Toolboxes

Machine Learning

A number of machine learning algorithms are provided. Every algorithm is optimized for both: memory consumption and execution speed. The range of algorithms spreads from supervised to unsupervised algorithms and each provides a convenient variable parameter list.

• Expectation Maximization (em) - estimate centers and covariance of n multivariate normal distributions according to the samples
• k Nearest Neighbors (knn) - searches k nearest neighbors for every input sample, handles several distances
• Ordinary Least Squares Regression (ridge_regression) - creates a model of the sample data which can be used as (biased) predictor than
• Kernel Ridge Regression (krr) - kernelized version of ridge regression, creates and applies the model with a number of different kernels
• Principal Component Analysis (pca) - finds orthogonal directions used to reduce the dimensionality of the data
• k Means Clustering (kmeansclust) - splits the data into a given number of clusters

Statistical Functions

The following common functions for statistical operations are included:

• cov - covariance matrix
• mean
• median
• mvnpdf - probability density function of a multivariate normal distribution
• mvnrnd - choose samples from a multivariate normal distribution
• rand - choose uniformly distributed samples
• randn - choose normal distributed samples
• randperm - permute integers randomly
• std - standard deviation
• var - variance
• select - select n-th smallest element
• nansum - sum ignoring nan values
• nanmean - mean ignoring nan values