Matlab Code by Mark Schmidt
Summary
This package contains the most recent version of various Matlab codes I released during my PhD work. I would recommend downloading and using this package if you plan on using more than one of my Matlab codes. This is because this package includes all the more recent bug-fixes and efficiency-improvements, while in making this package I have updated my old code to make it compatible with the new code and newer versions of Matlab. Further, I typically do not update the individual packages unless I am making a major change (such as the updates of minFunc and UGM).
The particular packages included (from oldest to newest) are:
- minFunc - Function for
unconstrained
optimization of differentiable real-valued multivariate functions.
- lasso - Functions implementing a
variety of the methods available to solve 'LASSO' regression (and basis
selection) problems.
- blogreg
- Functions for MCMC simulation of binary probit/logistic regression posterior
distributions over parameters.
- DAGLearn
Functions for structure learning in
Gaussian and sigmoid directed acyclic graphical (DAG) models.
- L1precision - Block coordinate descent function for fitting Gaussian graphical models with an L1-norm penalty on the matrix elements.
- L1General -
Functions implementing strategies for minimizing unconstrained functions
subject to L1 regularization.
- minConf
- Functions for optimization of differentiable real-valued
multivariate functions with simple constraints.
- minFunc examples - A series of examples showing how to solve problems with minFunc.
- UGM - Functions implementing exact and approximate decoding, inference, sampling, and parameter estimation in discrete undirected graphical models.
- UGMlearn
- Code for structure learning in discrete-state undirected graphical models using group L1-regularization.
- crfChain
- Learning/inference/decoding/sampling in
chain-structured conditional random fields with categorical features.
- PQN - Code for optimization of (costly) differentiable multivariate functions over simple convex sets.
- L1General examples - A series of examples showing how to solve problems with L1General.
- PQN examples - A series of examples showing how to solve problems with PQN.
- thesis - Quasi-Newton methods for non-differentiable optimization,
and structure learning in graphical models.
- alphaBeta - Functions for approximate pairwise energy minimization with attractive
energies.
- batching - An optimizer that combines an L-BFGS line-search method with a growing batch-size strategy.
- prettyPlot - A wrapper that uses Matlab's plot function to make nicer-looking plots.
- TMP examples - A series of examples showing how to solve problems with TMP.
- QNST examples - A series of examples showing how to solve problems with QNST.
- SAG - Matlab mex files implementing the stochastic average gradient method for L2-regularized logistic regression.
Examples
Each of the packages includes one or more demos that show how to use the code. Below is a list of all the available demos (the ones highlighted in blue consist of a single function that contains a series of demos):
minFunc
- example_minFunc % Runs various limited-memory solvers in minFunc
- example_minFunc_LR % Shows how to use preconditioning and Hessian-vector products in minFunc
- example_derivativeCheck % Shows how to check user-supplied derivatives against numerical approximations
lasso
- example_lasso % Runs various LASSO solvers
- example_lasso_warmStart % Warm-starting active set LASSO solver
blogreg
- example_ProbRegSamp % Probit regression auxiliary variables samplers
- example_LogRegSamp % Logistic regression auxiliary variable samplers
- example_MultLogRegSamp % Multinomial logistic regression auxiliary variable samplers
- example_LogRegRJSamp % Reversible-jump sparse logistic regression auxiliary variable sampler
- example_blogreg % Runs various logistic regression samplers on digits data
DAGlearn
- example_DAGlearn % Runs various DAG structure learning methods
L1precision
- example_L1precision % Runs block coordinate descent for graphical LASSO
L1General
- example_L1General % Runs various solvers for L1-regularized logistic regression
minConf
- example_minConf % Shows how to use various methods in minConf
minFunc_examples.m
- Huber and Student T robust regression
- Robust Regression with Basis Expansion
- Logistic and Probit regression
- L2-regularized logistic regression
- Weighted Logistic regression
- Kernel logistic regression
- Multinomial logistic regression with L2-regularization
- Kernel multinomial logistic regression
- Density estimation with multivariate student T
