The DimmWitted high-speed sampler

deepdive.sampler.sampler_cmd: "/path/to/your/sampler gibbs"

-q, --quiet
    Quiet output

-c <int>,  --n_datacopy <int> (Linux only)
    Number of data copies.  One or more NUMA nodes can hold a copy of the
    factor graph and their CPU cores run the threads.  This argument
    specifies how many partitions the NUMA nodes should be grouped into.
    Default is to keep a copy of the factor graph in every NUMA node.

-t <int>,  --n_threads <int>
    Number of threads to use.  Defaults to zero (0) which uses all
    available threads.  The number of threads are equally divided and
    assigned to each data copy when --n_datacopy is greater than 1.

-w <weightsFile> | --weights <weightsFile>
    Weights file (required)
    It is a binary format file output by DeepDive.

-v <variablesFile> | --variables <variablesFile>
    Variables file (required)
    It is a binary format file output by DeepDive.

--domains <domainsFile>
    Categorical variable domains file (optional)
    It is a binary format file output by DeepDive.

-f <factorsFile> | --factors <factorsFile>
    Factors file (required)
    It is a binary format file output by DeepDive.

-m <metaFile> | --fg_meta <metaFile>
    Factor graph meta data file file (required)
    It is a text file containing factor graph meta information
    as well as paths to weight/variable/factor/edge files.

-o <outputFile> | --outputFile <outputFile>
    Output file path (required)

-i <numSamplesInference> | --n_inference_epoch <numSamplesInference>
    Number of iterations (epochs) during inference (required)

-l <learningNumIterations> | --n_learning_epoch <learningNumIterations>
    Number of iterations (epochs) during weight learning (required)

-a <learningRate> | --alpha <learningRate> | --stepsize <learningRate>
    The learning rate for gradient descent (default: 0.1)

-d <diminishRate> | --diminish <diminishRate>
    The diminish rate for learning (default: 0.95).
    Learning rate will shrink by this parameter after each iteration.

-b <regularizationParameter> | --reg_param <regularizationParameter>
    The l2 regularization parameter for learning (default: 0.01).

--sample_evidence
    Output probablities for evidence variables. Default is off, i.e., output
    only contains probabilities for non-evidence variables.

--learn_non_evidence
    Sample non-evidence variables during learning. Default if off. This option
    should be turned on if there exists a factor connecting evidence and non-evidence
    variables.