Package mdp :: Package nodes
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Package nodes

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  ARDRegressionScikitsLearnNode
Bayesian ARD regression.
  AdaBoostClassifierScikitsLearnNode
An AdaBoost classifier.
  AdaBoostRegressorScikitsLearnNode
An AdaBoost regressor.
  AdaptiveCutoffNode
Node which uses the data history during training to learn cutoff values.
  AdditiveChi2SamplerScikitsLearnNode
Approximate feature map for additive chi2 kernel.
  AffinityPropagationScikitsLearnNode
Perform Affinity Propagation Clustering of data.
  BaggingClassifierScikitsLearnNode
A Bagging classifier.
  BaggingRegressorScikitsLearnNode
A Bagging regressor.
  BayesianRidgeScikitsLearnNode
Bayesian ridge regression
  BernoulliNBScikitsLearnNode
Naive Bayes classifier for multivariate Bernoulli models.
  BernoulliRBMScikitsLearnNode
Bernoulli Restricted Boltzmann Machine (RBM).
  BinarizerScikitsLearnNode
Binarize data (set feature values to 0 or 1) according to a threshold
  BirchScikitsLearnNode
Implements the Birch clustering algorithm.
  CCAScikitsLearnNode
CCA Canonical Correlation Analysis.
  CalibratedClassifierCVScikitsLearnNode
Probability calibration with isotonic regression or sigmoid.
  Convolution2DNode
Convolve input data with filter banks.
  CountVectorizerScikitsLearnNode
Convert a collection of text documents to a matrix of token counts
  CuBICANode
Perform Independent Component Analysis using the CuBICA algorithm. Note that CuBICA is a batch-algorithm, which means that it needs all input data before it can start and compute the ICs. The algorithm is here given as a Node for convenience, but it actually accumulates all inputs it receives. Remember that to avoid running out of memory when you have many components and many time samples.
  CutoffNode
Node to cut off values at specified bounds.
  DPGMMScikitsLearnNode
Variational Inference for the Infinite Gaussian Mixture Model.
  DecisionTreeClassifierScikitsLearnNode
A decision tree classifier.
  DecisionTreeRegressorScikitsLearnNode
A decision tree regressor.
  DictVectorizerScikitsLearnNode
Transforms lists of feature-value mappings to vectors.
  DictionaryLearningScikitsLearnNode
Dictionary learning
  DiscreteHopfieldClassifier
Node for simulating a simple discrete Hopfield model
  ElasticNetCVScikitsLearnNode
Elastic Net model with iterative fitting along a regularization path
  ElasticNetScikitsLearnNode
Linear regression with combined L1 and L2 priors as regularizer.
  EtaComputerNode
Compute the eta values of the normalized training data.
  ExtraTreeClassifierScikitsLearnNode
An extremely randomized tree classifier.
  ExtraTreeRegressorScikitsLearnNode
An extremely randomized tree regressor.
  ExtraTreesClassifierScikitsLearnNode
An extra-trees classifier.
  ExtraTreesRegressorScikitsLearnNode
An extra-trees regressor.
  FANode
Perform Factor Analysis.
  FDANode
Perform a (generalized) Fisher Discriminant Analysis of its input. It is a supervised node that implements FDA using a generalized eigenvalue approach.
  FactorAnalysisScikitsLearnNode
Factor Analysis (FA)
  FastICANode
Perform Independent Component Analysis using the FastICA algorithm. Note that FastICA is a batch-algorithm. This means that it needs all input data before it can start and compute the ICs. The algorithm is here given as a Node for convenience, but it actually accumulates all inputs it receives. Remember that to avoid running out of memory when you have many components and many time samples.
  FastICAScikitsLearnNode
FastICA: a fast algorithm for Independent Component Analysis.
  FeatureAgglomerationScikitsLearnNode
Agglomerate features.
  FeatureHasherScikitsLearnNode
Implements feature hashing, aka the hashing trick.
  ForestRegressorScikitsLearnNode
Base class for forest of trees-based regressors.
  FunctionTransformerScikitsLearnNode
Constructs a transformer from an arbitrary callable.
  GMMScikitsLearnNode
Gaussian Mixture Model
  GaussianClassifier
Perform a supervised Gaussian classification.
