GP-PRED: Make predictions for test cases using Gaussian process model. GP-pred prints guesses at the target values for a set of test cases, as obtained using a Gaussian process, or set of Gaussian processes. If the true targets are known, performance of the guesses can also be evaluated. Inputs can be printed as well. Usage: gp-pred options { log-file range } [ / test-inputs [ test-targets ] ] The final optional arguments give the source of inputs and targets for the cases to look at; they default to the test data specification in the first log file given. The Gaussian processes to use in making guesses are taken from the records with the given ranges of indexes in the given log files. The outputs of all these are combined to give a single guess for each case. An index range can have one of the forms "[low][:[high]][%mod]" or "[low][:[high]]+num", or one of these forms preceded by "@". When "@" is present, "low" and "high" are given in terms of cpu time, otherwise they are iteration numbers. When just "low" is given, only that index is used. If the colon is included, but "high" is not, the range extends to the highest index in the log file. The "mod" form allows iterations to be selected whose numbers are multiples of "mod", with the default being "mod" of one. The "num" form allows the total number of iterations used to be specified; they are distributed as evenly as possible within the specified range. Note that it is possible that the number of Gaussian processes used in the end may not equal this number, if records with some indexes are missing. The 'options' argument consists of one or more of the following letters: i Display the input values for each case t Display the target values for each case r Use the raw form of the target values, before transformation p Display the log probability of the true targets m Display the guess based on the mode, and whether it is in error n Display the guess based on the mean, and its squared error d Display the guess based on the median, and its absolute error l Use stored latent values for predictions with a regression model (if available). Normally, predictions for regression models are done as if no latent values were stored, since this is better, but suppressing this behaviour might be of interest for testing purposes. (Stored latent values are essential for other models.) b Suppress headings and averages - just bare numbers for each case B Bare numbers, but with blank lines whenever first input changes a Display only average log probabilities and errors, suppressing the results for individual cases (makes sense only in combination with one or more of 'p', 'm', 'n', and 'd', and not with 'i' or 't') Some of these options are illegal for some data models. The illegal combinations are marked with an 'X' in the following table: binary class real-valued no-model r X X p X m X X n d X X f X X X Furthermore, the 'a' and 'b' options are incompatible, and the 't', 'p', and 'a' options may be used only if the true targets are given. The errors for individual cases are also displayed only if the true targets are known. The 'n' option for class models displays the mean probabilities for each class, and computes a single figure for squared error that is the sum of the squares of the differences between these probabilities and the indicator variables that are one for the right class and zero for the wrong classes. Making predictions requires inverting the covariance matrix of the training points for each Gaussian process used, which can take a substantial amount of time if the number of training points is large. This could be avoided if the inverse covariance matrix were saved in the log file for each iteration, but this could take up a very large amount of disk space. When the number of test cases is large, it will take much longer to compute the median or log probability predictions (options "d" or "p") for a regression model than to compute only the mean predictons (option "n"). For binary and class models, all sorts of predictions take about the same amount of time. The median is calculated by Monte Carlo, using a sample consisting of eleven points from the predictive distribution for each Gaussian process. A sample of one hundred points for each Gaussian process is used to calculate predictive probabilities for binary and class models. If the model has case-by-case noise variances, a single test-case variance is chosen randomly for each Gaussian process (this is a bit sub-optimal, as it would be better to pick a new variance for each of the eleven points used to compute the median, and to integrate the variance away to produce a t-distribution when computing the log predictive probability - but the programming was easier this way). In all these cases, the random number seed used in making predictions is 101*x+i, where x is the iteration number and i is the number of the test case. Each average performance figure is accompanied by +- its standard error (as long as there is more than one test case). If only inputs and targets are to be displayed (no predictions), one may give just a single log file with no range. Otherwise, at least one Gaussian process must be specified. Copyright (c) 1996 by Radford M. Neal