Package 'proxyC'

Standard deviation of columns and rows of large matrices

Description

Produces the same result as apply(x, 1, sd) or apply(x, 2, sd) without coercing matrix to dense matrix. Values are not identical to sd because of the floating point precision issue in C++.

Usage

colSds(x)

rowSds(x)

Arguments

x	matrix or Matrix object

Examples

mt <- Matrix::rsparsematrix(100, 100, 0.01)
colSds(mt)
apply(mt, 2, sd) # the same

---

Count number of zeros in columns and rows of large matrices

Description

Produces the same result as applying sum(x == 0) to each row or column.

Usage

colZeros(x)

rowZeros(x)

Arguments

x	matrix or Matrix object

Examples

mt <- Matrix::rsparsematrix(100, 100, 0.01)
colZeros(mt)
apply(mt, 2, function(x) sum(x == 0)) # the same

---

Compute similarity/distance between rows or columns of large matrices

Description

Fast similarity/distance computation function for large sparse matrices. You can floor small similarity value to to save computation time and storage space by an arbitrary threshold (min_simil) or rank (rank). You can specify the number of threads for parallel computing via options(proxyC.threads).

Usage

simil(
  x,
  y = NULL,
  margin = 1,
  method = c("cosine", "correlation", "jaccard", "ejaccard", "fjaccard", "dice", "edice",
    "hamann", "faith", "simple matching"),
  min_simil = NULL,
  rank = NULL,
  drop0 = FALSE,
  diag = FALSE,
  use_nan = NULL,
  digits = 14
)

dist(
  x,
  y = NULL,
  margin = 1,
  method = c("euclidean", "chisquared", "kullback", "jeffreys", "jensen", "manhattan",
    "maximum", "canberra", "minkowski", "hamming"),
  p = 2,
  smooth = 0,
  drop0 = FALSE,
  diag = FALSE,
  use_nan = NULL,
  digits = 14
)

Arguments

x	matrix or Matrix object. Dense matrices are covered to the CsparseMatrix-class internally.
y	if a matrix or Matrix object is provided, proximity between documents or features in x and y is computed.
margin	integer indicating margin of similarity/distance computation. 1 indicates rows or 2 indicates columns.
method	method to compute similarity or distance
min_simil	the minimum similarity value to be recorded.
rank	an integer value specifying top-n most similarity values to be recorded.
drop0	if TRUE, zero values are removed regardless of min_simil or rank.
diag	if TRUE, only compute diagonal elements of the similarity/distance matrix; useful when comparing corresponding rows or columns of x and y.
use_nan	if TRUE, return NaN if the standard deviation of a vector is zero when method is "correlation"; if all the values are zero in a vector when method is "cosine", "chisquared", "kullback", "jeffreys" or "jensen". Note that use of NaN makes the similarity/distance matrix denser and therefore larger in RAM. If FALSE, return zero in same use situations as above. If NULL, will also return zero but also generate a warning (default).
digits	determines rounding of small values towards zero. Use primarily to correct rounding errors in C++. See zapsmall.
p	weight for Minkowski distance
smooth	adds a fixed value to all the cells to avoid division by zero. Only used when method is "chisquared", "kullback", "jeffreys" or "jensen".

Details

Available Methods

Similarity:

• cosine: cosine similarity

• correlation: Pearson's correlation

• jaccard: Jaccard coefficient

• ejaccard: the real value version of jaccard

• fjaccard: Fuzzy Jaccard coefficient

• dice: Dice coefficient

• edice: the real value version of dice

• hamann: Hamann similarity

• faith: Faith similarity

• ⁠simple matching⁠: the percentage of common elements

Distance:

• euclidean: Euclidean distance

• chisquared: chi-squared distance

• kullback: Kullback–Leibler divergence

• jeffreys: Jeffreys divergence

• jensen: Jensen–Shannon divergence

• manhattan: Manhattan distance

• maximum: the largest difference between values

• canberra: Canberra distance

• minkowski: Minkowski distance

• hamming: Hamming distance

See the vignette for how the similarity and distance are computed: vignette("measures", package = "proxyC")

Parallel Computing

It performs parallel computing using Intel oneAPI Threads Building Blocks. The number of threads for parallel computing should be specified via options(proxyC.threads) before calling the functions. If the value is -1, all the available threads will be used. Unless the option is used, the number of threads will be limited by the environmental variables (OMP_THREAD_LIMIT or RCPP_PARALLEL_NUM_THREADS) to comply with CRAN policy and offer backward compatibility.

See Also

zapsmall

Examples

mt <- Matrix::rsparsematrix(100, 100, 0.01)
simil(mt, method = "cosine")[1:5, 1:5]
mt <- Matrix::rsparsematrix(100, 100, 0.01)
dist(mt, method = "euclidean")[1:5, 1:5]

---