- Data Visualization with Multi-Dimensional Scaling
- Regression with neural networks
- Classification with Neural Network with multiple hidden layers
- Smooth support vector machine
- Huberized support vector machine
- Smooth support vector regression
- Kernel smooth support vector machine
- Multi-class smooth support vector machine
- Extreme-value regression
- Sparse Gaussian graphical model precision matrix estimation
- Chain-structured conditional random field
- Tree-structured Markov random field with exp(linear) potentials
- Lattice-structured conditional random field
UGM
- example_UGM_QuickStart % A demo that shows how various parts of UGM work
- example_UGM_Small % Runs UGM on small graph where exact decoding/inference/sampling is possible
- example_UGM_Chain % Runs UGM for decoding/inference/sampling in chain-structured data
- example_UGM_Tree % Runs UGM for decoding/inference/sampling in tree-structured data
- example_UGM_Condition % Runs UGM for conditional decoding/inference/sampling
- example_UGM_Cutset % Runs cutset-conditioning in UGM for exact decoding/inference/sampling
- example_UGM_Junction % Runs junction tree in UGM for exact decoding/inference/sampling
- example_UGM_GraphCuts % Runs graph cuts in UGM for exact decoding in binary sub-modular models
- example_UGM_ICM % Runs local search methods in UGM for approximate decoding
- example_UGM_MCMC % Runs MCMC sampling methods in UGM for approximate sampling
- example_UGM_Variational % Runs variational methods in UGM for approximate decoding/inference/sampling
- example_UGM_Block % Runs block versions of approximate decoding/inference/sampling methods
- example_UGM_AlphaBeta % Alpha-beta swaps in UGM for approximate decoding in pairwise sub-modular models
- example_UGM_Linprog % Runs linear programming relaxation in UGM for approximate decoding
- example_UGM_TrainMRF % Shows how to train MRFs with UGM
- example_UGM_TrainCRF % Shows how to train CRFs with UGM
- example_UGM_TrainHidden % Shows how to train CRFs with missing labels
- example_UGM_OCR % Shows how to train CRFs where different examples have different structres
- example_UGM_TrainApprox % Approximate training of UGMs (pseudo-likelihood, mean field, loopy belief propagation)
- example_UGM_TrainApprox2 % Approximate training of UGMs (score matching, piecewise, min probability flow, product of marginals, block pseudolikelihood, sub-modular constraints)
- example_UGM_TrainSGD % Stochastic gradient training of UGMs
- example_UGM_TrainSGD2 % Stochastic gradient training of UGMs (projected, max-margin, contrastive divergence, stochastic maximum likelihood)
L1GeneralGroup
- example_L1GeneralGroup % Computes regularization paths with L1 and group-L1 regularization using SPG
- example_L1GeneralGroupPath % Regularization path for group Lasso with iterative soft-thresholding
UGMlearn
- example_UGMlearn % Runs different approaches to structure learning in conditional random fields
crfChain
- example_crfChain % Training a chain-structured conditional random field with categorical features
L1General_examples.m
- LASSO
- Elastic Net
- Logistic Regression
- Logistic Regression (larger number of variables)
- Lasso Regularization Path
- Huber Robust Regression Regularization Path
- Logistic Regression Regularization Path
- Probit Regression, Smooth SVM, Huberized SVM
- Non-Parametric Logistic Regression with Sparse Prototypes
- Multinomial Logistic Regression
- Compressed Sensing
- Chain-structured conditional random field
- Graphical Lasso
- Markov Random Field Structure Learning
- Neural Network with Sparse Connections
- Deep Network with Sparse Connections
PQN_examples.m
- Linear Regression on the Simplex
- Lasso regression
- Lasso with Complex Variables
- Group-Sparse Linear Regression with Categorical Features
- Group-Sparse Simultaneous Regression
- Group-Sparse Multinomial Logistic Regression
- Group-Sparse Multi-Task Classification
- Low-Rank Multi-Task Classification
- L_1,inf Blockwise-Sparse Graphical Lasso
- L_1,2 Blockwise-Sparse Graphical Lasso
- Linear Regression with the Over-Lasso
- Kernelized dual form of support vector machines
- Smooth (Primal) Support Vector Machine with Multiple Kernel Learning
- Conditional Random Field Feature Selection
- Approximating node marginals in undirected graphical models with variational mean field
- Multi-State Markov Random Field Structure Learning
- Conditional Random Field Structure Learning with Pseudo-Likelihood
thesis
- demo_L1General % Runs various solvers for L1-regularized logistic regression
- demo_L1Generalpath % Computes L1-regularization path for logistic regression with warm-starting and an active set method
