    A THREE-WAY CLASSIFICATION PROBLEM

    I also created a synthetic data set for a three-way classification
    problem.  Data items were generated by first drawing quantities x1,
    x2, x3, and x4 independently from distributions uniform over (0,1).
    The class of the item, represented by "0", "1", or "2", was then
    selected as follows: if the two-dimensional Euclidean distance of
    (x1,x2) from the point (0.4,0.5) was less than 0.35, the class was set
    to "0"; otherwise, if 0.8*x1+1.8*x2 was less than 0.6, the class was
    set to "1"; and if neither of these conditions held, the class was set
    to "2".  Note that x3 and x4 have no effect on the class.  The class
    selected by this procedure was the target value for the network; the
    inputs available for use in predicting the class were not x1, x2, x3,
    and x4 themselves, however, but rather these four values plus Gaussian
    noise of standard deviation 0.1.  I generated 1000 cases in this way,
    of which 400 were used for training and 600 for testing.

    A neural network model for the three-way classification problem.

    This example will illustrate the "softmax" model for target values in
    a small set, and the Automatic Relevance Determination (ARD) prior for
    input-to-hidden weights.  The network and model specifications used
    were as follows:

        > net-spec clog.net 4 8 3 / - x0.2:0.5:0.5 0.05:0.5 - \
                                    x0.05:0.5 - 0.05:0.5
        > model-spec clog.net class

    This specifies a network with 4 input units, 8 hidden units, and 3
    output units.  The output units have identity activation functions,
    but their values are used in a "softmax" data model, in which the
    probability for target i is exp(o_i) / SUM_j exp(o_j), where the o_j
    are the values of the output units.

    The prior specified for input-to-hidden weights, "x0.2:0.5:0.5", has
    two "alpha" values, indicating that there is both a high-level
    hyperparameter controlling the overall magnitude of input-to-hidden
    weights, and lower-level hyperparameters for each input unit, which
    control the magnitudes of weights out of each input.  If some of the
    inputs are irrelevant (as are the third and fourth inputs in this
    problem), we hope that the corresponding hyperparameters will take on
    small values, forcing the weights out of these inputs to be close to
    zero, and thereby avoiding any damage to predictive performance that
    could result from the inclusion of these irrelevant inputs. The prefix
    of "x" causes the width of this prior to be automatically scaled
    according to the number of inputs.  This is appropriate if the number
    of highly relevant inputs is thought not to depend on the total number
    of inputs.

    We need to use the following data specification for this problem:

        > data-spec clog.net 4 1 3 / cdata@1:400 . cdata@401:1000 .
        Number of training cases: 400
        Number of test cases: 600

    The arguments "4" and "1" are the numbers of input and target values.
    The "3" before the "/" says that each target must be one of the
    integers "0", "1", or "2".  This "3" must match the number of softmax
    output units specified in the network architecture, or an error will
    result when training is started.

    The initial and sampling phases of the simulation can be done using
    the same approach as was used above for the binary data set (but the
    "sample-noise" operation doesn't actually do anything here):

        > net-gen clog.net fix 0.5
        > mc-spec clog.net repeat 10 sample-noise heatbath hybrid 100:10 0.2
        > net-mc clog.net 1
        > mc-spec clog.net repeat 10 sample-sigmas heatbath 0.95 \
                                     hybrid 100:10 0.3 negate
        > net-mc clog.net 200

    This takes 8.7 minutes on the system used (see Ex-system.doc).

    We can see the effect of the ARD model by looking at the values of
    the low-level input-hidden hyperparameters over the course of the
    simulation, with the command:

        > net-plt t h1@ clog.net | plot

    The third and fourth hyperparameters are much smaller than the first
    two, indicating that ARD has done its job.

    Predictions for test cases based on the predictive mode can now be
    done in the same way as for the binary response problem.  If we are
    interested only in the estimated classification performance, we can
    use the following command:

        > net-pred ma clog.net 51:

        Number of networks used: 150

        Number of test cases: 600

        Fraction of guesses that were wrong:  0.1400+-0.0142

    Here, the "a" option is used to suppress everything except the summary.

