Multivariate Pattern Analysis in Python
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PyMVPA Documentation Contents
ΒΆ
Introduction
What this Manual is NOT
A bit of History
Prerequisites
Dependencies
Strong Recommendations
Suggestions
Obtaining PyMVPA
Binary packages
Debian
Debian backports and inofficial Ubuntu packages
Building from Source
Build with enabled libsvm bindings
Building patched libsvm from source
Alternative build procedure
Installation
How to cite PyMVPA
Credits
Overview
Datasets
Data Mapping
Data Splitting
Classifiers
Stateful objects
Error Calculation
Cross-validated Transfer Error
Boosted and Multi-class Classifiers
Gaussian Process Regression
k-Nearest-Neighbour
Least Angle Regression
Penalized Logistic Regression
Ridge Regression
Sparse Multinomial Logistic Regression
Support Vector Machines
Classifiers “Warehouse”
Measures
Sensitivity Measures
Basic Sensitivity (and related Measures)
ANOVA
Linear SVM Weights
Noise Perturbation
Meta Sensitivity Measures
Splitting Measures
Feature Selection
Recursive Feature Elimination
Incremental Feature Search
Analysis Scenarios
Searchlight
Statistical Testing of classifier-based Analyses
Miscellaneous
Progress Tracking
Redirecting Output
Verbose Messages
Warning Messages
Debug Messages
Additional Little Helpers
Random Number Generation
Others
FSL Bindings
Data vs. Dataset: A Glossary
PyMVPA for Matlab Users
Frequently Asked Questions
I am tired of writing these endless import blocks. Any alternative?
I feel like I want to contribute something, do you mind?
The manual is quite insufficient. When will you improve it?
Examples
Simple Plotting of Classifier Behavior
Easy Searchlight
Sensitivity Measure
Classification of SVD-mapped Datasets
Compare SMLR to Linear SVM Classifier
License
PyMVPA Development Changelog
Releases
TODO
Navigation
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PyMVPA Home
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