- demo_minConf % Runs various solvers for group L1-regularized multi-task logistic regression
- demo_minConfpath % Computes group L1-regularization paths for multi-task logistic regression with warm-starting and an active-set method
- demo_DAGlearnGOrdered % Runs various methods to estimate a Gaussian DAG given a topological ordering
- demo_DAGlearnG % Runs various methods to estimate a Gaussian DAG given interventional data
- demo_DAGlearn2Ordered % Runs various methods to estimate a sigmoid belief network given a topological ordering
- demo_DAGlearn2 % Runs various methods to estimate a sigmoid belief network given interventional data
- demo_UGMlearn % Trains pairwise log-linear models with different parameterizations and regularization types
- demo_UGMlearn2 % Computes regularization path for a pairwise log-linear model
- demo_UGMlearn3 % Train pairwise log-lienar models with different approximations and regularization types
- demo_LLM % Trains log-linear models with different order restrictions and regularization types
- demo_LLM2 % Computes regularization path for a hierarchical log-linear model
- demo_LLM3 % Compares parameterizations and regularization types for fitting hierarchical log-linear model
alphaBeta
- example_alphaBeta_UGMep % Compares various energy minimization methods on a stereo reconstruction task (using the UGMep codes)
- example_alphaBeta_UGM % Compares various energy minimization methods on a stereo reconstruction task (using the UGM codes)
batching
- example_batching % Compares different strategies for parameter estimation with a redundant training set under a severe function evaluation restriction
- example_batchingL2SVM % Compares different strategies for avoiding looking at all training examples on every iteration when training smooth support vector machines.
prettyPlot
- example_prettyPlot % Demo showing basic functionality of prettyPlot, including not placing a marker at all points.
- example_prettyPlot2 % Demo showing how to plot sequences of different lenghts, using repeating sequences of lineStyles/markers/colors, and having unspecified upper/lower limts.
- example_prettyPlot3 % Demo showing how to plot upper/lower error lines.
- example_prettyPlot4 % Demo showing how to directly label lines.
TMP_examples.m
- Non-negative Least Squares
- L1-Regularization Least Squares
- Logistic Regression with Bounded Coefficients
- Dual Support Vector Machines (no bias or regularized bias)
- Ordinal Logistic Regression
- Kernel Ordinal Logistic Regression
- Graphical LASSO
- Associative Conditional Random Fields (trained with pseudo-likelihood)
QNST_examples.m
- L1-Regularization
- Group L_{1,2}-Regularization
- Group L_{1,inf}-Regularization
- Combined L1- and Group L1-Regularization
- Nuclear norm-regularization
- Sparse plus low-rank
SAG
- example_SAG % Run all of the basic stochastic methods on a sparse data set.
- example_SAG2 % Run the BLAS version of several of the variants of the methods on a dense dat set.
Updates
September 2013:
- SAG: Added these functions implementing various stochastic methods for L2-regularized logistic regression.
- prettyPlot: Added the ability to place labels directly on lines instead in a legend box.
- blogreg: A bug in the sampleLambda.m function was fixed. Thanks to Sharon Chiang for pointing this out.
November 2012:
- UGM: Added score matching, piecewise training, block pseudo-likelihood, max-margin, contrastive divergence, and stochastic maximum likelihood.
- QNST: Added this series of demos showing how to use minConf_QNST to solve problems with non-smooth regularizers.
July, 2012:
- UGM: Added demos showing how to train CRFs with hidden variables, and how to train when different training examples have different graph structures.
- TMP examples: Added this series of demos showing how to use minConf_TMP to solve various bound-constrained problems.
April, 2012:
- minFunc: Added the fastDerivativeCheck function, which quickly checks the accuracy of a user-supplied gradient using only 2 or 3 function evaluations. This is preferable to the standard derivativeCheck function, which requires n+1 function evaluations for an n-dimensional problem. For an example, see example_derivativeCheck.
March, 2012:
- prettyPlot: Added this function that helps to make nicer looking plots. For example, it uses bigger lines/markers/fonts, does not require a marker at every point, allows specifying only a lower/upper limit, allows lines of different lengths, allows (separate) repeating sequences of colors/lineSyltes/markers, and plotting upper/lower error bars.
