DexiTabularFunction-class: DexiTabularFunction

Description

DexiTabularFunction is a RC class, derived from DexiFunction. Functions of this type aggregate attribute values according to decision rules, defined in terms of a decision table.

Arguments

Fields

attribute: DexiAttribute. The attribute this function is associated with. Both the attribute and its inputs are required to be discrete (i.e., associated with DexiDiscreteScales).

values

A multi-dimensional list of rule values. The dimensions of the list are equal to attribute$dim(), and the length of the list is nvals() == prod(dim). The list contains rule values that are in general value sets, i.e., integer vectors of value indices w.r.t. attribute$scale.

args

A list of integer vectors, containing all possible combinations of values of attribute$inputs. args and values are of the same length and ordered so that, for each i, args[[i]] defines function arguments that map to values[[i]]).

Methods

evaluate(x): A silent wrapper around value(x); it returns NULL when value(x) fails with an error.

nargs()

Return the number of function arguments.

nvals()

Return the function size (number of rules).

to_string()

Return a short informative string about the size and dimensions of values.

value(x)

Return the function value for arguments x, where arguments are a numeric vector of length equal to att$inputs. Additionally, arguments of a DexiTabularFunctions$value() must be integer numbers, and the argument of DexiDiscretizeFunctions$value() must be a single number.

verify()

Check the correctnes of this function object and its fields. Result: error() or TRUE.

Details

A decision table contains as many decision rules as there are possible combinations of input attributes' values. For instance, if some attribute has two inputs whose discrete scales have three and four values, respectively (i.e., attribute$dim() == c(3,4)), then the number of rules is equal to prod(attribute$dim()) == 12. Each rule defines the value of attribute for one of the possible combinations of values of attribute$inputs. Thus, a decision table can be interpreted as a lookup table that, given a vector of values of attribute$inputs (i.e., function arguments) returns the corresponding attribute value.

Objects of class DexiTabularFunction store decision rules in values, a multi-dimensional list that contains rule values. In most cases, a rule value is a single integer, representing an ordinal number of some value from attribute$scale. In a general case, however, a rule value can be an integer vector, representing a (sub)set of values from attribute$scale.

Examples

Run this code

# Load "Car.dxi"
CarDxi <- system.file("extdata", "Car.dxi", package = "DEXiR")
Car <- read_dexi(CarDxi)

# For example, consider the function of attribute CAR
fnc <- Car$attrib("CAR")$funct

# Print fields and basic properties of fnc
fnc$verify()
att_names(fnc$attribute)
fnc$values
fnc$args
fnc$nargs()
fnc$nvals()
fnc$to_string()

# Try some args to value mappings
fnc$evaluate(c(1, 1))
fnc$evaluate(c(2, 2))
fnc$evaluate(c(3, 4))
fnc$evaluate(c(4, 4)) # the first argument is out of bounds, returns NULL