Updating least squares
to deal with such data is taken from Collaborative Filtering for Implicit Feedback Datasets.Essentially, instead of trying to model the matrix of ratings directly, this approach treats the data as numbers representing the in observations of user actions (such as the number of clicks, or the cumulative duration someone spent viewing a movie).
Back to top I have received several requests for Fortran code to perform logistic regression, that is to fit: p = F/(1 F) where p = the probability that a case is in one of two categories F = exp(b0 b1. I have updated some of the Transactions on Mathematical Software (TOMS) algorithms to Fortran 90.
For linear regression, but when the regression coefficients must be positive or zero, there is the Lawson & Hanson non-negative least-squares routine nnls.f90. Quadruple precision gives about twice as many accurate digits as double precision and is much faster than MP. I have been asked to provide a link to the copyright policy of the ACM.
I am indebted to Keith Briggs (previouly at University of Cambridge) for access to his package in C for quadruple precision which helped improve the algorithm for calculating exponentials. Xk) X1, X2, ..., Xk is a set of k predictors, and b0, b1, b2, ..., bk is a set of coefficients to be fitted. Loosely paraphrased, this allows use, and modification, of the TOMS algorithms for most non-commercial purposes.
has a value that can either be varied in the fit or held at a fixed value, and can have upper and/or lower bounds placed on the value.
It can even have a value that is constrained by an algebraic expression of other Parameter values.