From ea5300da57dc2e8f1e3f003dc79abd4dcb7b6f27 Mon Sep 17 00:00:00 2001 From: Kkkakania <200867803+Kkkakania@users.noreply.github.com> Date: Mon, 1 Jun 2026 05:38:23 +0800 Subject: [PATCH] Fix README and Contents typos --- Contents.m | 28 ++++++++++++++-------------- README.md | 10 +++++----- 2 files changed, 19 insertions(+), 19 deletions(-) diff --git a/Contents.m b/Contents.m index bd96bba..94f4336 100644 --- a/Contents.m +++ b/Contents.m @@ -18,7 +18,7 @@ % CHAPTER03 % linReg - Fit linear regression model y=w'x+w0 % linRegFp - Fit empirical Bayesian linear model with Mackay fixed point method (p.168) -% linRegPred - Compute linear regression model reponse y = w'*X+w0 and likelihood +% linRegPred - Compute linear regression model response y = w'*X+w0 and likelihood % linRnd - Generate data from a linear model p(t|w,x)=G(w'x+w0,sigma), sigma=sqrt(1/beta) % CHAPTER04 % binPlot - Plot binary classification result for 2d data @@ -27,7 +27,7 @@ % logitBinPred - Prediction of binary logistic regression model % logitMn - Multinomial regression for multiclass problem (Multinomial likelihood) % logitMnPred - Prediction of multiclass (multinomial) logistic regression model -% sigmoid - Sigmod function +% sigmoid - Sigmoid function % softmax - Softmax function % CHAPTER05 % mlpClass - Train a multilayer perceptron neural network for classification with backpropagation @@ -39,7 +39,7 @@ % knCenter - Centerize the data in the kernel space % knGauss - Gaussian (RBF) kernel K = exp(-|x-y|/(2s)); % knKmeans - Perform kernel kmeans clustering. -% knKmeansPred - Prediction for kernel kmeans clusterng +% knKmeansPred - Prediction for kernel kmeans clustering % knLin - Linear kernel (inner product) % knPca - Kernel PCA % knPcaPred - Prediction for kernel PCA @@ -49,9 +49,9 @@ % sd2kn - Transform a squared distance matrix to a kernel matrix. % CHAPTER07 % rvmBinFp - Relevance Vector Machine (ARD sparse prior) for binary classification. -% rvmBinPred - Prodict the label for binary logistic regression model +% rvmBinPred - Predict the label for binary logistic regression model % rvmRegFp - Relevance Vector Machine (ARD sparse prior) for regression -% rvmRegPred - Compute RVM regression model reponse y = w'*X+w0 and likelihood +% rvmRegPred - Compute RVM regression model response y = w'*X+w0 and likelihood % rvmRegSeq - Sparse Bayesian Regression (RVM) using sequential algorithm % CHAPTER08 % MRF @@ -61,13 +61,13 @@ % mrfIsGa - Contruct a latent Ising MRF with Gaussian observation % mrfMf - Mean field for MRF % NaiveBayes -% nbBern - Naive bayes classifier with indepenet Bernoulli. +% nbBern - Naive bayes classifier with independent Bernoulli. % nbBernPred - Prediction of naive Bayes classifier with independent Bernoulli. -% nbGauss - Naive bayes classifier with indepenet Gaussian +% nbGauss - Naive bayes classifier with independent Gaussian % nbGaussPred - Prediction of naive Bayes classifier with independent Gaussian. % CHAPTER09 % kmeans - Perform kmeans clustering. -% kmeansPred - Prediction for kmeans clusterng +% kmeansPred - Prediction for kmeans clustering % kmeansRnd - Generate samples from a Gaussian mixture distribution with common variances (kmeans model). % kmedoids - Perform k-medoids clustering. % kseeds - Perform kmeans++ seeding @@ -76,7 +76,7 @@ % mixBernRnd - Generate samples from a Bernoulli mixture distribution. % mixGaussEm - Perform EM algorithm for fitting the Gaussian mixture model. % mixGaussPred - Predict label and responsibility for Gaussian mixture model. -% mixGaussRnd - Genarate samples form a Gaussian mixture model. +% mixGaussRnd - Generate samples from a Gaussian mixture model. % rvmBinEm - Relevance Vector Machine (ARD sparse prior) for binary classification. % rvmRegEm - Relevance Vector Machine (ARD sparse prior) for regression % CHAPTER10 @@ -94,21 +94,21 @@ % mixDpGb - Collapsed Gibbs sampling for Dirichlet process (infinite) mixture model. % mixDpGbOl - Online collapsed Gibbs sampling for Dirichlet process (infinite) mixture model. % mixGaussGb - Collapsed Gibbs sampling for Dirichlet process (infinite) Gaussian mixture model (a.k.a. DPGM). -% mixGaussSample - Genarate samples form a Gaussian mixture model with GaussianWishart prior. +% mixGaussSample - Generate samples from a Gaussian mixture model with GaussianWishart prior. % CHAPTER12 % fa - Perform EM algorithm for factor analysis model % pca - Principal component analysis % pcaEm - Perform