smdc (version 0.0.2)

simDic: Document Similarity using Dictionary

Description

This function calculates the similarity between documents and documents by using dictionary.

Usage

simDic(docMatrix1, docMatrix2, scoreDict, breaks = seq(-1, 1, length = 11), norm = FALSE, method = "cosine", scoreFunc = mean)

Arguments

docMatrix1

Document matrix whose rows represent feature vector of one document. This matrix must satisfy the following: colnames(docMatrix1) denote feature names, rownames(docMatrix1) denote document names, every element is numerical.

docMatrix2

Document matrix whose rows represent feature vector of one document. This matrix must satisfy the following: colnames(docMatrix2) denote feature names, rownames(docMatrix2) denote document names, every element is numerical.

scoreDict

Dictionary matrix which converts features to numbers. This matrix must k * 2 matrix: 1st colmn represents features and 2nd column represents corresponding number. Similarity is calculated according to the number.

breaks

Range vector of frequency distribution. Each element must be ascending order.

norm

Whether normalize similarity matrix or not.

method

Method to caluculate similarity.

scoreFunc

Function of scoring from dictionary.

Value

Similarity Matrix whose rows represent documents of docMatrix1 and whose columns represent documents of docMatrix2. This matrix is n * m matrix where n=ncol(docMatrix1) and m=ncol(docMatrix2), and satisfy the following: rownames(returnValue)=colnames(docMatrix1), colnames(returnValue)=colnames(docMatrix2).

Examples

## The function is currently defined as
function (docMatrix1, docMatrix2, scoreDict, breaks = seq(-1, 
    1, length = 11), norm = FALSE, method = "cosine", scoreFunc = mean) 
{
    library("proxy")
    words <- unique(rbind(matrix(rownames(docMatrix1)), matrix(rownames(docMatrix2))))
    words <- words[order(words)]
    wordScores <- rep(NA, length(words))
    for (i in 1:length(words)) {
        cond <- (scoreDict[, 1] == words[i])
        value <- scoreDict[cond, 2]
        if (length(value) != 0) {
            wordScores[i] <- scoreFunc(value, na.rm = TRUE)
        }
    }
    names(breaks) <- cut(breaks, breaks)
    wordClass <- cut(wordScores, breaks)
    names(wordClass) <- words
    docFreq1 <- conv2Freq(docMatrix1, wordClass, breaks)
    docFreq2 <- conv2Freq(docMatrix2, wordClass, breaks)
    colnames(docFreq1) <- paste("r_", colnames(docMatrix1), sep = "")
    colnames(docFreq2) <- paste("c_", colnames(docMatrix2), sep = "")
    sim <- as.matrix(simil(t(cbind(docFreq1, docFreq2)), method = method))[colnames(docFreq1), 
        colnames(docFreq2)]
    rownames(sim) <- colnames(docMatrix1)
    colnames(sim) <- colnames(docMatrix2)
    if (norm) {
        sim <- normalize(sim)
    }
    return(sim)
  }