  GaussianNBScikitsLearnNode
Gaussian Naive Bayes (GaussianNB)
  GaussianProcessScikitsLearnNode
The Gaussian Process model class.
  GaussianRandomProjectionHashScikitsLearnNode
This node has been automatically generated by wrapping the sklearn.neighbors.approximate.GaussianRandomProjectionHash class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
  GaussianRandomProjectionScikitsLearnNode
Reduce dimensionality through Gaussian random projection
  GeneralExpansionNode
Expands the input samples by applying to them one or more functions provided.
  GenericUnivariateSelectScikitsLearnNode
Univariate feature selector with configurable strategy.
  GradientBoostingClassifierScikitsLearnNode
Gradient Boosting for classification.
  GradientBoostingRegressorScikitsLearnNode
Gradient Boosting for regression.
  GridSearchCVScikitsLearnNode
Exhaustive search over specified parameter values for an estimator.
  GrowingNeuralGasExpansionNode
Perform a trainable radial basis expansion, where the centers and sizes of the basis functions are learned through a growing neural gas.
  GrowingNeuralGasNode
Learn the topological structure of the input data by building a corresponding graph approximation.
  HLLENode
Perform a Hessian Locally Linear Embedding analysis on the data.
  HashingVectorizerScikitsLearnNode
Convert a collection of text documents to a matrix of token occurrences
  HistogramNode
Node which stores a history of the data during its training phase.
  HitParadeNode
Collect the first n local maxima and minima of the training signal which are separated by a minimum gap d.
  ICANode
ICANode is a general class to handle different batch-mode algorithm for Independent Component Analysis. More information about ICA can be found among others in Hyvarinen A., Karhunen J., Oja E. (2001). Independent Component Analysis, Wiley.
  ISFANode
Perform Independent Slow Feature Analysis on the input data.
  IdentityNode
Execute returns the input data and the node is not trainable.
  ImputerScikitsLearnNode
Imputation transformer for completing missing values.
  IncrementalPCAScikitsLearnNode
Incremental principal components analysis (IPCA).
  IsomapScikitsLearnNode
Isomap Embedding
  IsotonicRegressionScikitsLearnNode
Isotonic regression model.
  JADENode
Perform Independent Component Analysis using the JADE algorithm. Note that JADE is a batch-algorithm. This means that it needs all input data before it can start and compute the ICs. The algorithm is here given as a Node for convenience, but it actually accumulates all inputs it receives. Remember that to avoid running out of memory when you have many components and many time samples.
  KMeansClassifier
Employs K-Means Clustering for a given number of centroids.
  KMeansScikitsLearnNode
K-Means clustering
  KNNClassifier
K-Nearest-Neighbour Classifier.
  KNeighborsClassifierScikitsLearnNode
Classifier implementing the k-nearest neighbors vote.
  KNeighborsRegressorScikitsLearnNode
Regression based on k-nearest neighbors.
  KernelCentererScikitsLearnNode
Center a kernel matrix
  KernelPCAScikitsLearnNode
Kernel Principal component analysis (KPCA)
  KernelRidgeScikitsLearnNode
Kernel ridge regression.
  LLENode
Perform a Locally Linear Embedding analysis on the data.
  LabelBinarizerScikitsLearnNode
Binarize labels in a one-vs-all fashion
  LabelEncoderScikitsLearnNode
Encode labels with value between 0 and n_classes-1.
  LabelPropagationScikitsLearnNode
Label Propagation classifier
  LabelSpreadingScikitsLearnNode
LabelSpreading model for semi-supervised learning
  LarsCVScikitsLearnNode
Cross-validated Least Angle Regression model
  LarsScikitsLearnNode
Least Angle Regression model a.k.a.
  LassoCVScikitsLearnNode
Lasso linear model with iterative fitting along a regularization path
  LassoLarsCVScikitsLearnNode
Cross-validated Lasso, using the LARS algorithm
  LassoLarsICScikitsLearnNode
Lasso model fit with Lars using BIC or AIC for model selection
  LassoLarsScikitsLearnNode
Lasso model fit with Least Angle Regression a.k.a.
