Class Hierarchy

object: The most base type
- exceptions.BaseException: Common base class for all exceptions
  - exceptions.Exception: Common base class for all non-exit exceptions.
    - mdp.graph.GraphException: Base class for exception in the graph package.
      - mdp.graph.GraphTopologicalException: Exception thrown during a topological sort if the graph is cyclical.
    - mdp.MDPException: Base class for exceptions in MDP.
      - mdp.CircularOnlineFlowException: Base class for exceptions in Flow subclasses.
      - mdp.CrashRecoveryException: Class to handle crash recovery
        
        mdp.FlowExceptionCR: Class to handle flow-crash recovery
      - mdp.FlowException: Base class for exceptions in Flow subclasses.
        
        mdp.FlowExceptionCR: Class to handle flow-crash recovery
        
        mdp.parallel.ParallelFlowException: Standard exception for problems with ParallelFlow.
        
        mdp.parallel.NoTaskException: Exception for problems with the task creation.
      - mdp.NodeException: Base class for exceptions in Node subclasses.
        
        mdp.IsNotInvertibleException: Raised when the Node.inverse method is called although the node is not invertible.
        
        mdp.parallel.JoinParallelException: Exception for errors when joining parallel nodes.
        
        mdp.parallel.NotForkableParallelException: Exception to signal that a fork is not possible.
        
        mdp.OnlineNodeException: Base class for exceptions in OnlineNode subclasses.
        
        mdp.hinet.SwitchboardException: Exception for routing problems in the Switchboard class.
        
        mdp.hinet.DoubleRect2dSwitchboardException: Exception for routing problems in the DoubleRect2dSwitchboard class.
        
        mdp.hinet.DoubleRhomb2dSwitchboardException: Exception for routing problems in the DoubleRhomb2dSwitchboard class.
        
        mdp.hinet.Rectangular2dSwitchboardException: Exception for routing problems in the Rectangular2dSwitchboard class.
        
        mdp.TrainingException: Base class for exceptions in the training phase.
        
        mdp.IsNotTrainableException: Raised when the Node.train method is called although the node is not trainable.
        
        mdp.TrainingFinishedException: Raised when the Node.train method is called although the training phase is closed.
      - mdp.OnlineFlowException: Base class for exceptions in Flow subclasses.
      - mdp.utils.QuadraticFormException
    - exceptions.StandardError: Base class for all standard Python exceptions that do not represent interpreter exiting.
      - exceptions.EnvironmentError: Base class for I/O related errors.
        
        exceptions.OSError: OS system call failed.
    - exceptions.Warning: Base class for warning categories.
      - exceptions.DeprecationWarning: Base class for warnings about deprecated features.
        
        mdp.MDPDeprecationWarning: Warn about deprecated MDP API.
      - exceptions.UserWarning: Base class for warnings generated by user code.
        
        mdp.MDPWarning: Base class for warnings in MDP.
        
