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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)
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AdaptiveCutoffNode Node which uses the data history during training to learn cutoff values. |
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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).
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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.
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CCAScikitsLearnNode CCA Canonical Correlation Analysis. |
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CCIPCANode Candid-Covariance free Incremental Principal Component Analysis (CCIPCA) extracts the principal components from the input data incrementally. |
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CCIPCAWhiteningNode Incrementally updates whitening vectors for the input data using CCIPCA. |
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Convolution2DNode Convolve input data with filter banks. |
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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.
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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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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.
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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. |
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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. It is a supervised node that implements FDA using a generalized eigenvalue approach. |
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FastICANode Perform Independent Component Analysis using the FastICA algorithm. |
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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
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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
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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.
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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
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GSFANode This node implements "Graph-Based SFA (GSFA)", which is the main component of hierarchical GSFA (HGSFA). |
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GaussianClassifier Perform a supervised Gaussian classification. |
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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.
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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
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GeneralExpansionNode Expands the input samples by applying to them one or more functions provided. |
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| GenericUnivariateSelectScikitsLearnNode | |||
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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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IncSFANode Incremental Slow Feature Analysis (IncSFA) extracts the slowly varying components from the input data incrementally. |
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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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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.
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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. |
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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
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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
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LLENode Perform a Locally Linear Embedding analysis on the data. |
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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.
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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.
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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).
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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).
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| LengthNormalizerScikitsLearnNode | |||
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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.
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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.
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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
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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.
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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.
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MCANode Minor Component Analysis (MCA) extracts minor components (dual of principal components) from the input data incrementally. |
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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
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NIPALSNode Perform Principal Component Analysis using the NIPALS algorithm. |
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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. |
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NearestMeanClassifier Nearest-Mean classifier. |
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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
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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.
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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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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.
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NormalizingRecursiveExpansionNode Recursively computable (orthogonal) expansions and a trainable transformation to the domain of the expansions. |
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NuSVCScikitsLearnNode NuSVC for sparse matrices (csr). |
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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. |
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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.
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OnlineCenteringNode OnlineCenteringNode centers the input data, that is, subtracts the arithmetic mean (average) from the input data. This is an online learnable node. |
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OnlineTimeDiffNode Compute the discrete time derivative of the input using backward difference approximation: |
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PCANode Filter the input data through the most significatives of its principal components. |
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PCAScikitsLearnNode Principal component analysis (PCA) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.PCA`` class from the ``sklearn`` library. |
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PLSCanonicalScikitsLearnNode PLS canonical. |
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PLSRegressionScikitsLearnNode PLS regression (Also known PLS2 or PLS in case of one dimensional response). |
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PLSSVDScikitsLearnNode Partial Least Square SVD This node has been automatically generated by wrapping the ``scikits.learn.pls.PLSSVD`` class from the ``sklearn`` library. |
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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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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. |
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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. |
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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
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QuadraticExpansionNode Perform expansion in the space formed by all linear and quadratic monomials. QuadraticExpansionNode() is equivalent to a PolynomialExpansionNode(2) |
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RBFExpansionNode Expand input space with Gaussian Radial Basis Functions (RBFs). |
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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. |
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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. |
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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
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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
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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. |
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RecursiveExpansionNode Recursively computable (orthogonal) expansions. |
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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.
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| RidgeClassifierCVScikitsLearnNode | |||
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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
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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
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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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SGDClassifierScikitsLearnNode Linear model fitted by minimizing a regularized empirical loss with SGD. |
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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).
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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
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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SimpleMarkovClassifier A simple version of a Markov classifier. |
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| SparseBaseLibLinearScikitsLearnNode | |||
| SparseBaseLibSVMScikitsLearnNode | |||
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TDSEPNode Perform Independent Component Analysis using the TDSEP algorithm. |
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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:
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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. Whereas the TimeDelayNode works in a batch manner, for online application a sliding window is necessary which yields only one row per call. |
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TimeFramesNode Copy delayed version of the input signal on the space dimensions. |
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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. |
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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.
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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
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WhiteningNode Whiten the input data by filtering it through the most significant of its principal components. |
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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). |
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_OneDimensionalHitParade Class to produce hit-parades (i.e., a list of the locally largest and smallest values) out of a one-dimensional time-series. |
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iGSFANode This node implements "information-preserving graph-based SFA (iGSFA)", which is the main component of hierarchical iGSFA (HiGSFA). |
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| int |
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__package__ = |
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nvariables after
a polynomial expansion of degree degree.
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__package__
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