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AdaptiveCutoffNode Node which uses the data history during training to learn cutoff values. |
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Convolution2DNode Convolve input data with filter banks. |
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CuBICANode Perform Independent Component Analysis using the CuBICA algorithm. |
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CutoffNode Node to cut off values at specified bounds. |
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DiscreteHopfieldClassifier Node for simulating a simple discrete Hopfield model |
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EtaComputerNode Compute the eta values of the normalized training data. |
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FANode Perform Factor Analysis. |
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FDANode Perform a (generalized) Fisher Discriminant Analysis of its input. |
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FastICANode Perform Independent Component Analysis using the FastICA algorithm. |
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GaussianClassifier Perform a supervised Gaussian classification. |
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GeneralExpansionNode Expands the input samples by applying to them one or more functions provided. |
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GrowingNeuralGasExpansionNode Perform a trainable radial basis expansion, where the centers and sizes of the basis functions are learned through a growing neural gas. |
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GrowingNeuralGasNode Learn the topological structure of the input data by building a corresponding graph approximation. |
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HLLENode Perform a Hessian Locally Linear Embedding analysis on the data. |
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HistogramNode Node which stores a history of the data during its training phase. |
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HitParadeNode Collect the first ``n`` local maxima and minima of the training signal which are separated by a minimum gap ``d``. |
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ICANode ICANode is a general class to handle different batch-mode algorithm for Independent Component Analysis. |
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ISFANode Perform Independent Slow Feature Analysis on the input data. |
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IdentityNode Execute returns the input data and the node is not trainable. |
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JADENode Perform Independent Component Analysis using the JADE algorithm. |
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KMeansClassifier Employs K-Means Clustering for a given number of centroids. |
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KNNClassifier K-Nearest-Neighbour Classifier. |
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LLENode Perform a Locally Linear Embedding analysis on the data. |
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LibSVMClassifier The ``LibSVMClassifier`` class acts as a wrapper around the LibSVM library for support vector machines. |
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LinearRegressionNode Compute least-square, multivariate linear regression on the input data, i.e., learn coefficients ``b_j`` so that:: |
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NIPALSNode Perform Principal Component Analysis using the NIPALS algorithm. |
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NearestMeanClassifier Nearest-Mean classifier. |
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NeuralGasNode Learn the topological structure of the input data by building a corresponding graph approximation (original Neural Gas algorithm). |
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NoiseNode Inject multiplicative or additive noise into the input data. |
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NormalNoiseNode Special version of ``NoiseNode`` for Gaussian additive noise. |
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NormalizeNode Make input signal meanfree and unit variance |
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PCANode Filter the input data through the most significatives of its principal components. |
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PerceptronClassifier A simple perceptron with input_dim input nodes. |
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PolynomialExpansionNode Perform expansion in a polynomial space. |
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QuadraticExpansionNode Perform expansion in the space formed by all linear and quadratic monomials. |
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RBFExpansionNode Expand input space with Gaussian Radial Basis Functions (RBFs). |
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RBMNode Restricted Boltzmann Machine node. |
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RBMWithLabelsNode Restricted Boltzmann Machine with softmax labels. |
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SFA2Node Get an input signal, expand it in the space of inhomogeneous polynomials of degree 2 and extract its slowly varying components. |
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SFANode Extract the slowly varying components from the input data. |
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SignumClassifier This classifier node classifies as ``1`` if the sum of the data points... |
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SimpleMarkovClassifier A simple version of a Markov classifier. |
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TDSEPNode Perform Independent Component Analysis using the TDSEP algorithm. |
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TimeDelayNode Copy delayed version of the input signal on the space dimensions. |
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TimeDelaySlidingWindowNode ``TimeDelaySlidingWindowNode`` is an alternative to ``TimeDelayNode`` which should be used for online learning/execution. |
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TimeFramesNode Copy delayed version of the input signal on the space dimensions. |
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WhiteningNode *Whiten* the input data by filtering it through the most significatives of its principal components. |
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XSFANode Perform Non-linear Blind Source Separation using Slow Feature Analysis. |
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_OneDimensionalHitParade Class to produce hit-parades (i.e., a list of the largest and smallest values) out of a one-dimensional time-series. |
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Return the size of a vector of dimension ``nvariables`` after a polynomial expansion of degree ``degree``. |
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