通過R中的餘弦相似度檢索矩陣中每行的前k個相似行R

Question

How to efficiently retrieve top K-similar vectors by cosine similarity using R?詢問如何計算一個矩陣的每個向量相對於另一個矩陣的頂部相似向量。這是satisfactorily answered，我想調整它在單個矩陣上運行。

也就是說，我想在矩陣中的每一行頂部K類似其他行。我懷疑解決方案非常相似，但可以進行優化。

Answer 1

該功能是基於鏈接的答案：

CosineSimilarities <- function(m, top.k) { 
    # Computes cosine similarity between each row and all other rows in a matrix. 
    # 
    # Args: 
    # m: Matrix of values. 
    # top.k: Number of top rows to show for each row. 
    # 
    # Returns: 
    # Data frame with columns for pair of rows, and cosine similarity, for top 
    # `top.k` rows per row. 
    # 
    # Similarity computation 
    cp <- tcrossprod(m) 
    mm <- rowSums(m^2) 
    result <- cp/sqrt(outer(mm, mm)) 
    # Top similar rows from train (per row) 
    # Use `top.k + 1` to remove the self-reference (similarity = 1) 
    top <- apply(result, 2, order, decreasing=TRUE)[seq(top.k + 1), ] 
    result.df <- data.frame(row.id1=c(col(top)), row.id2=c(top)) 
    result.df$cosine.similarity <- result[as.matrix(result.df[, 2:1])] 
    # Remove same-row records and return 
    return(result.df[result.df$row.id1 != result.df$row.id2, ]) 
} 

例如：

(m <- matrix(1:9, nrow=3)) 
#  [,1] [,2] [,3] 
# [1,] 1 4 7 
# [2,] 2 5 8 
# [3,] 3 6 9 
CosineSimilarities(m, 1) 
# row.id1 row.id2 cosine.similarity 
# 2  1  2   0.9956 
# 4  2  3   0.9977 
# 6  3  2   0.9977