dist.simple: Cosine Distance

Description

Function for computing Eder's Simple distance of a matrix of values, e.g. a table of word frequencies. This is done by normalizing the input dataset by a square root function, and then applying Manhattan distance.

Usage

dist.simple(x)

Value

The function returns an object of the class dist, containing distances between each pair of samples. To convert it to a square matrix instead, use the generic function as.dist.

Arguments

x: a matrix or data table containing at least 2 rows and 2 cols, the samples (texts) to be compared in rows, the variables in columns.

Author

Maciej Eder

Examples

Run this code

# first, preparing a table of word frequencies
        Iuvenalis_1 = c(3.939, 0.635, 1.143, 0.762, 0.423)
        Iuvenalis_2 = c(3.733, 0.822, 1.066, 0.933, 0.511)
        Tibullus_1  = c(2.835, 1.302, 0.804, 0.862, 0.881)
        Tibullus_2  = c(2.911, 0.436, 0.400, 0.946, 0.618)
        Tibullus_3  = c(1.893, 1.082, 0.991, 0.879, 1.487)
        dataset = rbind(Iuvenalis_1, Iuvenalis_2, Tibullus_1, Tibullus_2, 
                        Tibullus_3)
        colnames(dataset) = c("et", "non", "in", "est", "nec")

# the table of frequencies looks as follows
        print(dataset)
        
# then, applying a distance, in two flavors
        dist.simple(dataset)
        as.matrix(dist.simple(dataset))

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning