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Find exact nearest neighbors by brute force

Source: R/rnndescent.R
brute_force_knn.Rd

Returns the exact nearest neighbors of a dataset. A brute force search is carried out: all possible pairs of points are compared, and the nearest neighbors are returned.

Usage

brute_force_knn(
  data,
  k,
  metric = "euclidean",
  use_alt_metric = TRUE,
  n_threads = 0,
  verbose = FALSE,
  obs = "R"
)

Arguments

data

Matrix of n items to generate neighbors for, with observations in the rows and features in the columns. Optionally, input can be passed with observations in the columns, by setting obs = "C", which should be more efficient. Possible formats are base::data.frame(), base::matrix() or Matrix::sparseMatrix(). Sparse matrices should be in dgCMatrix format. Dataframes will be converted to numerical matrix format internally, so if your data columns are logical and intended to be used with the specialized binary metrics, you should convert it to a logical matrix first (otherwise you will get the slower dense numerical version).

k

Number of nearest neighbors to return.

metric

Type of distance calculation to use. One of:

  • "braycurtis"

  • "canberra"

  • "chebyshev"

  • "correlation" (1 minus the Pearson correlation)

  • "cosine"

  • "dice"

  • "euclidean"

  • "hamming"

  • "hellinger"

  • "jaccard"

  • "jensenshannon"

  • "kulsinski"

  • "sqeuclidean" (squared Euclidean)

  • "manhattan"

  • "rogerstanimoto"

  • "russellrao"

  • "sokalmichener"

  • "sokalsneath"

  • "spearmanr" (1 minus the Spearman rank correlation)

  • "symmetrickl" (symmetric Kullback-Leibler divergence)

  • "tsss" (Triangle Area Similarity-Sector Area Similarity or TS-SS metric)

  • "yule"

For non-sparse data, the following variants are available with preprocessing: this trades memory for a potential speed up during the distance calculation. Some minor numerical differences should be expected compared to the non-preprocessed versions:

  • "cosine-preprocess": cosine with preprocessing.

  • "correlation-preprocess": correlation with preprocessing.

For non-sparse binary data passed as a logical matrix, the following metrics have specialized variants which should be substantially faster than the non-binary variants (in other cases the logical data will be treated as a dense numeric vector of 0s and 1s):

  • "dice"

  • "hamming"

  • "jaccard"

  • "kulsinski"

  • "matching"

  • "rogerstanimoto"

  • "russellrao"

  • "sokalmichener"

  • "sokalsneath"

  • "yule"

use_alt_metric

If TRUE, use faster metrics that maintain the ordering of distances internally (e.g. squared Euclidean distances if using metric = "euclidean"), then apply a correction at the end. Probably the only reason to set this to FALSE is if you suspect that some sort of numeric issue is occurring with your data in the alternative code path.

n_threads

Number of threads to use.

verbose

If TRUE, log information to the console.

obs

set to "C" to indicate that the input data orientation stores each observation as a column. The default "R" means that observations are stored in each row. Storing the data by row is usually more convenient, but internally your data will be converted to column storage. Passing it already column-oriented will save some memory and (a small amount of) CPU usage.

Value

the nearest neighbor graph as a list containing:

  • idx an n by k matrix containing the nearest neighbor indices.

  • dist an n by k matrix containing the nearest neighbor distances.

Details

This method is accurate but scales poorly with dataset size, so use with caution with larger datasets. Having the exact neighbors as a ground truth to compare with approximate results is useful for benchmarking and determining parameter settings of the approximate methods.

Examples

# Find the 4 nearest neighbors using Euclidean distance
# If you pass a data frame, non-numeric columns are removed
iris_nn <- brute_force_knn(iris, k = 4, metric = "euclidean")

# Manhattan (l1) distance
iris_nn <- brute_force_knn(iris, k = 4, metric = "manhattan")

# Multi-threading: you can choose the number of threads to use: in real
# usage, you will want to set n_threads to at least 2
iris_nn <- brute_force_knn(iris, k = 4, metric = "manhattan", n_threads = 1)

# Use verbose flag to see information about progress
iris_nn <- brute_force_knn(iris, k = 4, metric = "euclidean", verbose = TRUE)
#> 17:01:38 Using alt metric 'sqeuclidean' for 'euclidean'
#> 17:01:38 Calculating brute force k-nearest neighbors with k = 4
#> 17:01:38 Finished