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https://issues.apache.org/jira/browse/MAHOUT-237?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12831396#action_12831396
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Jake Mannix commented on MAHOUT-237:
------------------------------------
{code}
RandomAccessSparseVector vector =
new RandomAccessSparseVector(key.toString(), Integer.MAX_VALUE,
valueString.length() / 5); // guess at initial size
{code}
This whole Integer.MAX_VALUE thing is killing me whenever I try to move back
and forth between sparse and dense vectors (which is necessary for performance
in the DistributedLanczos I'm working on). Ugh.
We really need to have a vector flag which says "I'm infinite dimensional, I
just return 0 whenever you ask me about dimensions I don't know about", so we
don't have to have this hack of Integer.MAX_VALUE as the dimension. I've
suggested it to people myself, but it's such a baaaaad hack.
> Map/Reduce Implementation of Document Vectorizer
> ------------------------------------------------
>
> Key: MAHOUT-237
> URL: https://issues.apache.org/jira/browse/MAHOUT-237
> Project: Mahout
> Issue Type: New Feature
> Affects Versions: 0.3
> Reporter: Robin Anil
> Assignee: Robin Anil
> Fix For: 0.3
>
> Attachments: DictionaryVectorizer.patch, DictionaryVectorizer.patch,
> DictionaryVectorizer.patch, DictionaryVectorizer.patch,
> DictionaryVectorizer.patch, MAHOUT-237-tfidf.patch, MAHOUT-237-tfidf.patch,
> SparseVector-VIntWritable.patch
>
>
> Current Vectorizer uses Lucene Index to convert documents into SparseVectors
> Ted is working on a Hash based Vectorizer which can map features into Vectors
> of fixed size and sum it up to get the document Vector
> This is a pure bag-of-words based Vectorizer written in Map/Reduce.
> The input document is in SequenceFile<Text,Text> . with key = docid, value =
> content
> First Map/Reduce over the document collection and generate the feature counts.
> Second Sequential pass reads the output of the map/reduce and converts them
> to SequenceFile<Text, LongWritable> where key=feature, value = unique id
> Second stage should create shards of features of a given split size
> Third Map/Reduce over the document collection, using each shard and create
> Partial(containing the features of the given shard) SparseVectors
> Fourth Map/Reduce over partial shard, group by docid, create full document
> Vector
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