        mdp.MDPDeprecationWarning: Warn about deprecated MDP API.
- dict: dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list.
  - collections.OrderedDict: Dictionary that remembers insertion order
- unreachable.newobject: A magical object class that provides Python 2 compatibility methods:: next __unicode__ __nonzero__
  - mdp.CheckpointFunction: Base class for checkpoint functions.
    - mdp.CheckpointSaveFunction: This checkpoint function saves the node in pickle format. The pickle dump can be done either before the training phase is finished or right after that. In this way, it is for example possible to reload it in successive sessions and continue the training.
  - mdp.utils.CovarianceMatrix: This class stores an empirical covariance matrix that can be updated incrementally. A call to the 'fix' method returns the current state of the covariance matrix, the average and the number of observations, and resets the internal data.
    - mdp.utils.CrossCovarianceMatrix
    - mdp.utils.VartimeCovarianceMatrix: This class stores an empirical covariance matrix that can be updated incrementally. A call to the 'fix' method returns the current state of the covariance matrix, the average and the number of observations, and resets the internal data.
  - mdp.utils.DelayCovarianceMatrix: This class stores an empirical covariance matrix between the signal and time delayed signal that can be updated incrementally.
  - mdp.ExtensionNode: Base class for extensions nodes.
    - mdp.parallel.ParallelExtensionNode: Base class for parallel trainable MDP nodes.
      - mdp.parallel.ParallelCloneLayer: Parallel version of CloneLayer class.
      - mdp.parallel.ParallelFDANode
      - mdp.parallel.ParallelFlowNode: Parallel version of FlowNode.
      - mdp.parallel.ParallelHistogramNode: Parallel version of the HistogramNode.
      - mdp.parallel.ParallelLayer: Parallel version of a Layer.
      - mdp.parallel.ParallelSFANode: Parallel version of MDP SFA node.
  - mdp.Flow: A 'Flow' is a sequence of nodes that are trained and executed together to form a more complex algorithm. Input data is sent to the first node and is successively processed by the subsequent nodes along the sequence.
    - mdp.CheckpointFlow: Subclass of Flow class that allows user-supplied checkpoint functions to be executed at the end of each phase, for example to save the internal structures of a node for later analysis.
      - mdp.parallel.ParallelCheckpointFlow: Parallel version of CheckpointFlow.
    - mdp.OnlineFlow: An 'OnlineFlow' is a sequence of nodes that are trained online and executed together to form a more complex algorithm.
      - mdp.CircularOnlineFlow: A 'CircularOnlineFlow' is a cyclic sequence of online/non-trainable nodes that are trained and executed together to form a more complex algorithm. This type of flow is useful when one needs to train the nodes internally for several iterations using stored inputs before training with the next external input.
    - mdp.parallel.ParallelFlow: A parallel flow provides the methods for parallel training / execution.
      - mdp.parallel.ParallelCheckpointFlow: Parallel version of CheckpointFlow.
  - mdp.graph.Graph: Represent a directed graph.
  - mdp.graph.GraphEdge: Represent a graph edge and all information attached to it.
  - mdp.graph.GraphNode: Represent a graph node and all information attached to it.
  - mdp.hinet.htmlvisitor.HiNetHTMLVisitor: Class to convert a hinet flow to HTML.
    - mdp.hinet.htmlvisitor.HiNetXHTMLVisitor: Modified converter to create valid XHTML.
  - mdp.utils.MultipleCovarianceMatrices: Container class for multiple covariance matrices to easily execute operations on all matrices at the same time. Note: all operations are done in place where possible.
  - mdp.hinet.htmlvisitor.NewlineWriteFile: Decorator for file-like object.
  - mdp.Node: A Node is the basic building block of an MDP application.
    - mdp.nodes.Convolution2DNode: Convolve input data with filter banks.
    - mdp.Cumulator: A specialized version of VariadicCumulator which only fills the field self.data.
      - mdp.nodes.ARDRegressionScikitsLearnNode: Bayesian ARD regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.ARDRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization)
      - mdp.nodes.BayesianRidgeScikitsLearnNode: Bayesian ridge regression This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.BayesianRidge class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Fit a Bayesian ridge model and optimize the regularization parameters lambda (precision of the weights) and alpha (precision of the noise).
      - mdp.nodes.BinarizerScikitsLearnNode: This node has been automatically generated by wrapping the scikits.learn.preprocessing.sparse.Binarizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
      - mdp.nodes.CCAScikitsLearnNode: CCA Canonical Correlation Analysis.
      - mdp.nodes.CountVectorizerScikitsLearnNode: Convert a collection of raw documents to a matrix of token counts This node has been automatically generated by wrapping the scikits.learn.feature_extraction.text.CountVectorizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. This implementation produces a sparse representation of the counts using scipy.sparse.coo_matrix.
      - mdp.nodes.ElasticNetCVScikitsLearnNode: Elastic Net model with iterative fitting along a regularization path This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.ElasticNetCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The best model is selected by cross-validation.
      - mdp.nodes.ElasticNetScikitsLearnNode: Linear Model trained with L1 and L2 prior as regularizer This node has been automatically generated by wrapping the ``scikits.learn.linear_model.coordinate_descent.ElasticNet`` class from the ``sklearn`` library.
      - mdp.nodes.FastICAScikitsLearnNode: FastICA; a fast algorithm for Independent Component Analysis This node has been automatically generated by wrapping the scikits.learn.decomposition.fastica_.FastICA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
      - mdp.nodes.GMMHMMScikitsLearnNode: Hidden Markov Model with Gaussin mixture emissions This node has been automatically generated by wrapping the scikits.learn.hmm.GMMHMM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Attributes
      - mdp.nodes.GMMScikitsLearnNode: Gaussian Mixture Model This node has been automatically generated by wrapping the scikits.learn.mixture.GMM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Representation of a Gaussian mixture model probability distribution. This class allows for easy evaluation of, sampling from, and maximum-likelihood estimation of the parameters of a GMM distribution.
      - mdp.nodes.GaussianHMMScikitsLearnNode: Hidden Markov Model with Gaussian emissions This node has been automatically generated by wrapping the scikits.learn.hmm.GaussianHMM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Representation of a hidden Markov model probability distribution. This class allows for easy evaluation of, sampling from, and maximum-likelihood estimation of the parameters of a HMM.
      - mdp.nodes.GaussianProcessScikitsLearnNode: The Gaussian Process model class. This node has been automatically generated by wrapping the scikits.learn.gaussian_process.gaussian_process.GaussianProcess class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
      - mdp.nodes.GenericUnivariateSelectScikitsLearnNode
      - mdp.nodes.ICANode: ICANode is a general class to handle different batch-mode algorithm for Independent Component Analysis.
        
