R语言中如何基于自定义混合向量距离函数生成距离矩阵

测试数据集 #

实验使用的测试数据集定义代码：

data=structure(list(X126 = c("X266", "B7", "T133", "J34", "T218", 
"X249"), TVGUIDE = c("TVGUIDE", "MODMAT", "MASSEY", "KMART", 
"MASSEY", "ROSES"), YES = c("YES", "YES", "YES", "NO", "YES", 
"NO"), KEY = c("KEY", "KEY", "KEY", "KEY", "KEY", "KEY"), YES.1 = c("YES", 
"YES", "YES", "YES", "YES", "YES"), BENTON = c("BENTON", "BENTON", 
"BENTON", "BENTON", "BENTON", "BENTON"), GALLATIN = c("GALLATIN", 
"GALLATIN", "GALLATIN", "GALLATIN", "GALLATIN", "GALLATIN"), 
    UNCOATED = c("UNCOATED", "UNCOATED", "UNCOATED", "UNCOATED", 
    "UNCOATED", "COATED"), UNCOATED.1 = c("UNCOATED", "COATED", 
    "UNCOATED", "COATED", "UNCOATED", "COATED"), NO = c("NO", 
    "NO", "NO", "NO", "NO", "NO"), LINE = c("LINE", "LINE", "LINE", 
    "LINE", "LINE", "LINE"), YES.2 = c("YES", "YES", "YES", "YES", 
    "YES", "YES"), Motter94 = c("Motter94", "WoodHoe70", "WoodHoe70", 
    "WoodHoe70", "WoodHoe70", "Motter94"), TABLOID = c("TABLOID", 
    "CATALOG", "CATALOG", "TABLOID", "CATALOG", "TABLOID"), NorthUS = c("NorthUS", 
    "NorthUS", "NorthUS", NA, "NorthUS", "CANADIAN"), band = c("noband", 
    "noband", "noband", "noband", "noband", "noband"), X25503 = c(25503L, 
    47201L, 39039L, 37351L, 38039L, 35751L), X821 = c(821L, 815L, 
    816L, 816L, 816L, 827L), X2 = c(2L, 9L, 9L, 2L, 2L, 2L), 
    X1911 = c(NA, NA, 1910L, 1910L, 1910L, 1911L), X46 = c(46L, 
    40L, 40L, 46L, 40L, 46L), X78 = c(80L, 80L, 75L, 80L, 76L, 
    75L), X20 = c(20L, 30L, 30L, 30L, 28L, 30L), X1700 = c(1900L, 
    1850L, 1467L, 2100L, 1467L, 2600L), X40 = c(40L, 40L, 40L, 
    40L, 40L, 40L), X100 = c(100L, 100L, 100L, 100L, 100L, 100L
    ), X55 = c(55, 62, 52, 50, 50, 50), X0.2 = c(0.3, 0.433, 
    0.3, 0.3, 0.267, 0.3), X17 = c(15, 16, 16, 17, 16.8, 16.5
    ), X0.75 = c(0.75, NA, 0.3125, 0.75, 0.4375, 0.75), X13.1 = c(6.6, 
    6.5, 5.6, 0, 8.6, 0), X50.5 = c(54.9, 53.8, 55.6, 57.5, 53.8, 
    62.5), X36.4 = c(38.5, 39.8, 38.8, 42.5, 37.6, 37.5), X0 = c(0, 
    0, 0, 5, 5, 6), X0.1 = c(0, 0, 0, 0, 0, 0), X2.5 = c(2.5, 
    2.8, 2.5, 2.3, 2.5, 2.5), X1 = c(0.7, 0.9, 1.3, 0.6, 0.8, 
    0.6), X34 = c(34, 40, 40, 35, 40, 30), X105 = c(105, 103.87, 
    108.06, 106.67, 103.87, 106.67)), row.names = c(NA, 6L), class = "data.frame")

自定义距离计算函数 #

函数入参说明：

• data：传入的目标数据集对象
• x、y：参与距离计算的两行数据对应的行索引

函数定义代码：

mixed_similarity_distance<-function(data=data,x,y){
  length_charachter_part=length(which(sapply(data,class)=="character"))
  comparison<-c(data[x,1:length_charachter_part]==data[y,1:length_charachter_part])
  char_distance=length_charachter_part-table(comparison)["TRUE"]
  numerical_distance=dist(rbind(data[x,-c(1:length_charachter_part)],data[y,-c(1:length_charachter_part)]))
  total_distance=numerical_distance+char_distance 
  return(total_distance)    
}

函数调用示例 #

mixed_similarity_distance(data=data,1,1)  # 输出 0 
mixed_similarity_distance(data=data,2,2)  # 输出 0 
mixed_similarity_distance(data=data,3,1)  # 计算第1行与第3行的距离值

核心需求与当前尝试 #

• 核心需求：计算数据集所有行两两配对的完整距离矩阵
• 此前尝试的实现代码：

distance_matrix <- Vectorize(mixed_similarity_distance, c("x", "y"))
distance_matrix(1:nrow(data), 1:nrow(data), data)

内容的提问来源于stack exchange，提问作者Tou Mou