**proxyC** computes proximity between rows or columns of large matrices efficiently in C++. It is optimized for large sparse matrices using the Armadillo and Intel TBB libraries. Among several built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast.

This code was originally written for **quanteda** to compute similarity/distance between documents or features in large corpora, but separated as a stand-alone package to make it available for broader data scientific purposes.

```
require(Matrix)
## Loading required package: Matrix
require(microbenchmark)
## Loading required package: microbenchmark
require(RcppParallel)
## Loading required package: RcppParallel
# Set number of threads
setThreadOptions(8)
# Make a matrix with 99% zeros
sm1k <- rsparsematrix(1000, 1000, 0.01) # 1,000 columns
sm10k <- rsparsematrix(1000, 10000, 0.01) # 10,000 columns
# Convert to dense format
dm1k <- as.matrix(sm1k)
dm10k <- as.matrix(sm10k)
```

With sparase matrices, **proxyC** is roughly 10 to 100 times faster than **proxy**.

```
bm1 <- microbenchmark(
"proxyC 1k" = proxyC::simil(sm1k, margin = 2, method = "cosine"),
"proxy 1k" = proxy::simil(dm1k, method = "cosine"),
"proxyC 10k" = proxyC::simil(sm10k, margin = 2, method = "cosine"),
"proxy 10k" = proxy::simil(dm10k, method = "cosine"),
times = 10
)
boxplot(bm1)
```

If `rank`

is used, **proxyC** becomes even faster as many similarity scores are discarded (rounded to zero).

```
bm2 <- microbenchmark(
"proxyC rank" = proxyC::simil(sm1k, margin = 2, method = "cosine", rank = 10),
"proxyC all" = proxyC::simil(sm1k, margin = 2, method = "cosine"),
times = 10
)
boxplot(bm2)
```

`min_simil`

also makes **proxyC** faster.