Calculates stochastic results for alternative assignments, assignment-based preference relation and class cardinalities. The results are computed by sampling the space of compatible models.https://github.com/kciomek/rorutadisA list of criteria (<criteria> tag) with information about preference direction (<criteriaValues mcdaConcept="preferenceDirection">, 0 - gain, 1 - cost) and number of characteristic points (<criteriaValues mcdaConcept="numberOfCharacteristicPoints">, 0 for the most general marginal utility function or integer grater or equal to 2) of each criterion.
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]]>A list of alternatives.[...]
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]]>A list of categories (classes). List must be sorted from the worst category to the best.
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]]>The performances of the alternatives.A list of assignment examples of alternatives to intervals of categories (classes) or to a specific category (class).[...][...]
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]]>Two lists of assignment pairwise comparisons. A comparison from list with attribute mcdaConcept="atLeastAsGoodAs" indicates that some alternative should be assigned to class at least as good as class of some other alternative (k = 0) or at least better by k classes (k > 0). A comparison from list with attribute mcdaConcept="atMostAsGoodAs" indicates that some alternative should be assigned to class at most better by k classes (k > 0) then some other alternative. Note: usage of this kind of preference information significantly slows down computations.[...][...]k
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]]>A list of category (class) cardinality constraints. It allows to define minimal and/or maximal desired category (class) cardinalities. Note: usage of this kind of preference information significantly slows down computations.[...][...][...]
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]]>Method parameters.%1%2
]]>Whether marginal value functions strictly monotonic or not.falseNumber of samples.100