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mvpa2.clfs.warehouseΒΆ
Collection of classifiers to ease the exploration.
Functions
| absolute_features() | Returns a mapper that converts features into absolute values. |
| is_sequence_type | isSequenceType(a) – Return True if a has a sequence type, False otherwise. |
| maxofabs_sample() | Returns a mapper that finds max of absolute values of all samples. |
Classes
| BLR([sigma_p, sigma_noise]) | Bayesian Linear Regression (BLR). |
| FeatureSelectionClassifier(clf, mapper, **kwargs) | This is nothing but a MappedClassifier. |
| FixedNElementTailSelector(nelements, **kwargs) | Given a sequence, provide set of IDs for a fixed number of to be selected |
| FractionTailSelector(felements, **kwargs) | Given a sequence, provide Ids for a fraction of elements |
| GNB(**kwargs) | Gaussian Naive Bayes Classifier. |
| GPR([kernel]) | Gaussian Process Regression (GPR). |
| GeneralizedLinearKernel(*args, **kwargs) | The linear kernel class. |
| LDA(**kwargs) | Linear Discriminant Analysis. |
| LinearCSVMC([C]) | C-SVM classifier using linear kernel. |
| LinearKernel(*args, **kwargs) | Simple linear kernel: K(a,b) = a*b.T |
| LinearLSKernel(*args, **kwargs) | A simple Linear kernel: K(a,b) = a*b.T |
| LinearNuSVMC([nu]) | Nu-SVM classifier using linear kernel. |
| LinearSVMKernel | A simple Linear kernel: K(a,b) = a*b.T |
| MulticlassClassifier(clf[, bclf_type]) | CombinedClassifier to perform multiclass using a list of |
| OneWayAnova([space]) | FeaturewiseMeasure that performs a univariate ANOVA. |
| PLR([lm, criterion, reduced, maxiter]) | Penalized logistic regression Classifier. |
| PolyLSKernel(**kwargs) | Polynomial kernel: K(a,b) = (gamma*a*b.T + coef0)**degree |
| QDA(**kwargs) | Quadratic Discriminant Analysis. |
| RangeElementSelector([lower, upper, ...]) | Select elements based on specified range of values .. |
| RbfCSVMC([C]) | C-SVM classifier using a radial basis function kernel |
| RbfLSKernel(**kwargs) | Radial Basis Function kernel (aka Gaussian): |
| RbfNuSVMC([nu]) | Nu-SVM classifier using a radial basis function kernel |
| RbfSVMKernel | Radial Basis Function kernel (aka Gaussian): |
| RegressionAsClassifier(clf[, centroids, ...]) | Allows to use arbitrary regression for classification. |
| SKLLearnerAdapter(skl_learner[, tags, ...]) | Generic adapter for instances of learners provided by scikits.learn |
| SMLR(**kwargs) | Sparse Multinomial Logistic Regression Classifier. |
| SMLRWeights(clf[, force_train]) | SensitivityAnalyzer that reports the weights SMLR trained |
| SVM(**kwargs) | Support Vector Machine Classifier. |
| SensitivityBasedFeatureSelection(...[, ...]) | Feature elimination. |
| SigmoidLSKernel(**kwargs) | Sigmoid kernel: K(a,b) = tanh(gamma*a*b.T + coef0) |
| SplitClassifier(clf[, partitioner, splitter]) | BoostedClassifier to work on splits of the data |
| SquaredExponentialKernel([length_scale, sigma_f]) | The Squared Exponential kernel class. |
| Warehouse([known_tags, matches]) | Class to keep known instantiated classifiers |
| kNN([k, dfx, voting]) | k-Nearest-Neighbour classifier. |
| sklElasticNet | Linear Model trained with L1 and L2 prior as regularizer |
| sklExtraTreesClassifier | An extra-trees classifier. |
| sklExtraTreesRegression | An extra-trees regressor. |
| sklLDA | Linear Discriminant Analysis (LDA) |
| sklLars | Least Angle Regression model a.k.a. LAR |
| sklLassoLars | Lasso model fit with Least Angle Regression a.k.a. Lars |
| sklLassoLarsIC | Lasso model fit with Lars using BIC or AIC for model selection |
| sklPLSRegression | PLS regression |
| sklRandomForestClassifier | A random forest classifier. |
| sklRandomForestRegression | A random forest regressor. |
