Included gomill framework for SGF and GTP support, and sketched out SGF game-loading code.
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gomill/examples/clop_example.ctl
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gomill/examples/clop_example.ctl
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# The following settings are supported:
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#
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# - all _common settings_
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#
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# - all _game settings_
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#
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# - tuning event settings (cf mcts_tuner):
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# - candidate_colour
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# - opponent
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# - parameters
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# - make_candidate
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#
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# - settings for experiment control
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# - parallel -- number of games to run in parallel
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# - stop_on_error -- boolean
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#
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# - regression parameters:
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# - clop_H -- float
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# - correlations -- 'all' (default) or 'none'
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#
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## <<
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# clop_H: 3 is recommended (it is the default value)
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# correlations:
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# Even if variables are not correlated "all" should work well. The problem is
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# that the regression might become very costly if the number of variables is
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# high. So use "correlations none" only if you are certain parameters are
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# independent or you have so many variables that "all" is too costly.
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## >>
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#
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# The available parameter types are:
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# LinearParameter
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# IntegerParameter
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# GammaParameter
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# IntegerGammaParameter
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# For GammaParameter, quadratic regression is performed on log(x)
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competition_type = "clop_tuner"
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description = """\
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Sample control file for CLOP integration.
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"""
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def gnugo(level):
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return Player("gnugo --mode=gtp --chinese-rules --capture-all-dead "
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"--level=%d" % level)
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def pachi(playouts, policy):
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return Player(
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"~/src/pachi/pachi "
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"-d 0 " # silence stderr
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"-t =%d "
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"threads=1,max_tree_size=2048 "
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"policy=%s "
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% (playouts, policy))
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players = {
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'gnugo-l7' : gnugo(7),
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}
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parameters = [
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Parameter('equiv_rave',
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type = "GammaParameter",
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min = 40,
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max = 32000),
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]
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def make_candidate(equiv_rave):
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return pachi(2000, policy="ucb1amaf:equiv_rave=%f" % equiv_rave)
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board_size = 19
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komi = 7.5
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opponent = 'gnugo-l7'
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candidate_colour = 'w'
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parallel = 2
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