Take a set of rules (a rule-base) and perform a Perception-based Logical Deduction (PbLD) on
each row of a given
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Input to the inference. It should be
an object of class
Each row represents a single case of inference. Columns should be named after predicates in rules' antecedents.
A rule-base (a.k.a. linguistic description) either in the form
Crisp values that correspond to rows of memberhsip degrees in the
The type of inference to use. It can be either
Whether the processing should be run in parallel or not. Parallelization is
implemented using the
Perform a Perception-based Logical Deduction (PbLD) with given rule-base
rules on each
row of input
x. Columns of
x are truth values of predicates that appear in the
antecedent part of
partition together with
values determine the
shape of predicates in consequents: each element in
values corresponds to a row of
membership degrees in
A vector of inferred defuzzified values. The number of resulting values corresponds to the
number of rows of the
A. Dvo<c5><99><c3><a1>k, M. <c5><a0>t<c4><9b>pni<c4><8d>ka, On perception-based logical deduction and its variants, in: Proc. 16th World Congress of the International Fuzzy Systems Association and 9th Conference of the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT 2015), Advances in Intelligent Systems Research, Atlantic Press, Gijon, 2015.
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# --- TRAINING PART --- # split data into training and testing set testing <- CO2[1:5, ] training <- CO2[-1 * 1:5, ] # custom context of the RHS variable uptakeContext <- c(7, 28.3, 46) # convert training data into fuzzy sets d <- lcut3(training, context=list(uptake=uptakeContext)) # search for rules r <- searchrules(d, lhs=1:38, rhs=39:58) # --- TESTING PART --- # convert testing data info fuzzy sets x <- lcut3(testing, context=list(uptake=uptakeContext)) # prepare values and partition v <- slices(uptakeContext, uptakeContext, 1000) p <- lcut3(v, name='uptake', context=uptakeContext) # do the inference pbld(x, r, p, v)
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