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mvpa.clfs.warehouseΒΆ
Collection of classifiers to ease the exploration.
Functions
| absolute_features() | Returns a mapper that converts features into absolute values. |
| maxofabs_sample() | Returns a mapper that finds max of absolute values of all samples. |
Classes
| BLR(**kwargs[, 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(**kwargs[, kernel]) | Gaussian Process Regression (GPR). |
| GeneralizedLinearKernel(*args, **kwargs) | The linear kernel class. |
| LDA(**kwargs) | Linear Discriminant Analysis. |
| LinearCSVMC(**kwargs[, 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(**kwargs[, nu]) | Nu-SVM classifier using linear kernel. |
| LinearSGKernel(**kwargs[, normalizer_cls, ...]) | A basic linear kernel computed via Shogun: K(a,b) = a*b.T |
| LinearSVMKernel | A simple Linear kernel: K(a,b) = a*b.T |
| MulticlassClassifier(clf, **kwargs[, bclf_type]) | CombinedClassifier to perform multiclass using a list of |
| OneWayAnova(**kwargs[, space]) | FeaturewiseMeasure that performs a univariate ANOVA. |
| PLR(**kwargs[, lm, criterion, reduced, maxiter]) | Penalized logistic regression Classifier. |
| PolyLSKernel(**kwargs) | Polynomial kernel: K(a,b) = (gamma*a*b.T + coef0)**degree |
| PolySGKernel(**kwargs) | Polynomial kernel: K(a,b) = (a*b.T + c)**degree |
| QDA(**kwargs) | Quadratic Discriminant Analysis. |
| RangeElementSelector(**kwargs[, lower, ...]) | Select elements based on specified range of values .. |
| RbfCSVMC(**kwargs[, C]) | C-SVM classifier using a radial basis function kernel |
| RbfLSKernel(**kwargs) | Radial Basis Function kernel (aka Gaussian): |
| RbfNuSVMC(**kwargs[, nu]) | Nu-SVM classifier using a radial basis function kernel |
| RbfSGKernel(**kwargs) | Radial basis function: K(a,b) = exp(-||a-b||**2/sigma) |
| RbfSVMKernel | Radial Basis Function kernel (aka Gaussian): |
| RegressionAsClassifier(clf, **kwargs[, ...]) | Allows to use arbitrary regression for classification. |
| SKLLearnerAdapter(skl_learner, **kwargs[, ...]) | Generic adapter for instances of learners provided by scikits.learn |
| SMLR(**kwargs) | Sparse Multinomial Logistic Regression Classifier. |
| SMLRWeights(clf, **kwargs[, 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, **kwargs[, ...]) | BoostedClassifier to work on splits of the data |
| SquaredExponentialKernel(**kwargs[, ...]) | The Squared Exponential kernel class. |
| Warehouse([known_tags, matches]) | Class to keep known instantiated classifiers |
| kNN(**kwargs[, k, dfx, voting]) | k-Nearest-Neighbour classifier. |
| sklLDA | Linear Discriminant Analysis (LDA) |