        mdp.nodes.CuBICANode: Perform Independent Component Analysis using the CuBICA algorithm.
        
        mdp.nodes.FastICANode: Perform Independent Component Analysis using the FastICA algorithm.
        
        mdp.nodes.JADENode: Perform Independent Component Analysis using the JADE algorithm.
      - mdp.nodes.KernelCentererScikitsLearnNode: Centers a kernel. This is equivalent to centering phi(X) with This node has been automatically generated by wrapping the scikits.learn.preprocessing.KernelCenterer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
      - mdp.nodes.KernelPCAScikitsLearnNode: Kernel Principal component analysis (KPCA) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.KernelPCA`` class from the ``sklearn`` library.
      - mdp.nodes.LARSScikitsLearnNode: Least Angle Regression model a.k.a. LAR This node has been automatically generated by wrapping the scikits.learn.linear_model.least_angle.LARS class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
      - mdp.nodes.LLENode: Perform a Locally Linear Embedding analysis on the data.
        
        mdp.nodes.HLLENode: Perform a Hessian Locally Linear Embedding analysis on the data.
      - mdp.nodes.LabelBinarizerScikitsLearnNode: Binarize labels in a one-vs-all fashion. This node has been automatically generated by wrapping the scikits.learn.preprocessing.LabelBinarizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Several regression and binary classification algorithms are available in the scikit. A simple way to extend these algorithms to the multi-class classification case is to use the so-called one-vs-all scheme.
      - mdp.nodes.LassoCVScikitsLearnNode: Lasso linear model with iterative fitting along a regularization path This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.LassoCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The best model is selected by cross-validation.
      - mdp.nodes.LassoLARSScikitsLearnNode: Lasso model fit with Least Angle Regression a.k.a. LARS This node has been automatically generated by wrapping the scikits.learn.linear_model.least_angle.LassoLARS class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. It is a Linear Model trained with an L1 prior as regularizer. lasso).
      - mdp.nodes.LassoScikitsLearnNode: Linear Model trained with L1 prior as regularizer (aka the Lasso) This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.Lasso class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Technically the Lasso model is optimizing the same objective function as the Elastic Net with rho=1.0 (no L2 penalty).
      - mdp.nodes.LengthNormalizerScikitsLearnNode
      - mdp.nodes.LinearModelCVScikitsLearnNode: This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.LinearModelCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
      - mdp.nodes.LinearRegressionScikitsLearnNode: Ordinary least squares Linear Regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.base.LinearRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Attributes
      - mdp.nodes.MultinomialHMMScikitsLearnNode: Hidden Markov Model with multinomial (discrete) emissions This node has been automatically generated by wrapping the scikits.learn.hmm.MultinomialHMM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Attributes
      - mdp.nodes.NIPALSNode: Perform Principal Component Analysis using the NIPALS algorithm.
      - mdp.nodes.NMFScikitsLearnNode: Non-Negative matrix factorization by Projected Gradient (NMF) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.nmf.NMF`` class from the ``sklearn`` library.
      - mdp.nodes.NormalizerScikitsLearnNode: This node has been automatically generated by wrapping the scikits.learn.preprocessing.Normalizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
      - mdp.nodes.NuSVRScikitsLearnNode: NuSVR for sparse matrices (csr) This node has been automatically generated by wrapping the ``scikits.learn.svm.sparse.classes.NuSVR`` class from the ``sklearn`` library.
      - mdp.nodes.OneClassSVMScikitsLearnNode: Unsupervised Outliers Detection. This node has been automatically generated by wrapping the scikits.learn.svm.classes.OneClassSVM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Estimate the support of a high-dimensional distribution.
      - mdp.nodes.PCAScikitsLearnNode: Principal component analysis (PCA) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.PCA`` class from the ``sklearn`` library.
      - mdp.nodes.PLSCanonicalScikitsLearnNode: PLS canonical.
      - mdp.nodes.PLSRegressionScikitsLearnNode: PLS regression (Also known PLS2 or PLS in case of one dimensional response).
      - mdp.nodes.PLSSVDScikitsLearnNode: Partial Least Square SVD This node has been automatically generated by wrapping the ``scikits.learn.pls.PLSSVD`` class from the ``sklearn`` library.
      - mdp.nodes.ProbabilisticPCAScikitsLearnNode: Additional layer on top of PCA that adds a probabilistic evaluation This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.ProbabilisticPCA`` class from the ``sklearn`` library.
      - mdp.nodes.ProjectedGradientNMFScikitsLearnNode: Non-Negative matrix factorization by Projected Gradient (NMF) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.nmf.ProjectedGradientNMF`` class from the ``sklearn`` library.
      - mdp.nodes.RFECVScikitsLearnNode: Feature ranking with Recursive feature elimination and cross validation This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFECV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