    If we are interested in the probabilities for each class, we can use
    the "n" option, as follows:

        > net-pred tn clog.net 51:

        Number of iterations used: 150

        Case Targets   Means               Error^2

           1    2.00    0.19   0.00   0.81  0.0726
           2    2.00    0.23   0.01   0.76  0.1073
           3    2.00    0.12   0.00   0.87  0.0319
           4    0.00    0.82   0.04   0.13  0.0524

                           . . .

         597    2.00    0.04   0.00   0.96  0.0027
         598    1.00    0.33   0.65   0.01  0.2346
         599    0.00    0.23   0.00   0.77  1.1964
         600    2.00    0.03   0.00   0.97  0.0016

        Average squared error guessing mean:   0.20556+-0.01623

    The "Means" columns give the predictive probabilities for the three
    classes for each case.  The "Error^2" column gives the squared error
    found by summing the squared differences of these probabilities with
    the indicator variables that are zero for the wrong classes and one
    for the correct class.

    A Gaussian process model for the three-way classification problem.

    Gaussian process models can also be used for multi-way classification
    problems, in much the same way as for a binary response.  Here are
    commands that implement a "probit" style model for the three-way
    classification problem, using "persistent" hybrid Monte Carlo:

        > gp-spec clog.gpp 4 3 10 - 10 / 0.05:0.5 x0.2:0.5:1
        > model-spec clog.gpp class
        > data-spec clog.gpp 4 1 3 / cdata@1:400 . cdata@401:1000 .

        > gp-gen clog.gpp fix 0.5 1
        > mc-spec clog.gpp repeat 5 scan-values 100 heatbath 0.9 \
                                    hybrid 1 0.3 negate
        > gp-mc clog.gpp 5
        > mc-spec clog.gpp repeat 5 scan-values 100 heatbath 0.98 \
                                    hybrid 1 0.3 negate
        > gp-mc clog.gpp 100

    For the first five iterations, the persistence is fairly small (0.9),
    so that the high initial energy will be dissipated rapidly.  The
    remaining iterations use a larger persistence (0.98) to suppress
    random walks.  This run takes 19 minutes on the system used (see
    Ex-system.doc).  The cubic scaling of the computations for the
    Gaussian process models leads to their becoming slower relative to
    neural network models as the number of training cases increases.

    One can observe how the relevance hyperparameters change during the
    run as follows, assuming that the "plot" program can display four
    superimposed graphs:

        > gp-plt t R1@ clog.gpp | plot

    Once the run is finished, predictions for test cases can be made
    as illustrated below:

        > gp-pred itm clog.gpp 21:%5

        Number of iterations used: 16

        Case  Inputs                       Targets  Guesses  Wrong?

           1    0.29   0.90   0.74   0.33    2.00       2       0
           2    0.70   0.27   0.17   0.89    2.00       2       0
           3   -0.11   0.76   0.11  -0.02    2.00       2       0
           4    0.54   0.24   0.39   0.78    0.00       0       0

                          (middle lines omitted)

         597   -0.10   0.93   0.27   0.28    2.00       2       0
         598    0.14   0.19   0.08   0.70    1.00       1       0
         599    0.66   0.79   0.03   0.09    0.00       2       1
         600    0.76   0.90   0.73  -0.03    2.00       2       0

        Fraction of guesses that were wrong:  0.1350+-0.0140

    This takes 27 seconds on the system used (see Ex-system.doc).  The "n"
    option can be used to get the predictive probability for each class,
    as illustrated above for net-pred.

    A "logistic" style model can be obtained by decreasing the jitter,
    using a 'gp-spec' command such as the following:

        > gp-spec clog.gpl 4 3 1 - 0.1 / 0.05:0.5 x0.2:0.5:1

    A smaller stepsize is necessary with this model (0.15 rather than
    0.3).  Performance is similar.