EM-like algorithm for PCA (by Sam Roweis). % pcaEmC - Perform Constrained EM like algorithm for PCA. -% ppcaEm - Perform EM algorithm to maiximize likelihood of probabilistic PCA model. +% ppcaEm - Perform EM algorithm to maximize likelihood of probabilistic PCA model. % ppcaRnd - Generate data from probabilistic PCA model -% ppcaVb - Perform variatioanl Bayeisan inference for probabilistic PCA model. +% ppcaVb - Perform variational Bayesian inference for probabilistic PCA model. % CHAPTER13 % HMM % hmmEm - EM algorithm to fit the parameters of HMM model (a.k.a Baum-Welch algorithm) % hmmFilter - HMM forward filtering algorithm. % hmmRnd - Generate a data sequence from a hidden Markov model. -% hmmSmoother - HMM smoothing alogrithm (normalized forward-backward or normalized alpha-beta algorithm). +% hmmSmoother - HMM smoothing algorithm (normalized forward-backward or normalized alpha-beta algorithm). % hmmViterbi - Viterbi algorithm (calculated in log scale to improve numerical stability). % LDS % kalmanFilter - Kalman filter (forward algorithm for linear dynamic system) @@ -119,7 +119,7 @@ % CHAPTER14 % adaboostBin - Adaboost for binary classification (weak learner: kmeans) % adaboostBinPred - Prediction of binary Adaboost -% mixLinPred - Prediction function for mxiture of linear regression +% mixLinPred - Prediction function for mixture of linear regression % mixLinReg - Mixture of linear regression % mixLinRnd - Generate data from mixture of linear model % mixLogitBin - Mixture of logistic regression model for binary classification optimized by Newton-Raphson method diff --git a/README.md b/README.md index 9043b3c..e56f2bf 100644 --- a/README.md +++ b/README.md @@ -5,14 +5,14 @@ Pattern Recognition and Machine Learning by C. Bishop ([PRML](http://research.mi It is written purely in Matlab language. It is self-contained. There is no external dependency. -Note: this package requires Matlab **R2016b** or latter, since it utilizes a new Matlab syntax called [Implicit expansion](https://cn.mathworks.com/help/matlab/release-notes.html?rntext=implicit+expansion&startrelease=R2016b&endrelease=R2016b&groupby=release&sortby=descending) (a.k.a. broadcasting). It also requires Statistics Toolbox (for some simple random number generator) and Image Processing Toolbox (for reading image data). +Note: this package requires Matlab **R2016b** or later, since it utilizes a new Matlab syntax called [Implicit expansion](https://cn.mathworks.com/help/matlab/release-notes.html?rntext=implicit+expansion&startrelease=R2016b&endrelease=R2016b&groupby=release&sortby=descending) (a.k.a. broadcasting). It also requires Statistics Toolbox (for some simple random number generator) and Image Processing Toolbox (for reading image data). Design Goal ------- * Succinct: The code is extremely compact. Minimizing code length is a major goal. As a result, the core of the algorithms can be easily spotted. * Efficient: Many tricks for speeding up Matlab code are applied (e.g. vectorization, matrix factorization, etc.). Usually, functions in this package are orders faster than Matlab builtin ones (e.g. kmeans). -* Robust: Many tricks for numerical stability are applied, such as computing probability in logrithm domain, square root matrix update to enforce matrix symmetry\PD, etc. -* Readable: The code is heavily commented. Corresponding formulas in PRML are annoted. Symbols are in sync with the book. +* Robust: Many tricks for numerical stability are applied, such as computing probability in logarithm domain, square root matrix update to enforce matrix symmetry\PD, etc. +* Readable: The code is heavily commented. Corresponding formulas in PRML are annotated. Symbols are in sync with the book. * Practical: The package is not only readable, but also meant to be easily used and modified to facilitate ML research. Many functions in this package are already widely used (see [Matlab file exchange](http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A49739)). Installation @@ -25,9 +25,9 @@ git clone https://github.com/PRML/PRMLT.git 3. Run some demos in ~/PRMLT/demo folder. Enjoy! -FeedBack +Feedback ------- -If you find any bug or have any suggestion, please do file issues. I am graceful for any feedback and will do my best to improve this package. +If you find any bug or have any suggestion, please do file issues. I am grateful for any feedback and will do my best to improve this package. License -------