- minFunc: Re-wrote the core L-BFGS code to make it faster and more memory-efficient.
- UGM: Added example_UGM_QuickStart, a new demo that demonstrates how the different different data structures work and how to call the different functions.
- minFunc examples: Added the extra examples, including robust regression with the Huber and student t losses, density estimation with the multivariate t, data visualization with multi-dimensional scaling, Huberized support vector machines, and extreme-value regression.
- minFunc: Added the option of using central-differencing when using finite-difference derivatives.
- UGM: Fixed a bug in the junction tree sampler.
February, 2012:
- thesis: Added the canonical and spectral binary parameterizations described in the AI/Stats 2012 paper. These parameterizations can model any binary distribution, yet have no redundant parameters. See demo_LLM3.m.
January, 2012:
example_batchingL2SVM: Added this demo of different strategies for avoiding looking at all training examples on every iteration (without losing the exponential convergence rate) when training smooth support vector machines.
November, 2011:
- UGM: Updated the variant of UGM contained in this package to the 2011 version. This includes a variety of improvements and extensions detailed on the UGM webpage. The most important change in this version is a completely different way to set up parameter estimation, that allows arbitrary parameter tying schemes and makes it easier to have different graph structures for different training examples.
- Various: updated older packages to use the new version of UGM.
August, 2011:
- batching: Added this code for optimization with a large number of training examples, but where there is a large amount of redundancy in the training examples.
- alphaBeta: Added this code implementing alpha-expansion beta-shrink moves for MAP estimation in UGMs. Note that these functions use the mex wrapper to the maxflow code.
- thesis: Added the specialized code for log-linear MRFs (LLM2) and demos from my thesis of using group L1-regularization with different norms for structure learning in undirected graphical models. Also added the code for learning hierarchical log-linear models using overlapping group L1-regularization and a hierarchical search heuristic.
- Mex files: Removed the crfChain files from the mexAll command (since these don't compile on many systems). To compile these files, you now need to use mexCrfChain.
March 2011:
- demo_minConfpath: Added this demo of computing the simultaneous logistic
regression group L1-regularization
path for the group L2-norm and Linf-norm.
- DAGlearnG/DAGlearn2: Added these variants of the DAGlearn code from my thesis.
- Mex files: Added all files for 64-bit Athlon and Intel Mac architectures (mexa64 and maxmaci64). Thanks to Kevin Murphy for these.
- UGM_Decode_GraphCuts: Efficiency improvements, fixed graph construction in cases where the edge potentials are asymmetric (thanks to Mohamed Ghalwash), added ability to use Boykov and Kolmogorov's fast maxflow code via the mex interface available here (if the mex file is found on the path).
- UGM_Decode_AlphaExpansion: Added the truncation trick described here (Section 3.3) for approximate expansions in non-metric models.
Download
To get the complete set of most recent Matlab .m and .mex files, download code.zip.
Note that some of these packages use Matlab's optimization and/or statistics toolbox (they will say that functions like 'quadprog' or 'exprnd' are not found if these toolboxes are present). Also, several of the demos use graphViz for displaying graphs (it will say that 'neato' is not found if graphViz is not on the path). On architectures other than Windows, you can add graphViz to the Matlab path by using the function setGraphVizPath (this assumes the graphViz binaries are located in /usr/local/bin). To use the UGMep code (and substantially speed up the methods based on graph-cuts in UGM), you will need to have the mex wrapper to the maxflow code.
To add all folders in the zip file to the Matlab path, extract the contents of the zip file and then (in Matlab) type 'addpath(genpath(markSchmidt_dir))', where 'markSchmidt_dir' is the location of the extracted directory.
I have included compiled mex files for several operating systems, but if they are not included for your operating system then you will get an error that looks like: ??? Undefined function or method 'UGM_Decode_ExactC'. To compile most of the mex files for your operating system, run mexAll. This functions tries to compile all files except those associated with the crfChain code.
To compile the mex files associated with the crfChain code on Linux, run mexCrfChain. In order to compile the crfChain mex files on Windows, you can use MinGW and Gnumex. For compiling on Macs, you need to install Xcode.
Mark Schmidt > Software > Current Versions