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This is a repository for storing useful machine learning code
This repository contains various code that is applicable to machine learning. I'm going to be doing some ML work now. So I'm going to be doing a lot of math, and a lot of ml fun. The Probability Model can create uniform, single and multivariable normal, categorical, and mixture model samples from distributions with a specified mean and (co)variance. the plotsample.py is a script that I wrote with plotting functions so that I could test my models by eye. I use the Box Muller transform and the Cholesky decomposition for normal samples. regression.py is the skeleton for the first assignment for UC Berkeley's 2011 machine learning course. It does k-local linear regression on a location, phase, station pair to predict the time residual of a seismic event. other code is a precomputation procedure to determine the k-local linear regression. It is too big for my computer to manage without being painfully slow, so now I think I'll have to use some other trick up my sleeve to try and work it out. util.py contains helper functions. K-nearest neighbors, k-fold cross validation, and some functions created to make a toy test for my cross-validation function, which predicted a plane nonlinear equation using nearest neighbors util.discrete_histogram(data, attr, *computationFn) takes a data file(csv) and discrete attributes(list), or one computed from those attributes and returns (value, absolute_frequency, relative_frequency) tuples. the computation function is the identity for one attribute if none other is supplied util.confusion_matrix(attr1, attr2) returns a matrix M indexed by the values of each attribute such that M_kl is the fraction of records that have Z = z_l out of all records having X = x_k. It also returns the column values and the row values in order of their matrix indicies. plotregression.py is a visual test for my cross validation functions.
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