Trait

au.id.cxd.text.count

DocumentTermVectoriser

Related Doc: package count

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trait DocumentTermVectoriser extends AnyRef

Created by cd on 12/1/17.

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TfIdfCount
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Abstract Value Members

  1. abstract def count(data: Seq[Array[String]]): (Map[Int, (String, Int, Int)], DenseMatrix[Double])

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    Compute the count for the sequence of tokens found in each document.

    Compute the count for the sequence of tokens found in each document. Each record in the outer sequence is considered a document. Each inner sequence is considered the collection of tokens within the document.

    returns

    (termIndexMap, TF-IDF Matrix) (mutable.Map[Int, (String, Int)], DenseMatrix[Double]) The return is term index map that indicates which column each term is mapped to. The map key contains the index of the column and the value corresponds to the term and its hashcode (columnIndex x (Term x Hashcode)) The second item in the tuple is the TF-IDF matrix. Each row represents a document, each column contains the TF-IDF for the corresponding term within the document.

  2. abstract def countQuery(query: Array[String], lsi: LatentSemanticIndex): DenseVector[Double]

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    count the single query array and create a vector that assigns the tfidf term weights for the query into the indices of the term matrix that was constructed form the lsi model.

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