Math Functions — znorm • matrixprofiler

znorm(): Normalizes data for mean Zero and Standard Deviation One

ed_corr(): Converts euclidean distances into correlation values

corr_ed(): Converts correlation values into euclidean distances

mode(): Returns the most common value from a vector of integers

std(): Population SD, as R always calculate with n-1 (sample), here we fix it.

normalize(): Normalizes data to be between min and max.

complexity(): Computes the complexity index of the data

binary_split(): Creates a vector with the indexes of binary split.

znorm(data, rcpp = TRUE)

ed_corr(data, w, rcpp = TRUE)

corr_ed(data, w, rcpp = TRUE)

mode(x, rcpp = FALSE)

std(data, na.rm = FALSE, rcpp = TRUE)

normalize(data, min_lim = 0, max_lim = 1, rcpp = FALSE)

complexity(data)

binary_split(n, rcpp = TRUE)

Arguments

data	a `vector` of `numeric`.
rcpp	A `logical`. If `TRUE` will use the Rcpp implementation, otherwise will use the R implementation, that may or not be slower.
w	the window size
x	a `vector` of `integers`.
na.rm	A logical. If `TRUE` remove the `NA` values from the computation.
min_lim	A number
max_lim	A number
n	size of the vector

Value

znorm(): Returns the normalized data

ed_corr(): Returns the converted values from euclidean distance to correlation values.

corr_ed(): Returns the converted values from euclidean distance to correlation values.

mode(): Returns the most common value from a vector of integers.

std(): Returns the corrected standard deviation from sample to population.

normalize(): Returns the normalized data between min and max.

complexity(): Returns the complexity index of the data provided (normally a subset).

complexity(): Returns a vector with the binary split indexes.

Examples

normalized <- znorm(motifs_discords_small)
fake_data <- c(rep(3, 100), rep(2, 100), rep(1, 100))
correlation <- ed_corr(fake_data, 50)
fake_data <- c(rep(0.5, 100), rep(1, 100), rep(0.1, 100))
euclidean <- corr_ed(fake_data, 50)
fake_data <- c(1, 1, 4, 5, 2, 3, 1, 7, 9, 4, 5, 2, 3)
mode <- mode(fake_data)
fake_data <- c(1, 1.4, 4.3, 5.1, 2, 3.6, 1.24, 2, 9, 4.3, 5, 2.1, 3)
res <- std(fake_data)
fake_data <- c(1, 1.4, 4.3, 5.1, 2, 3.6, 1.24, 1, 9, 4.3, 5, 2.1, 3)
res <- normalize(fake_data)
fake_data <- c(1, 1.4, 4.3, 5.1, 2, 3.6, 1.24, 8, 9, 4.3, 5, 2.1, 3)
res <- complexity(fake_data)
fake_data <- c(10)
res <- binary_split(fake_data)