    MODEL-SPEC:  Specify model to use for target variables.

    Model-spec writes a specification to an existing log file for how the
    distribution of targets in a case is defined in terms of the function
    computed from the inputs for the case.  How this function of the
    inputs is found is specified separately (it might, for instance, be
    computed by a neural network).

    Models are available for regression with real-valued targets, logistic
    regression with binary targets, generalized logistic regression with
    targets taking on a finite set of values, and survival analysis with a
    hazard function that may depend on various covariates, and on time.

    Usage:

         model-spec log-file model-specifications...

    or:

         model-spec log-file

    The second form displays the existing specification stored in the log
    file, if any.

    The first form appends a specification to the log file, which must not
    contain any iterations yet.  If model-spec is invoked more than once,
    the last specification is used.  The model is described by one or more
    arguments after the log file argument.

    Regression for real-valued targets.

    Specification:

       real noise-prior [ "acf" corr { corr } ]

    A regression model is specified by the argument "real", followed by a
    noise prior (see prior.doc).  With this model, the data for each case
    consists of the same number of input values as the network has input
    units, and the same number of target values as the network has output
    units.  The network outputs give the means of independent Gaussian
    distributions, with variances determined by the noise hyperparameters.
    (Note, however, that if there are implicit case-by-case
    hyperparameters, the noise effectively comes from a t-distribution,
    rather than a Gaussian.)

    The noise prior may be followed by a specification of autocorrelations
    for the noise in training cases, consisting of "acf" followed by one
    or more autocorrelations, for lags from one on up; autocorrelations at
    higher lags are assumed to be zero.  These autocorrelations must be
    valid - ie, they must result in a positive definite correlation
    matrix.  Note that this option causes the noise to be treated as
    autocorrelated only for training cases.  The noise in test cases is
    considered to be independent of the noise in the training cases, and
    of the noise in other test cases.

    If the "acf" option is used, the noise prior must not specify that the
    noise variance varies from case to case.  Autocorrelated noise is
    currently implemented only for Gaussian process models.

    Logistic regression for binary targets.

    A logistic regression model for binary data is specified by the
    argument "binary".  With this model, the data for each case consists
    of the same number of inputs values as the network has input units,
    and the same number of binary target values as the network has output
    units.  The targets must be specified to be binary using the
    int-target option of data-spec.  Each output, o, of the network is
    used to derive the probability, p, that the corresponding target will
    be '1', via the logistic function, p = 1/(1+exp(-o)).

    Generalized logistic regression for class targets ("softmax").

    The "class" data model is used to model target values representing
    some finite number of "classes".  As with the "real" and "binary"
    models, the number of input units must match the number of input
    values in each case.  There must be only one target in each case, with
    the number of possible values for this target (specified with the
    int-target option of data-spec) corresponding to the number of output
    units in the network.  The distribution over target values for a case
    is found by letting the probability of a particular target value be
    proportional to the exponential of the value of the corresponding
    output.

    Survival data.

    The data-model arguments to net-spec can also specify that the net is
    to be used to model survival data, indicated by a first argument of
    "survival".  With a survival model, the inputs to the networks will be
    the values of various covariates that may affect survival, plus
    perhaps the time.  The net must have a single output, which is used to
    construct the log of the hazard function.  The form of the hazard
    function is specified by the arguments following "survival", in one of
    the following ways:

        const-hazard

           The hazard function is constant through time, though it may depend
           on the covariates.  Only the covariates are provided as inputs to
           the network.

        pw-const-hazard  [ "log" ] { time-point }

           The hazard function is piecewise constant, with changes in value
           at the time points listed.  There must be at least two time points,
           they must be greater than zero, and they must be increasing.  The
           log of the hazard for times before the first time point is obtained
           from the network output with the first network input set to the first
           time point (or the log of this time, if "log" is specified), and with
           any other inputs set to the covariates.  Similarly, the log of the
           hazard after the last time point is obtained by setting the first
           input to the last time point (or its log).  The log of the hazard at
           times between two time points is obtained by setting the first input
           to the average of the two time points (or the average of the logs of
           the two time points if "log" is specified).

    The data specification for a survival model must give the number of
    covariates as the number of inputs, and must specify that there is a
    single target.  Negative values of the target are interpreted as
    censored values (with observation stopping at the time given by the
    target's absolute value).

    Survival models are not implemented for Gaussian processes.

                Copyright (c) 1995-2003 by Radford M. Neal