Nothing

```
# Cosine Distance
# Function for computing a cosine similarity of a matrix of values,
# e.g. a table of word frequencies.
# The implementation inspired by the following post:
# http://stackoverflow.com/questions/2535234/find-cosine-similarity-in-r
#
# Argument: a matrix or data table containing at least 2 rows and 2 cols
dist.cosine = function(x){
# test if the input dataset is acceptable
if(is.matrix(x) == FALSE & is.data.frame(x) == FALSE) {
stop("cannot apply a distance measure: wrong data format!")
}
# then, test whether the number of rows and cols is >1
if(length(x[1,]) < 2 | length(x[,1]) < 2) {
stop("at least 2 cols and 2 rows are needed to compute a distance!")
}
# to get Centered Cosine dist (=Pearson Correlation Coeff.), one needs
# to normalize the feature vectors by subtracting the vector means
# x = t( t(x) - colMeans(x) )
# this computes cosine dissimilarity; to have similarity, 1- applies
y = 1 - as.dist( x %*% t(x) / (sqrt(rowSums(x^2) %*% t(rowSums(x^2)))) )
# alternative way of approaching it:
# crossprod(x, y) / sqrt(crossprod(x) * crossprod(y))
return(y)
}
```

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