úέ     NoneB FComputes "covariance matrix", alternative to (snd. meanCov). Source:  Thttps://hackage.haskell.org/package/mltool-0.1.0.2/docs/src/MachineLearning.PCA.htmlr covarianceMatrix :: Matrix Double -> Matrix Double covarianceMatrix x = ((tr x) <> x) / (fromIntegral $ rows x) Produces a compression matrix u' "Principal component analysis (PCA) ~Perform supervised learning to create a linear classifier. The ridge regression is run with regularization parameter mu=1e-4. 2Create a linear readout using the ridge regression Teacher matrixHPerforms the supervised training that results in a linear readout. See 5https://en.wikipedia.org/wiki/Tikhonov_regularization&Winner-takes-all classification methodVEvaluate the network state (nonlinear response) according to some readout matrix trW.Calculates the error rate in %Returns the misclassified casesRegularization constantTransposed readout matrixVector of possible classes    Safeö     %Learning-0.0.0-5ewsoL1OmI2G19qShzkllcLearningPaths_Learning ClassifierPCA_u _compress _decompressDataset_samples_labelspcalearnlearn'teacherwinnerTakesAllscores errorRateerrorspca'ridgeRegressionversion getBinDir getLibDir getDynLibDir getDataDir getLibexecDir getSysconfDirgetDataFileName