version 1.0.0.0 (109 KB) by
Mo Chen

Pattern Recognition and Machine Learning Toolbox

This package is a Matlab implementation of the algorithms described in the book: Pattern Recognition and Machine Learning by C. Bishop (PRML).

The repo for this package is located at: https://github.com/PRML/PRMLT

If you find a bug or have a feature request, please file issue there. I do not usually check the comment here.

The design goal of the code are as follows:

Succinct: Code is extremely terse. Minimizing the number of line of code is one of the primal target. As a result, the core of the algorithms can be easily spot.

Efficient: Many tricks for making Matlab scripts fast were applied (eg. vectorization and matrix factorization). Many functions are even comparable with C implementation. Usually, functions in this package are orders faster than Matlab builtin functions which provide the same functionality (eg. kmeans). If anyone found any Matlab implementation that is faster than mine, I am happy to further optimize.

Robust: Many numerical stability techniques are applied, such as probability computation in log scale to avoid numerical underflow and overflow, square root form update of symmetric matrix, etc.

Easy to learn: The code is heavily commented. Reference formulas in PRML book are indicated for corresponding code lines. Symbols are in sync with the book.

Practical: The package is designed not only to be easily read, but also to be easily used to facilitate ML research. Many functions in this package are already widely used (see Matlab file exchange).

Mo Chen (2020). Pattern Recognition and Machine Learning Toolbox (https://github.com/PRML/PRMLT), GitHub. Retrieved .

1.0.0.0 | update description |

Created with
R2016a

Compatible with any release

**Inspired:**
Variational Bayesian Linear Regression, Probabilistic Linear Regression, Variational Bayesian Relevance Vector Machine for Sparse Coding, Bayesian Compressive Sensing (sparse coding) and Relevance Vector Machine, Gram-Schmidt orthogonalization, Kalman Filter and Linear Dynamic System, Kernel Learning Toolbox, EM for Mixture of Bernoulli (Unsupervised Naive Bayes) for clustering binary data, Adaboost, Probabilistic PCA and Factor Analysis, Dirichlet Process Gaussian Mixture Model, Log Probability Density Function (PDF), Naive Bayes Classifier, Hidden Markov Model Toolbox (HMM), MLP Neural Network trained by backpropagation, Logistic Regression for Classification, Pairwise Distance Matrix, Kmeans Clustering, Kernel Kmeans, EM Algorithm for Gaussian Mixture Model (EM GMM), Kmedoids, Normalized Mutual Information, Variational Bayesian Inference for Gaussian Mixture Model, Information Theory Toolbox

Create scripts with code, output, and formatted text in a single executable document.

Georg SchAfter executing the kmedoid function on my data, how can I see the 2 medoids and the boundary values of the 2 cluster?

michioFidelis KipchumbaAlessandro Jurilei wangChapter 4.

Do there some functions lack sub-functions，such as softman and sigmod (lacking "logsumexp" and "log1pexp", respectively)

Rose LakatosOnly just diving deeper, but from someone coming from a non coding background this is a lifesaver. The book has great explanations and I'm already getting a better understanding of the code and how I can apply it to my research.

Mo Chen@zjyedword @MisterTellini, the MLP function has been rewritten, which matches the book better and includes bias.

Ansam alzubaidyi need rnn lstm code for any app but work ok

zjyedword zjyewordhello everyone, i don't understand the line "E = W{l}*dG;", after W{1} updating itself, why not excute E = W{l}*dG;? please explain it in detail, thanks

function [model, mse] = mlp(X, Y, h)

% Multilayer perceptron

% Input:

% X: d x n data matrix

% Y: p x n response matrix

% h: L x 1 vector specify number of hidden nodes in each layer l

% Ouput:

% model: model structure

% mse: mean square error

% Written by Mo Chen (sth4nth@gmail.com).

h = [size(X,1);h(:);size(Y,1)];

L = numel(h);

W = cell(L-1);

for l = 1:L-1

W{l} = randn(h(l),h(l+1));

end

Z = cell(L);

Z{1} = X;

eta = 1/size(X,2);

maxiter = 20000;

mse = zeros(1,maxiter);

for iter = 1:maxiter

% forward

for l = 2:L

Z{l} = sigmoid(W{l-1}'*Z{l-1});

end

% backward

E = Y-Z{L};

mse(iter) = mean(dot(E(:),E(:)));

for l = L-1:-1:1

df = Z{l+1}.*(1-Z{l+1});

dG = df.*E;

dW = Z{l}*dG';

W{l} = W{l}+eta*dW;

E = W{l}*dG;

end

end

mse = mse(1:iter);

model.W = W;

all codes is here:

https://www.mathworks.com/matlabcentral/fileexchange/55946-deep-multilayer-perceptron-neural-network-with-back-propagation

MisterTelliniShouldn't there be biases in the example from chapter 5?

Aleksey TrubyanovMisterTelliniI'm having some issues trying to implement the neural networks from chapter 5 for regression problems. More concretely, I am trying to implement those functions appearing in figure 5.3 from Bishop's book. Could anyone be so kind to lend me a hand? I would gladly appreciate it. Best regards, Aitor

Pann Thinzar SeintKarthick PAIt is very helpfull..Many thanks!

JorgeGood job, many thanks. How about a package for RL algorithms in Sutton Barto book (http://incompleteideas.net/book/bookdraft2018jan1.pdf)?

Victor Henrique Alves RibeiroMattia ChiniHenk-Jan RamakerGreat submission, thanks!

saida makhloufiMany thanks

ahmed silikchapter 1

this is my data for example i want to calculate the joint entropy but i cant please help me how

0.006304715

0.002032715

0.002948715

0.003558715

-0.000867286

0.000354715

0.005388715

0.004320715

-0.006969285

0.002948715

-0.000103286

-0.000103286

-0.009717285

-0.006665285

0.002184715

0.002490715

Marco Antonio Grivet Mattoso MaiaAlthough I've found quite instructing, the program hmm_demo.m from Chapter 13 does not work. It seems that the vilan is the normalized procedure.

sang moon KimTaotao Zhous wuahmed silikisequalf(Hx_y,Hxy-Hy) in this when try to run it said that there is error pl your comments

Faris NafiahDavid DuanQiong SongZikai LiVadim SmolyakovXU YIZHENzwang8gio locyusen zhangnice work, thanks. would you like to show us how to cite your work?

xin huangPabloChi-Funaushad warisCan you please provide the PDF of your book or just give the link for downloading the "Pattern Recognition and Machine Learning".

ramimjThank you for this work.

but why the classification results of rvmBinPred are reversed?

cityskyashkan abbasiYang SunDerry FitzgeraldThanks for clearing that up,

Derry

eanass abouzeidi am working using the hmm code, i understand that the emission matrix should be NxM

where N number of states and M number of symboles of the Observation, the HmmFilter used here uses another dimension for the Emission matrix it used Nxd where d is the length of the observation vector generated or used , can someone explain to me why?

Mo Chen@Derry Fitzgerald. The behavior is correct, the probability is the MAP probability of the who sequence. However the description is not right. I should have wrote p is single value.

Derry FitzgeraldHi, very nice toolbox, thanks!

I have noticed a bug in hmmViterbi_, it only outputs v as a single value instead of a vector of probabilities

SoobokChi-FumichioSyed Hasib Akhter FaruquiBin YangMinsu Kim