FORKS

Will generate a trajectory using FORKS.

This method was wrapped inside a container. The original code of this method is available here.

ti_forks(norm_function = "median", norm_quantile = 75L,
  cum_sum_exp_var = 0.9, cluster = c(4L, 10L),
  mapping_type = "Isomap", initialization = "kmeans",
  iterMax = 1000L, eta = 0.01, C = 1L)

Arguments

norm_function

No description provided by the author. Domain: mean, median, quantile. Default: median. Format: character.
norm_quantile

No description provided by the author. Domain: U(0, 100). Default: 75. Format: numeric.
cum_sum_exp_var

No description provided by the author. Domain: U(0, 1). Default: 0.9. Format: numeric.
cluster

No description provided by the author. Domain: ( U(3, 20), U(3, 20) ). Default: (4, 10). Format: integer_range.
mapping_type

No description provided by the author. Domain: Isomap, MDS, PCA, RandomForest, SpectralEmbedding, LLE_standard, tSNE. Default: Isomap. Format: character.
initialization

No description provided by the author. Domain: kmeans, kmedoids, random. Default: kmeans. Format: character.
iterMax

No description provided by the author. Domain: e^U(4.61, 11.51). Default: 1000. Format: integer.
eta

No description provided by the author. Domain: e^U(-9.21, 4.61). Default: 0.01. Format: numeric.
C

No description provided by the author. Domain: e^U(-9.21, 6.91). Default: 1. Format: numeric.

Value

A TI method wrapper to be used together with infer_trajectory

References

Sharma, M., Li, H., Sengupta, D., Prabhakar, S., Jayadeva, J., 2017. FORKS: Finding Orderings Robustly using K-means and Steiner trees.