SRC-PRED: Make predictions for measurements in test cases. Src-pred makes predictions for the target values in test cases, which are the concentration measurements at the locations/times specified by the inputs for the test cases. It can also compare these predictions with the true values, if these have been provided. Usage: src-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 source, flow, and noise parameters to use for the predictions are taken from the records with the given ranges of indexes in the given log files. Mean predictions are obtained by averaging the predictions from all these iterations. 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 iterations 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 p Display the log probability of the true targets (to base e) n Display the guess based on the mean, and its squared error d Display the guess based on the median, and its absolute error D Display the guess based on the mean of the median for each iteration. Not really useful for src-pred. q Display the 10% and 90% quantiles of the predictive distributions for the targets. Note that these distributions include the noise. Q Display the 1% and 99% quantiles of the predictive distributions. b Suppress headings and averages - just bare numbers for each case. The numbers are printed in exponential format, to high precision. 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', 'n', and 'd', and not with 'i' or 't') Tthe 'a' option is incompatible with 'b', 'i', 't', 'q', or 'Q', 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 median and quantiles are calculated by Monte Carlo, using a sample consisting of 101 points from the predictive distribution for each Gaussian process. A sample of 100 points for each Gaussian process is used to calculate predictive probabilities for binary and class models. If the noise variance is not fixed, test-case variance is chosen randomly for each iteration (this is a bit sub-optimal, as it would be better to pick a new variance for each of the 101 points used to compute the median - but the programming was easier this way). These Monte Carlo estimates are found using a random number stream initialized by setting the seed to one at the start of the program. Accuracy can be increased by repeating the same log-file/range combination several times, effectively increasing the sample size use. Furthermore, the 'a' option is incompatible with 'b', 'i', 't', or 'q', 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 median and quantiles are calculated by Monte Carlo, using a sample consisting of 101 points from the predictive distribution for each Gaussian process. A sample of 100 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 101 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). These Monte Carlo estimates are found using a random number stream initialized by setting the seed to one at the start of the program. Accuracy can be increased by repeating the same log-file/range combination several times, effectively increasing the sample size use. The 'D' option is implemented, but is of no real use, since models for survival analysis aren't supported, and individual medians for other models are the same as means. It's here only because of net-pred. 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 iteration must be specified. Note that src-pred always considers there to be four inputs (x, y, z, and t), even for steady-state models (where t is meaningless), and when fewer inputs are specified in data-spec. Copyright (c) 2007 by Radford M. Neal