explained 1 x N Percentage of explained variance % % % Examples: % samples= % % % % apply PCA, keeping two dimensions % =cosmo_pca(samples,2) % % % % show samples in PC space % cosmo_disp(pca_samples) % %|| % % % % show parameters % cosmo_disp(params) % %||. mu M x 1 column-wise average of samples % It holds that: % % pca_samples*ef') %. coef M x retain Principal Component coefficients %. Default: N % % Output: % pca_samples M x retain samples in Principal Component % space, after samples have been centered % params struct with fields: %. Function = cosmo_pca (samples,retain ) % Principal Component Analysis % % =cosmo_pca(samples) % % Input: % samples M x N numeric matrix % retain (optional) number of components to retain % must be less than or equal to N.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |