what you're using) is also vocab size (number of unique terms).
On Tue, 25 Apr 2017 at 04:06 Peyman Mohajerian <mohaj...@gmail.com> wrote: > setVocabSize > > > On Mon, Apr 24, 2017 at 5:36 PM, Zeming Yu <zemin...@gmail.com> wrote: > >> Hi all, >> >> Beginner question: >> >> what does the 3 mean in the (3,[0,1,2],[1.0,1.0,1.0])? >> >> https://spark.apache.org/docs/2.1.0/ml-features.html >> >> id | texts | vector >> ----|---------------------------------|--------------- >> 0 | Array("a", "b", "c") | (3,[0,1,2],[1.0,1.0,1.0]) >> 1 | Array("a", "b", "b", "c", "a") | (3,[0,1,2],[2.0,2.0,1.0]) >> >> >