      - mdp.nodes.RFEScikitsLearnNode: Feature ranking with Recursive feature elimination This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFE class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
      - mdp.nodes.RandomizedPCAScikitsLearnNode: Principal component analysis (PCA) using randomized SVD This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.RandomizedPCA`` class from the ``sklearn`` library.
      - mdp.nodes.RidgeCVScikitsLearnNode: Ridge regression with built-in cross-validation. This node has been automatically generated by wrapping the scikits.learn.linear_model.ridge.RidgeCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. By default, it performs Generalized Cross-Validation, which is a form of efficient Leave-One-Out cross-validation. Currently, only the n_features > n_samples case is handled efficiently.
      - mdp.nodes.RidgeClassifierCVScikitsLearnNode
      - mdp.nodes.RidgeClassifierScikitsLearnNode: Classifier using Ridge regression This node has been automatically generated by wrapping the scikits.learn.linear_model.ridge.RidgeClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
      - mdp.nodes.RidgeScikitsLearnNode: Ridge regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.ridge.Ridge class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
      - mdp.nodes.SGDRegressorScikitsLearnNode: Linear model fitted by minimizing a regularized empirical loss with SGD This node has been automatically generated by wrapping the scikits.learn.linear_model.sparse.stochastic_gradient.SGDRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. SGD stands for Stochastic Gradient Descent: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate).
      - mdp.nodes.SVRScikitsLearnNode: epsilon-Support Vector Regression. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVR class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The free parameters in the model are C and epsilon.
      - mdp.nodes.ScalerScikitsLearnNode: Object to standardize a dataset This node has been automatically generated by wrapping the scikits.learn.preprocessing.Scaler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. It centers the dataset and optionaly scales to fix the variance to 1 for each feature
      - mdp.nodes.SelectFdrScikitsLearnNode: Filter : Select the p-values corresponding to an estimated false This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectFdr class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
      - mdp.nodes.SelectFprScikitsLearnNode: This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectFpr class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
      - mdp.nodes.SelectFweScikitsLearnNode: This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectFwe class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
      - mdp.nodes.SelectKBestScikitsLearnNode: This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectKBest class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
      - mdp.nodes.SelectPercentileScikitsLearnNode: This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectPercentile class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
      - mdp.nodes.SparseBaseLibLinearScikitsLearnNode
      - mdp.nodes.SparseBaseLibSVMScikitsLearnNode
      - mdp.nodes.TfidfTransformerScikitsLearnNode: Transform a count matrix to a TF or TF-IDF representation This node has been automatically generated by wrapping the scikits.learn.feature_extraction.text.TfidfTransformer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. TF means term-frequency while TF-IDF means term-frequency times inverse document-frequency:
      - mdp.nodes.VectorizerScikitsLearnNode: Convert a collection of raw documents to a matrix This node has been automatically generated by wrapping the scikits.learn.feature_extraction.text.Vectorizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Equivalent to CountVectorizer followed by TfidfTransformer.
      - mdp.nodes.WardAgglomerationScikitsLearnNode: Feature agglomeration based on Ward hierarchical clustering This node has been automatically generated by wrapping the scikits.learn.cluster.hierarchical.WardAgglomeration class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
    - unreachable.Cumulator
      - mdp.ClassifierCumulator: A ClassifierCumulator is a Node whose training phase simply collects all input data and labels. In this way it is possible to easily implement batch-mode learning.
        
        mdp.nodes.GNBScikitsLearnNode: Gaussian Naive Bayes (GNB) This node has been automatically generated by wrapping the scikits.learn.naive_bayes.GNB class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
        
        mdp.nodes.LDAScikitsLearnNode: Linear Discriminant Analysis (LDA) This node has been automatically generated by wrapping the scikits.learn.lda.LDA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
        
        mdp.nodes.LinearSVCScikitsLearnNode: Linear Support Vector Classification, Sparse Version This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.LinearSVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Similar to SVC with parameter kernel='linear', but uses internally liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should be faster for huge datasets.
        
        mdp.nodes.LogisticRegressionScikitsLearnNode: Logistic Regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Implements L1 and L2 regularized logistic regression.
        
