[ https://issues.apache.org/jira/browse/MAHOUT-237?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Robin Anil updated MAHOUT-237: ------------------------------ Attachment: DictionaryVectorizer.patch Uses String Reader. Removes unused imports and added License headers and unused variables. Still bzip decompressing wikipedia dump. Wish there was a mapreduce for that > 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, 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 -- This message is automatically generated by JIRA. - You can reply to this email to add a comment to the issue online.