主成分回归代码matlab及例子-machine-learning-r:R中的机器学习

# Machine Learning in R This is a repository for R scripts developed during my machine learning studies. Some of the codes have been adapted and converted into R from their original Matlab implementations. ## Classification ## - Euclidean (euclidean_classifier) - Mahalanobis (mahalanobis_classifier) - Perceptron (perceptron_classifier) - Online Perceptron (online_perceptron_classifier) - Sum-Squared Error (sse_classifier) ## Regression ## - plot data and (regression_plot) - plot regression decision boundary (regression_boundary) - generic regression wrapper function (regression_optimize) ### Linear regression ### - Linear regression cost function and gradient (lr_cost) - Linear regression gradient descent (lr_gradientdescent) ### Logistic regression ### - Logistic regression cost function and gradient (logr_cost) - Logistic regression optimizer (logr_optimize) - Predictions (logr_predict) ### Softmax regression ### - Softmax regression cost function and gradient (softmax_cost) - Softmax regression optimizer (softmax_optimize) - Predictions (softmax_predict) ### Softplus regression ### - Softplus regression cost function and gradient (softplus_cost) - Softplus regression optimizer (softplus_optimize) - Predictions (softplus_predict) ## Neural Network ## - sigmoid activation and derivative (nnet_sigmoid, nnet_dsigmoid) - softmax activation for multi-class classification/prediction (nnet_softmax) - forward and backward propagation (nnet_forward, nnet_backprop) - training via gradient descent (nnet_train) - training using stochastic gradient descent without regularization (nnet_stochastic) - multi-class classification/prediction (nnet_predict) - cost function for use with minimization algorithms (nnet_cost) - fast gradient descent computation (fmincg) - training using fast gradient descent computation (nnet_optimize) - training using R's optim function (nnet_minimize) ## Support Vector Machines ## - training, prediction (svm_train, svm_predict) - decision boundary visualization (svm_plot, svm_boundary) - linear, gaussian/rbf/erbf, polynomial kernels, sigmoid, fourier (svm_linear, svm_gaussian, svm_erbf, svm_polynomial, svm_sigmoid, svm_fourier) - sequential minimal optimization using various kernel functions (smo2, CalcKernel) - sequential minimal optimization boundary plotter (svcplot) ### Supported Kernels in SMO (CalcKernel) ### - linear - polynomial (poly) - radial basis function (rbf) - extended radial basis function (erbf) - sigmoid - fourier - spline - curved spline, ANOVA (curvedspline, anova) - B-spline (bspline) - ANOVA spline 1,2,3 (anovaspline1, anovaspline2, anovaspline3) - ANOVA B-spline (anovabspline) ## Features ## - normalize all features of X[m, n]: m-examples with n-features (featureNormalize) ## Principal Component Analysis ## - compute principal components (pca) - create projections of X (onto U) using K principal components (pca_project) - create approximations of X using K principal components (pca_estimate) ## Anomaly Detection ## - estimate Guassian distribution parameters: mean, variance (estimate_gaussian) - compute multivariate Gaussian probability distribution function (multivariate_gaussian) - determine best prediction threshold to use (select_threshold) ## Collaborative Filtering ## - compute cost function and gradients with Regularization (cf_costfunction ) - optimize using fast gradient descent optimizer fmincg (cf_optimize, cf_fmincg_cost) ## Clustering ## - basic sequential algorithmic scheme (BSAS) - generalized agglomerative scheme with single and complete linking (agglom) - run BSAS, GAS tests (test_clustering) ### k-means clustering ### - initialization of K-centroids (kmeans_initialize) - cluster assignment to nearest centroid (kmeans_assign) - compute new centroids from all points belonging to K-clusters (kmeans_compute) - run K-means algorithm for a maximum number of iterations (kmeans_run)