GP-GEN: Generate GP hyperparameters / latent values randomly, or fix them. Gp-gen writes a series of independently-generated values for the hyperparameters of a Gaussian process model to a log file. The hyperparameters are drawn randomly from their prior, or set to fixed values. Optionally, latent values and/or individual noise variances for each training case are generated as well, from their priors. Usage: gp-gen [ -l ] [ -n ] log-file [ max-index ] [ "fix" [ sc-val rel-val ] ] Records of hyperparameters with indexes from zero up to the indicated index are generated (the default is max-index of zero). If the log file already contains records with some of these indexes, only records with indexes greater than the last existing record are generated. If just max-index is specified, the hyperparameters are generated randomly from the prior, using the random number seed taken from the log file (eg, as specified by rand-seed). The "fix" option is useful for initializing iterative programs. If it is given, the hyperparameters are not generated at random, but are instead set to fixed values. These values are the top-level widths for each hyperparameter if no values follow "fix". If values do follow "fix", they specify the scale and relevance hyperparameters of exponential parts of the covariance. The hyperparameters for the linear part are set to the product of these two values. The hyperparameter for the constant part is set to the scale value given. No data model is required to use gp-gen, but if a model is specified, the hyperparameters associated with it are generated as well. If the -l option is specified, latent values for each training case are also generated, randomly from the Gaussian process prior defined by the generated hyperparameters. If no jitter term is included in the covariance function, a small amount (1e-6) will be added to the diagonal of the covariance matrix when generating latent values to try to avoid numerical problems. If the -n option is specified, noise variances are generated for each training case, from their prior given the generated hyperparameter values. If the targets are not real valued, or the model does not have case-by-case noise variances, the -n option is silently ignored. Copyright (c) 1995-2004 by Radford M. Neal