auto_sum_path_cpp {distantia} | R Documentation |
(C++) Sum Distances Between All Consecutive Samples in the Least Cost Path Between Two Time Series
Description
Computes the cumulative auto sum of auto-distances of two time series for the coordinates of a trimmed least cost path. The output value is used as normalization factor when computing dissimilarity scores.
Usage
auto_sum_path_cpp(x, y, path, distance = "euclidean")
Arguments
x |
(required, numeric matrix) univariate or multivariate time series. |
y |
(required, numeric matrix) univariate or multivariate time series with the same number of columns as 'x'. |
path |
(required, data frame) least-cost path produced by |
distance |
(optional, character string) distance name from the "names"
column of the dataset |
Value
numeric
See Also
Other Rcpp_auto_sum:
auto_distance_cpp()
,
auto_sum_cpp()
,
auto_sum_full_cpp()
,
subset_matrix_by_rows_cpp()
Examples
#simulate two time series
x <- zoo_simulate(seed = 1)
y <- zoo_simulate(seed = 2)
#distance matrix
dist_matrix <- distance_matrix_cpp(
x = x,
y = y,
distance = "euclidean"
)
#least cost matrix
cost_matrix <- cost_matrix_orthogonal_cpp(
dist_matrix = dist_matrix
)
#least cost path
cost_path <- cost_path_orthogonal_cpp(
dist_matrix = dist_matrix,
cost_matrix = cost_matrix
)
nrow(cost_path)
#remove blocks from least-cost path
cost_path_trimmed <- cost_path_trim_cpp(
path = cost_path
)
nrow(cost_path_trimmed)
#auto sum
auto_sum_path_cpp(
x = x,
y = y,
path = cost_path_trimmed,
distance = "euclidean"
)
[Package distantia version 2.0.0 Index]