        mdp.nodes.NeighborsClassifierScikitsLearnNode: Classifier implementing k-Nearest Neighbor Algorithm. This node has been automatically generated by wrapping the scikits.learn.neighbors.NeighborsClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
        
        mdp.nodes.NeighborsRegressorScikitsLearnNode: Regression based on k-Nearest Neighbor Algorithm This node has been automatically generated by wrapping the scikits.learn.neighbors.NeighborsRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The target is predicted by local interpolation of the targets associated of the k-Nearest Neighbors in the training set.
        
        mdp.nodes.NuSVCScikitsLearnNode: NuSVC for sparse matrices (csr).
        
        mdp.nodes.QDAScikitsLearnNode: Quadratic Discriminant Analysis (QDA) This node has been automatically generated by wrapping the scikits.learn.qda.QDA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
        
        mdp.nodes.SGDClassifierScikitsLearnNode: Linear model fitted by minimizing a regularized empirical loss with SGD.
        
        mdp.nodes.SVCScikitsLearnNode: C-Support Vector Classification. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
    - mdp.nodes.EtaComputerNode: Compute the eta values of the normalized training data.
    - mdp.nodes.FANode: Perform Factor Analysis.
    - mdp.nodes.FDANode: Perform a (generalized) Fisher Discriminant Analysis of its input. It is a supervised node that implements FDA using a generalized eigenvalue approach.
      - mdp.parallel.ParallelFDANode
    - mdp.hinet.FlowNode: FlowNode wraps a Flow of Nodes into a single Node.
      - mdp.hinet.CircularOnlineFlowNode: CircularOnlineFlowNode wraps a CircularOnlineFlow of OnlineNodes into a single OnlineNode.
      - mdp.hinet.OnlineFlowNode: OnlineFlowNode wraps an OnlineFlow of OnlineNodes into a single OnlineNode.
      - mdp.parallel.ParallelFlowNode: Parallel version of FlowNode.
    - mdp.nodes.GSFANode: This node implements "Graph-Based SFA (GSFA)", which is the main component of hierarchical GSFA (HGSFA).
    - mdp.nodes.GrowingNeuralGasNode: Learn the topological structure of the input data by building a corresponding graph approximation.
      - mdp.nodes.GrowingNeuralGasExpansionNode: Perform a trainable radial basis expansion, where the centers and sizes of the basis functions are learned through a growing neural gas.
      - mdp.nodes.NeuralGasNode: Learn the topological structure of the input data by building a corresponding graph approximation (original Neural Gas algorithm).
    - mdp.nodes.ICANode: ICANode is a general class to handle different batch-mode algorithm for Independent Component Analysis.
      - mdp.nodes.CuBICANode: Perform Independent Component Analysis using the CuBICA algorithm.
      - mdp.nodes.FastICANode: Perform Independent Component Analysis using the FastICA algorithm.
      - mdp.nodes.JADENode: Perform Independent Component Analysis using the JADE algorithm.
    - mdp.nodes.ISFANode: Perform Independent Slow Feature Analysis on the input data.
      - mdp.nodes.TDSEPNode: Perform Independent Component Analysis using the TDSEP algorithm.
    - mdp.hinet.Layer: Layers are nodes which consist of multiple horizontally parallel nodes.
      - mdp.hinet.CloneLayer: Layer with a single node instance that is used multiple times.
        
        mdp.hinet.CloneOnlineLayer: OnlineLayer with a single node instance that is used multiple times.
        
        mdp.parallel.ParallelCloneLayer: Parallel version of CloneLayer class.
      - mdp.hinet.OnlineLayer: OnlineLayers are nodes which consist of multiple horizontally parallel OnlineNodes.
        
        mdp.hinet.CloneOnlineLayer: OnlineLayer with a single node instance that is used multiple times.
        
        mdp.hinet.SameInputOnlineLayer: SameInputOnlineLayer is an OnlineLayer were all nodes receive the full input.
      - mdp.parallel.ParallelLayer: Parallel version of a Layer.
      - mdp.hinet.SameInputLayer: SameInputLayer is a layer were all nodes receive the full input.
        
        mdp.hinet.SameInputOnlineLayer: SameInputOnlineLayer is an OnlineLayer were all nodes receive the full input.
    - mdp.nodes.LinearRegressionNode: Compute least-square, multivariate linear regression on the input data, i.e., learn coefficients b_j so that the linear combination y_i = b_0 + b_1 x_1 + ... b_N x_N , for i = 1 ... M, minimizes the sum of squared error given the training x's and y's.
    - mdp.OnlineNode: An online Node (OnlineNode) is the basic building block of an online MDP application.
      - mdp.nodes.CCIPCANode: Candid-Covariance free Incremental Principal Component Analysis (CCIPCA) extracts the principal components from the input data incrementally.
        