  LassoScikitsLearnNode
Linear Model trained with L1 prior as regularizer (aka the Lasso)
  LatentDirichletAllocationScikitsLearnNode
Latent Dirichlet Allocation with online variational Bayes algorithm
  LibSVMClassifier
The LibSVMClassifier class acts as a wrapper around the LibSVM library for support vector machines.
  LinearDiscriminantAnalysisScikitsLearnNode
Linear Discriminant Analysis
  LinearModelCVScikitsLearnNode
This node has been automatically generated by wrapping the sklearn.linear_model.coordinate_descent.LinearModelCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
  LinearRegressionNode
Compute least-square, multivariate linear regression on the input data, i.e., learn coefficients b_j so that:
  LinearRegressionScikitsLearnNode
Ordinary least squares Linear Regression.
  LinearSVCScikitsLearnNode
Linear Support Vector Classification.
  LinearSVRScikitsLearnNode
Linear Support Vector Regression.
  LocallyLinearEmbeddingScikitsLearnNode
Locally Linear Embedding
  LogOddsEstimatorScikitsLearnNode
This node has been automatically generated by wrapping the sklearn.ensemble.gradient_boosting.LogOddsEstimator class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
  LogisticRegressionCVScikitsLearnNode
Logistic Regression CV (aka logit, MaxEnt) classifier.
  LogisticRegressionScikitsLearnNode
Logistic Regression (aka logit, MaxEnt) classifier.
  MaxAbsScalerScikitsLearnNode
Scale each feature by its maximum absolute value.
  MeanEstimatorScikitsLearnNode
This node has been automatically generated by wrapping the sklearn.ensemble.gradient_boosting.MeanEstimator class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
  MeanShiftScikitsLearnNode
Mean shift clustering using a flat kernel.
  MinMaxScalerScikitsLearnNode
Transforms features by scaling each feature to a given range.
  MiniBatchDictionaryLearningScikitsLearnNode
Mini-batch dictionary learning
  MiniBatchKMeansScikitsLearnNode
Mini-Batch K-Means clustering
  MiniBatchSparsePCAScikitsLearnNode
Mini-batch Sparse Principal Components Analysis
  MultiLabelBinarizerScikitsLearnNode
Transform between iterable of iterables and a multilabel format
  MultiTaskElasticNetCVScikitsLearnNode
Multi-task L1/L2 ElasticNet with built-in cross-validation.
  MultiTaskElasticNetScikitsLearnNode
Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer
  MultiTaskLassoCVScikitsLearnNode
Multi-task L1/L2 Lasso with built-in cross-validation.
  MultiTaskLassoScikitsLearnNode
Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer
  MultinomialNBScikitsLearnNode
Naive Bayes classifier for multinomial models
  NIPALSNode
Perform Principal Component Analysis using the NIPALS algorithm. This algorithm is particularyl useful if you have more variable than observations, or in general when the number of variables is huge and calculating a full covariance matrix may be unfeasable. It's also more efficient of the standard PCANode if you expect the number of significant principal components to be a small. In this case setting output_dim to be a certain fraction of the total variance, say 90%, may be of some help.
  NMFScikitsLearnNode
Non-Negative Matrix Factorization (NMF)
  NearestCentroidScikitsLearnNode
Nearest centroid classifier.
  NearestMeanClassifier
Nearest-Mean classifier.
  NeuralGasNode
Learn the topological structure of the input data by building a corresponding graph approximation (original Neural Gas algorithm).
  NoiseNode
Inject multiplicative or additive noise into the input data.
  NormalNoiseNode
Special version of NoiseNode for Gaussian additive noise.
  NormalizeNode
Make input signal meanfree and unit variance
  NormalizerScikitsLearnNode
Normalize samples individually to unit norm.
  NuSVCScikitsLearnNode
Nu-Support Vector Classification.
  NuSVRScikitsLearnNode
Nu Support Vector Regression.
  NystroemScikitsLearnNode
Approximate a kernel map using a subset of the training data.
  OneClassSVMScikitsLearnNode
Unsupervised Outlier Detection.
  OneHotEncoderScikitsLearnNode
Encode categorical integer features using a one-hot aka one-of-K scheme.
