SPIDER The Spider Objects
data - Storing input data and output results data_global - Implementation of data object that limits memory overhead algorithm - Generic algorithm object group - Groups sets of objects together (algorithms or data) loss - Evaluates loss functions get_mean - Takes mean loss over groups of algs chain - Builds chains of objects: output of one to input of another param - To train and test different hyperparameters of an object cv - Cross validation using objects given data kernel - Evaluates and caches kernel functions distance - Evaluates and caches distance functions
Core library objects. wilcoxon - Wilcoxon test of statistical significance of results corrt_test - Corrected resampled t-test - for dependent trials
Statistical Tests objects. spiral - Spiral dataset generator. toy - Generator of dataset with only a few relevant features. toy2d - Simple 2d Gaussian problem generator toyreg - Linear Regression with o outputs and n inputs
Dataset objects. normalize - Simple normalization of data map - General user specified mapping function of data
Pre-Processing objects. parzen - Parzen's windows kernel density estimator indep - Density estimator which assumes feature independence bayes - Classifer based on density estimation for each class gauss - Normal distribution density estimator
Density Estimation objects. svm - Support Vector Machine (svm) c45 - C4.5 for binary or multi-class knn - k-nearest neighbours platt - Conditional Probability estimation for margin classifiers mksvm - Multi-Kernel LP-SVM anorm - Minimize the a-norm in alpha space using kernels lgcz - Local and Global Consistent Learner hmm - Hidden Markov Model loom - Leave One Out Machine l1 - Minimize l1 norm of w for a linear separator kde - Kernel Dependency Estimation: general input/output machine rank_perceptron - general input / output mapping with joint kernels
Pattern Recognition objects. one_vs_rest - Voting method of one against the rest (also for multi-label) one_vs_one - Voting method of one against one mc_svm - Multi-class SVM c45 - C4.5 for binary or multi-class knn - k-nearest neighbours
Multi-Class and Multi-label objects. feat_sel - Generic object for feature selection + classifier r2w2_sel - SVM Bound-based gradient descent selection rfe - Recursive Feature Elimination (also for the non-linear case) l0 - Dual zero-norm minimization (Weston, Elisseeff) fsv - Primal zero-norm based feature selection (Mangasarian) fisher - Fisher criterion feature selection mars - selection algorithm of Friedman (greedy selection) clustub - Multi-class feature selection using spectral clustering mutinf - Mutual Information for feature selection.
Feature Selection objects. svr - Support Vector Regression gproc - Gaussian Process Regression relvm_r - Relevance vector machine multi_rr - (possibly multi-dimensional) ridge regression mrs - Multivariate Regression via Stiefel Constraints knn - k-nearest neighbours multi_reg - meta method for independent multiple output regression kmp - kernel matching pursuit kpls - kernel partial least squares lms - least mean squared regression [now obselete due to multi_rr]
Regression objects. gridsel - select parameters from a grid of values r2w2_sel - Selecting SVM parameters by generalization bound bayessel - Bayessian parameter selection
Model Selection objects. one_class_svm - One class SVM kmeans - K means clustering kpca - Kernel Principal Components Analysis ppca - Probabilistic Principal Components Analysis nmf - Non-negative Matrix factorization spectral - Spectral clustering kvq - Kernel Vector Quantization mrank - Manifold ranking
Unsupervised objects.