        mdp.nodes.CCIPCAWhiteningNode: Incrementally updates whitening vectors for the input data using CCIPCA.
      - mdp.hinet.CircularOnlineFlowNode: CircularOnlineFlowNode wraps a CircularOnlineFlow of OnlineNodes into a single OnlineNode.
      - mdp.nodes.IncSFANode: Incremental Slow Feature Analysis (IncSFA) extracts the slowly varying components from the input data incrementally.
      - mdp.nodes.MCANode: Minor Component Analysis (MCA) extracts minor components (dual of principal components) from the input data incrementally.
      - mdp.hinet.OnlineFlowNode: OnlineFlowNode wraps an OnlineFlow of OnlineNodes into a single OnlineNode.
      - mdp.hinet.OnlineLayer: OnlineLayers are nodes which consist of multiple horizontally parallel OnlineNodes.
        
        mdp.hinet.CloneOnlineLayer: OnlineLayer with a single node instance that is used multiple times.
        
        mdp.hinet.SameInputOnlineLayer: SameInputOnlineLayer is an OnlineLayer were all nodes receive the full input.
      - mdp.PreserveDimOnlineNode: Abstract base class with output_dim == input_dim.
        
        mdp.nodes.OnlineCenteringNode: OnlineCenteringNode centers the input data, that is, subtracts the arithmetic mean (average) from the input data. This is an online learnable node.
        
        mdp.nodes.OnlineTimeDiffNode: Compute the discrete time derivative of the input using backward difference approximation:
    - mdp.nodes.PCANode: Filter the input data through the most significatives of its principal components.
      - mdp.nodes.NIPALSNode: Perform Principal Component Analysis using the NIPALS algorithm.
      - mdp.nodes.WhiteningNode: Whiten the input data by filtering it through the most significant of its principal components.
    - mdp.parallel.ParallelExtensionNode: Base class for parallel trainable MDP nodes.
      - mdp.parallel.ParallelCloneLayer: Parallel version of CloneLayer class.
      - mdp.parallel.ParallelFDANode
      - mdp.parallel.ParallelFlowNode: Parallel version of FlowNode.
      - mdp.parallel.ParallelHistogramNode: Parallel version of the HistogramNode.
      - mdp.parallel.ParallelLayer: Parallel version of a Layer.
      - mdp.parallel.ParallelSFANode: Parallel version of MDP SFA node.
    - mdp.PreserveDimNode: Abstract base class with output_dim == input_dim.
      - mdp.ClassifierNode: A ClassifierNode can be used for classification tasks that should not interfere with the normal execution flow. A reason for that is that the labels used for classification do not form a vector space, and so they don't make much sense in a flow.
        
        mdp.ClassifierCumulator: A ClassifierCumulator is a Node whose training phase simply collects all input data and labels. In this way it is possible to easily implement batch-mode learning.
        
        mdp.nodes.GNBScikitsLearnNode: Gaussian Naive Bayes (GNB) This node has been automatically generated by wrapping the scikits.learn.naive_bayes.GNB class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
        
        mdp.nodes.LDAScikitsLearnNode: Linear Discriminant Analysis (LDA) This node has been automatically generated by wrapping the scikits.learn.lda.LDA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
        
        mdp.nodes.LinearSVCScikitsLearnNode: Linear Support Vector Classification, Sparse Version This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.LinearSVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Similar to SVC with parameter kernel='linear', but uses internally liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should be faster for huge datasets.
        
        mdp.nodes.LogisticRegressionScikitsLearnNode: Logistic Regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Implements L1 and L2 regularized logistic regression.
        
        mdp.nodes.NeighborsClassifierScikitsLearnNode: Classifier implementing k-Nearest Neighbor Algorithm. This node has been automatically generated by wrapping the scikits.learn.neighbors.NeighborsClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
        
        mdp.nodes.NeighborsRegressorScikitsLearnNode: Regression based on k-Nearest Neighbor Algorithm This node has been automatically generated by wrapping the scikits.learn.neighbors.NeighborsRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The target is predicted by local interpolation of the targets associated of the k-Nearest Neighbors in the training set.
        
        mdp.nodes.NuSVCScikitsLearnNode: NuSVC for sparse matrices (csr).
        
        mdp.nodes.QDAScikitsLearnNode: Quadratic Discriminant Analysis (QDA) This node has been automatically generated by wrapping the scikits.learn.qda.QDA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
        
        mdp.nodes.SGDClassifierScikitsLearnNode: Linear model fitted by minimizing a regularized empirical loss with SGD.
        
