cost_path_cpp: Least Cost Path

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Least Cost Path

Description

Least cost path between two time series x and y. NA values must be removed from x and y before using this function. If the selected distance function is "chi" or "cosine", pairs of zeros should be either removed or replaced with pseudo-zeros (i.e. 0.00001).

Usage

cost_path_cpp(
  x,
  y,
  distance = "euclidean",
  diagonal = TRUE,
  weighted = TRUE,
  ignore_blocks = FALSE,
  bandwidth = 1
)

Arguments

x	(required, numeric matrix) multivariate time series.
y	(required, numeric matrix) multivariate time series with the same number of columns as 'x'.
distance	(optional, character string) distance name from the "names" column of the dataset distances (see distances$name). Default: "euclidean".
diagonal	(optional, logical). If TRUE, diagonals are included in the computation of the cost matrix. Default: TRUE.
weighted	(optional, logical). Only relevant when diagonal is TRUE. When TRUE, diagonal cost is weighted by y factor of 1.414214 (square root of 2). Default: TRUE.
ignore_blocks	(optional, logical). If TRUE, blocks of consecutive path coordinates are trimmed to avoid inflating the psi distance. Default: FALSE.
bandwidth	(required, numeric) Size of the Sakoe-Chiba band at both sides of the diagonal used to constrain the least cost path. Expressed as a fraction of the number of matrix rows and columns. Unrestricted by default. Default: 1

Value

data frame

See Also

Other Rcpp_cost_path: cost_path_diagonal_bandwidth_cpp(), cost_path_diagonal_cpp(), cost_path_orthogonal_bandwidth_cpp(), cost_path_orthogonal_cpp(), cost_path_slotting_cpp(), cost_path_sum_cpp(), cost_path_trim_cpp()

distantia documentation built on April 4, 2025, 5:42 a.m.