Project New Points into a SONG Embedding

Maps new data points into an existing SONG embedding by assigning each point to its nearest coding vector and returning that coding vector's embedding coordinates.

Usage

# S3 method for class 'song_model'
predict(object, newdata, ...)

Arguments

object

A trained "song_model" object.

newdata

Numeric matrix of new data (n_new x D).

...

Ignored.

Value

A numeric matrix (n_new x d) of embedding coordinates.

References

Senanayake, D. A., Wang, W., Naik, S. H., & Halgamuge, S. (2021). Self-Organizing Nebulous Growths for Robust and Incremental Data Visualization. IEEE Transactions on Neural Networks and Learning Systems, 32(10), 4588–4602. doi:10.1109/TNNLS.2020.3023941

See also

song, update.song_model

Examples

model <- song(as.matrix(iris[1:120, 1:4]), epochs = 5L, seed = 42)
#> Epoch 1/5 | CVs: 3 | QE: 1.4789 | so_lr: 1.0000 | lr: 1.0000
#> Epoch 2/5 | CVs: 5 | QE: 0.8328 | so_lr: 0.8187 | lr: 0.8000
#> Epoch 3/5 | CVs: 9 | QE: 0.5362 | so_lr: 0.4493 | lr: 0.6000
#> Epoch 4/5 | CVs: 16 | QE: 0.4402 | so_lr: 0.1653 | lr: 0.4000
#> Epoch 5/5 | CVs: 24 | QE: 0.3847 | so_lr: 0.0408 | lr: 0.2000
#> Running UMAP dispersion step...
new_coords <- predict(model, newdata = as.matrix(iris[121:150, 1:4]))