        mdp.nodes.SVCScikitsLearnNode: C-Support Vector Classification. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
        
        mdp.nodes.DiscreteHopfieldClassifier: Node for simulating a simple discrete Hopfield model
        
        mdp.nodes.GaussianClassifier: Perform a supervised Gaussian classification.
        
        mdp.nodes.KMeansClassifier: Employs K-Means Clustering for a given number of centroids.
        
        mdp.nodes.KNNClassifier: K-Nearest-Neighbour Classifier.
        
        mdp.nodes.NearestMeanClassifier: Nearest-Mean classifier.
        
        mdp.nodes.PerceptronClassifier: A simple perceptron with input_dim input nodes.
        
        mdp.nodes.SignumClassifier: This classifier node classifies as 1 if the sum of the data points is positive and as -1 if the data point is negative.
        
        mdp.nodes.SimpleMarkovClassifier: A simple version of a Markov classifier.
      - mdp.nodes.CutoffNode: Node to cut off values at specified bounds.
      - mdp.nodes.HistogramNode: Node which stores a history of the data during its training phase.
        
        mdp.nodes.AdaptiveCutoffNode: Node which uses the data history during training to learn cutoff values.
        
        mdp.parallel.ParallelHistogramNode: Parallel version of the HistogramNode.
      - mdp.nodes.HitParadeNode: Collect the first n local maxima and minima of the training signal which are separated by a minimum gap d.
      - mdp.nodes.IdentityNode: Execute returns the input data and the node is not trainable.
      - mdp.nodes.NoiseNode: Inject multiplicative or additive noise into the input data.
      - mdp.nodes.NormalNoiseNode: Special version of NoiseNode for Gaussian additive noise.
      - mdp.nodes.NormalizeNode: Make input signal meanfree and unit variance.
      - mdp.PreserveDimOnlineNode: Abstract base class with output_dim == input_dim.
        
        mdp.nodes.OnlineCenteringNode: OnlineCenteringNode centers the input data, that is, subtracts the arithmetic mean (average) from the input data. This is an online learnable node.
        
        mdp.nodes.OnlineTimeDiffNode: Compute the discrete time derivative of the input using backward difference approximation:
    - mdp.nodes.RBFExpansionNode: Expand input space with Gaussian Radial Basis Functions (RBFs).
    - mdp.nodes.RBMNode: Restricted Boltzmann Machine node. An RBM is an undirected probabilistic network with binary variables. The graph is bipartite into observed (visible) and hidden (latent) variables. By default, the execute method returns the probability of one of the hiden variables being equal to 1 given the input. Use the sample_v method to sample from the observed variables given a setting of the hidden variables, and sample_h to do the opposite. The energy method can be used to compute the energy of a given setting of all variables.
      - mdp.nodes.RBMWithLabelsNode: Restricted Boltzmann Machine with softmax labels. An RBM is an undirected probabilistic network with binary variables. In this case, the node is partitioned into a set of observed (visible) variables, a set of hidden (latent) variables, and a set of label variables (also observed), only one of which is active at any time. The node is able to learn associations between the visible variables and the labels. By default, the execute method returns the probability of one of the hiden variables being equal to 1 given the input. Use the sample_v method to sample from the observed variables (visible and labels) given a setting of the hidden variables, and sample_h to do the opposite. The energy method can be used to compute the energy of a given setting of all variables.
    - mdp.nodes.SFANode: Extract the slowly varying components from the input data.
      - mdp.parallel.ParallelSFANode: Parallel version of MDP SFA node.
      - mdp.nodes.SFA2Node: Get an input signal, expand it in the space of inhomogeneous polynomials of degree 2 and extract its slowly varying components.
      - mdp.nodes.VartimeSFANode: Extract the slowly varying components from the input data. This node can be understood as a generalization of the SFANode that allows non-constant time increments between samples.
    - mdp.hinet.Switchboard: Does the routing associated with the connections between layers.
      - mdp.hinet.ChannelSwitchboard: Base class for Switchboards in which the data is bundled into channels.
        
        mdp.hinet.DoubleRect2dSwitchboard: Special 2d Switchboard where each inner point is covered twice.
        
        mdp.hinet.DoubleRhomb2dSwitchboard: Rectangular lattice switchboard covering a rhombic lattice.
        
        mdp.hinet.Rectangular2dSwitchboard: Switchboard for a 2-dimensional topology.
    - mdp.nodes.TimeFramesNode: Copy delayed version of the input signal on the space dimensions.
      - mdp.nodes.TimeDelayNode: Copy delayed version of the input signal on the space dimensions.
        