  OneVsOneClassifierScikitsLearnNode
One-vs-one multiclass strategy
  OneVsRestClassifierScikitsLearnNode
One-vs-the-rest (OvR) multiclass/multilabel strategy
  OrthogonalMatchingPursuitCVScikitsLearnNode
Cross-validated Orthogonal Matching Pursuit model (OMP)
  OrthogonalMatchingPursuitScikitsLearnNode
Orthogonal Matching Pursuit model (OMP)
  OutputCodeClassifierScikitsLearnNode
(Error-Correcting) Output-Code multiclass strategy
  PCANode
Filter the input data through the most significatives of its principal components.
  PCAScikitsLearnNode
Principal component analysis (PCA)
  PLSCanonicalScikitsLearnNode
PLSCanonical implements the 2 blocks canonical PLS of the original Wold algorithm [Tenenhaus 1998] p.204, referred as PLS-C2A in [Wegelin 2000].
  PLSRegressionScikitsLearnNode
PLS regression
  PLSSVDScikitsLearnNode
Partial Least Square SVD
  PassiveAggressiveClassifierScikitsLearnNode
Passive Aggressive Classifier
  PassiveAggressiveRegressorScikitsLearnNode
Passive Aggressive Regressor
  PatchExtractorScikitsLearnNode
Extracts patches from a collection of images
  PerceptronClassifier
A simple perceptron with input_dim input nodes.
  PerceptronScikitsLearnNode
Perceptron
  PolynomialExpansionNode
Perform expansion in a polynomial space.
  PolynomialFeaturesScikitsLearnNode
Generate polynomial and interaction features.
  PriorProbabilityEstimatorScikitsLearnNode
An estimator predicting the probability of each
  ProjectedGradientNMFScikitsLearnNode
Non-Negative Matrix Factorization (NMF)
  QuadraticDiscriminantAnalysisScikitsLearnNode
Quadratic Discriminant Analysis
  QuadraticExpansionNode
Perform expansion in the space formed by all linear and quadratic monomials. QuadraticExpansionNode() is equivalent to a PolynomialExpansionNode(2)
  QuantileEstimatorScikitsLearnNode
This node has been automatically generated by wrapping the sklearn.ensemble.gradient_boosting.QuantileEstimator class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
  RANSACRegressorScikitsLearnNode
RANSAC (RANdom SAmple Consensus) algorithm.
  RBFExpansionNode
Expand input space with Gaussian Radial Basis Functions (RBFs).
  RBFSamplerScikitsLearnNode
Approximates feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.
  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.
  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.
  RFECVScikitsLearnNode
Feature ranking with recursive feature elimination and cross-validated selection of the best number of features.
  RFEScikitsLearnNode
Feature ranking with recursive feature elimination.
  RadiusNeighborsClassifierScikitsLearnNode
Classifier implementing a vote among neighbors within a given radius
  RadiusNeighborsRegressorScikitsLearnNode
Regression based on neighbors within a fixed radius.
  RandomForestClassifierScikitsLearnNode
A random forest classifier.
  RandomForestRegressorScikitsLearnNode
A random forest regressor.
  RandomTreesEmbeddingScikitsLearnNode
An ensemble of totally random trees.
  RandomizedLassoScikitsLearnNode
Randomized Lasso.
  RandomizedLogisticRegressionScikitsLearnNode
Randomized Logistic Regression
  RandomizedPCAScikitsLearnNode
Principal component analysis (PCA) using randomized SVD
  RandomizedSearchCVScikitsLearnNode
Randomized search on hyper parameters.
  RidgeCVScikitsLearnNode
Ridge regression with built-in cross-validation.
  RidgeClassifierCVScikitsLearnNode
Ridge classifier with built-in cross-validation.
  RidgeClassifierScikitsLearnNode
Classifier using Ridge regression.
  RidgeScikitsLearnNode
Linear least squares with l2 regularization.
  RobustScalerScikitsLearnNode
Scale features using statistics that are robust to outliers.
  SFA2Node
Get an input signal, expand it in the space of inhomogeneous polynomials of degree 2 and extract its slowly varying components. The get_quadratic_form method returns the input-output function of one of the learned unit as a QuadraticForm object. See the documentation of mdp.utils.QuadraticForm for additional information.
