snf_scheme {metasnf} | R Documentation |
SNF schemes
Description
These functions manage the way in which input data frames are passed into SNF to yield a final fused network.
Usage
two_step_merge(
dl,
k = 20,
alpha = 0.5,
t = 20,
cnt_dist_fn,
dsc_dist_fn,
ord_dist_fn,
cat_dist_fn,
mix_dist_fn,
weights_row
)
domain_merge(
dl,
cnt_dist_fn,
dsc_dist_fn,
ord_dist_fn,
cat_dist_fn,
mix_dist_fn,
weights_row,
k,
alpha,
t
)
individual(
dl,
k = 20,
alpha = 0.5,
t = 20,
cnt_dist_fn,
dsc_dist_fn,
ord_dist_fn,
cat_dist_fn,
mix_dist_fn,
weights_row
)
Arguments
dl |
A nested list of input data from |
k |
k hyperparameter. |
alpha |
alpha/eta/sigma hyperparameter. |
t |
SNF number of iterations hyperparameter. |
cnt_dist_fn |
distance metric function for continuous data. |
dsc_dist_fn |
distance metric function for discrete data. |
ord_dist_fn |
distance metric function for ordinal data. |
cat_dist_fn |
distance metric function for categorical data. |
mix_dist_fn |
distance metric function for mixed data. |
weights_row |
data frame row containing feature weights. |
Details
individual: The "vanilla" scheme - does distance matrix conversions of each input data frame separately before a single call to SNF fuses them into the final fused network.
domain_merge: Given a data list, returns a new data list where all data objects of a particular domain have been concatenated.
two_step_merge: Individual data frames into individual similarity matrices into one fused network per domain into one final fused network.