úÎ3ª1r     NoneB/ÏTeacher matrix w0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 <- Desired class index is 2 0 0 0 0 0 <- Number of classes is 4 ^ 5 repetitionsLinear readout (matrix)ÌRegressor function that maps some feature matrix into a continuous multidimensional output. The feature matrix is expected to have columns corresponding to measurements (data points) and rows, features.†Classifier function that maps some network state with measurements as matrix columns and features as rows, into a categorical output.#Principal components analysis tools Compression matrix U Compression function Inverse to compression function 0A dataset representation for supervised learning Create a  1 from list of samples (first) and labels (second)Compute the covariance matrix sigma and return its eigenvectors u' and eigenvalues s+Principal components analysis resulting in  toolsDPerform supervised learning (ridge regression) and create a linear I function. The regression is run with regularization parameter ¼ = 1e-4.DPerform supervised learning (ridge regression) and create a linear  function.Create a linear ) using the ridge regression. Similar to , but instead of a " function a (already transposed)  matrix may be returned.Create a binary ? matrix with ones row corresponding to the desired class indexFPerforms a supervised training that results in a linear readout. See 5https://en.wikipedia.org/wiki/Tikhonov_regularization&Winner-takes-all classification methodCEvaluate the network state (nonlinear response) according to some 4 matrix. Used by classification strategies such as .:Error rate in %, an error measure for classification taskserrorRate [1,2,3,4] [1,2,3,7]25.0Accuracy of classification, 100% - errorRateaccuracy [1,2,3,4] [1,2,3,7]75.0)Pairs of misclassified and correct values(errors $ zip ['x','y','z'] ['x','b','a'][('y','b'),('z','a')]fNormalized root mean square error (NRMSE), one of the most common error measures for regression tasks  Data samples*Number of principal components to preserveAnalyzed data samples$All possible outcomes (classes) listŒNetwork state (nonlinear response) where each matrix column corresponds to a measurement (data point) and each row corresponds to a featureHorizontally concatenated 7 matrices where each row corresponds to a desired classMFeature matrix with data points (measurements) as colums and features as rows•Desired outputs matrix corresponding to data point columns. In case of scalar (one-dimensional) prediction output, it should be a single row matrix."Network state (nonlinear response)Horizontally concatenated  matricesNumber of classes (labels)(Desired class index (starting from zero),Number of repeated columns in teacher matrixRegularization constant matrixVector of possible classes matrix Network state Target signalPredicted signalNRMSE    Safe1K !"#$%&'(        !"#$%&'%Learning-0.0.1-4mgQMwuH9aM9zhBxOqcKZyLearningPaths_LearningTeacherReadout Regressorpredict ClassifierclassifyPCA_u _compress _decompressDataset_samples_labelstoListfromListpca'pcalearnClassifierlearnRegressorlearn'teacherwinnerTakesAllscores errorRateaccuracyerrorsnrmseridgeRegressionversion getBinDir getLibDir getDynLibDir getDataDir getLibexecDir getSysconfDirgetDataFileName