  SFANode
Extract the slowly varying components from the input data. More information about Slow Feature Analysis can be found in Wiskott, L. and Sejnowski, T.J., Slow Feature Analysis: Unsupervised Learning of Invariances, Neural Computation, 14(4):715-770 (2002).
  SGDClassifierScikitsLearnNode
Linear classifiers (SVM, logistic regression, a.o.) with SGD training.
  SGDRegressorScikitsLearnNode
Linear model fitted by minimizing a regularized empirical loss with SGD
  SVCScikitsLearnNode
C-Support Vector Classification.
  SVRScikitsLearnNode
Epsilon-Support Vector Regression.
  ScaledLogOddsEstimatorScikitsLearnNode
This node has been automatically generated by wrapping the sklearn.ensemble.gradient_boosting.ScaledLogOddsEstimator class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
  SelectFdrScikitsLearnNode
Filter: Select the p-values for an estimated false discovery rate
  SelectFprScikitsLearnNode
Filter: Select the pvalues below alpha based on a FPR test.
  SelectFromModelScikitsLearnNode
Meta-transformer for selecting features based on importance weights.
  SelectFweScikitsLearnNode
Filter: Select the p-values corresponding to Family-wise error rate
  SelectKBestScikitsLearnNode
Select features according to the k highest scores.
  SelectPercentileScikitsLearnNode
Select features according to a percentile of the highest scores.
  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
  SimpleMarkovClassifier
A simple version of a Markov classifier. It can be trained on a vector of tuples the label being the next element in the testing data.
  SkewedChi2SamplerScikitsLearnNode
Approximates feature map of the "skewed chi-squared" kernel by Monte Carlo approximation of its Fourier transform.
  SparseCoderScikitsLearnNode
Sparse coding
  SparsePCAScikitsLearnNode
Sparse Principal Components Analysis (SparsePCA)
  SparseRandomProjectionScikitsLearnNode
Reduce dimensionality through sparse random projection
  StandardScalerScikitsLearnNode
Standardize features by removing the mean and scaling to unit variance
  TDSEPNode
Perform Independent Component Analysis using the TDSEP algorithm. Note that TDSEP, as implemented in this Node, is an online algorithm, i.e. it is suited to be trained on huge data sets, provided that the training is done sending small chunks of data for each time.
  TfidfTransformerScikitsLearnNode
Transform a count matrix to a normalized tf or tf-idf representation
  TfidfVectorizerScikitsLearnNode
Convert a collection of raw documents to a matrix of TF-IDF features.
  TheilSenRegressorScikitsLearnNode
Theil-Sen Estimator: robust multivariate regression model.
  TimeDelayNode
Copy delayed version of the input signal on the space dimensions.
  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.
  TimeFramesNode
Copy delayed version of the input signal on the space dimensions.
  TruncatedSVDScikitsLearnNode
Dimensionality reduction using truncated SVD (aka LSA).
  VBGMMScikitsLearnNode
Variational Inference for the Gaussian Mixture Model
  VarianceThresholdScikitsLearnNode
Feature selector that removes all low-variance features.
  VotingClassifierScikitsLearnNode
Soft Voting/Majority Rule classifier for unfitted estimators.
  WhiteningNode
Whiten the input data by filtering it through the most significatives of its principal components. All output signals have zero mean, unit variance and are decorrelated.
  XSFANode
Perform Non-linear Blind Source Separation using Slow Feature Analysis.
  ZeroEstimatorScikitsLearnNode
This node has been automatically generated by wrapping the sklearn.ensemble.gradient_boosting.ZeroEstimator class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
  _OneDimensionalHitParade
Class to produce hit-parades (i.e., a list of the largest and smallest values) out of a one-dimensional time-series.
Functions [hide private]
 
_expanded_dim(degree, nvariables)
Return the size of a vector of dimension nvariables after a polynomial expansion of degree degree.
Variables [hide private]
  __package__ = 'mdp.nodes'
Function Details [hide private]

_expanded_dim(degree, nvariables)

 
Return the size of a vector of dimension nvariables after a polynomial expansion of degree degree.

Variables Details [hide private]

__package__

Value:
'mdp.nodes'