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
data_list().- 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.