        mdp.nodes.TimeDelaySlidingWindowNode: TimeDelaySlidingWindowNode is an alternative to TimeDelayNode which should be used for online learning/execution. Whereas the TimeDelayNode works in a batch manner, for online application a sliding window is necessary which yields only one row per call.
    - mdp.nodes.XSFANode: Perform Non-linear Blind Source Separation using Slow Feature Analysis. This node is designed to iteratively extract statistically independent sources from (in principle) arbitrary invertible nonlinear mixtures. The method relies on temporal correlations in the sources and consists of a combination of nonlinear SFA and a projection algorithm. More details can be found in the reference given below (once it's published).
    - unreachable._ExpansionNode
      - mdp.nodes.GeneralExpansionNode: Expands the input samples by applying to them one or more functions provided.
      - mdp.nodes.PolynomialExpansionNode: Perform expansion in a polynomial space.
        
        mdp.nodes.QuadraticExpansionNode: Perform expansion in the space formed by all linear and quadratic monomials. QuadraticExpansionNode() is equivalent to a PolynomialExpansionNode(2)
      - mdp.nodes.RecursiveExpansionNode: Recursively computable (orthogonal) expansions.
        
        mdp.nodes.NormalizingRecursiveExpansionNode: Recursively computable (orthogonal) expansions and a trainable transformation to the domain of the expansions.
    - mdp.nodes.iGSFANode: This node implements "information-preserving graph-based SFA (iGSFA)", which is the main component of hierarchical iGSFA (HiGSFA).
  - unreachable.ProjectMatrixMixin: Mixin class to be inherited by all ICA-like algorithms
    - mdp.nodes.ICANode: ICANode is a general class to handle different batch-mode algorithm for Independent Component Analysis.
      - mdp.nodes.CuBICANode: Perform Independent Component Analysis using the CuBICA algorithm.
      - mdp.nodes.FastICANode: Perform Independent Component Analysis using the FastICA algorithm.
      - mdp.nodes.JADENode: Perform Independent Component Analysis using the JADE algorithm.
    - mdp.nodes.TDSEPNode: Perform Independent Component Analysis using the TDSEP algorithm.
  - mdp.utils.QuadraticForm: Define an inhomogeneous quadratic form as 1/2 x'Hx + f'x + c . This class implements the quadratic form analysis methods presented in:
  - mdp.parallel.ResultContainer: Abstract base class for result containers.
    - mdp.parallel.ListResultContainer: Basic result container using simply a list.
      - mdp.parallel.OrderedResultContainer: Default result container with automatic restoring of the result order.
        
        mdp.parallel.ExecuteResultContainer: Default result container with automatic restoring of the result order.
    - mdp.parallel.TrainResultContainer: Container for parallel nodes.
  - mdp.parallel.Scheduler: Base class and trivial implementation for schedulers.
    - mdp.parallel.ProcessScheduler: Scheduler that distributes the task to multiple processes.
    - mdp.parallel.ThreadScheduler: Thread based scheduler.
  - unreachable.StringTemplate: A base class for string template classes.
    - mdp.utils.HTMLSlideShow: Abstract slideshow base class.
      - mdp.utils.ImageHTMLSlideShow: Slideshow for images.
        
        mdp.utils.SectionImageHTMLSlideShow: Image slideshow with section markers.
      - mdp.utils.SectionHTMLSlideShow: Astract slideshow with additional support for section markers.
        
        mdp.utils.SectionImageHTMLSlideShow: Image slideshow with section markers.
  - mdp.parallel.TaskCallable: Abstract base class for task callables.
    - mdp.parallel.FlowTaskCallable: Base class for all flow callables.
      - mdp.parallel.FlowExecuteCallable: Implements data execution through a Flow.
      - mdp.parallel.FlowTrainCallable: Implements a single training phase in a flow for a data block.
    - mdp.parallel.SleepSqrTestCallable: Callable for testing.
    - mdp.parallel.SqrTestCallable: Callable for testing.
    - mdp.parallel.TaskCallableWrapper: Wrapper to provide a fork method for simple callables like a function.
  - mdp.utils.TemporaryDirectory: Create and return a temporary directory. This has the same behavior as mkdtemp but can be used as a context manager. For example:
  - mdp.config._ExternalDep
  - mdp.nodes._OneDimensionalHitParade: Class to produce hit-parades (i.e., a list of the locally largest and smallest values) out of a one-dimensional time-series.
  - mdp.caching.cache: Context manager for the 'cache_execute' extension.
  - mdp.config: Provide information about optional dependencies.
  - mdp.extension: Context manager for MDP extension.
- type: type(object) -> the object's type...
  - mdp.NodeMetaclass: A metaclass which copies docstrings from private to public methods.
    - mdp.ExtensionNodeMetaclass: This is the metaclass for node extension superclasses.