v0.0.0.9009

New features

• Data is now stored column-wise during optimization, which should result in an increase in performance for larger values of n_components (e.g. approximately 50% faster optimization time with MNIST and n_components = 50).
• New parameter: pca_center, which controls whether to center the data before applying PCA. It would be typical to set this to FALSE if you are applying PCA to binary data (although note you can't use this with setting with metric = "hamming")
• PCA will now be used when the metric is "manhattan" and "cosine". It's still not applied when using "hamming" (data still needs to be in binary format, not real-valued).
• If using mixed datatypes, you may override the pca and pca_center parameter values for a given data block by using a list for the value of the metric, with the column ids/names as an unnamed item and the overriding values as named items, e.g. instead of manhattan = 1:100, use manhattan = list(1:100, pca_center = FALSE) to turn off PCA centering for just that block. This functionality exists mainly for the case where you have
  mixed binary and real-valued data and want to apply PCA to both data types. It's normal to apply centering to real-valued data but not to binary data.

Bug fixes and minor improvements

• Fixed bug that affected umap_transform, where negative sampling was over the size of the test data (should be the training data).
• Some other performance improvements (around 10% faster for the optimization stage with MNIST).
• When verbose = TRUE, log the Annoy recall accuracy, which may help tune values of n_trees and search_k.