Imagine a Person class with a boolean flag indicating whether or not the person is employable - set to false by default.
public class Person{
boolean employable = false;
...
}
Now imagine having some external boolean methods which act on Person objects. For example, consider static boolean methods in a utility class.
public class PersonUtil{
public static boolean ofWorkingAge(Person p){
if(p.getAge() > 16) return true;
return false;
}
...
}
Boolean static methods are in essence analogous to boolean valued functions i.e. predicates.
We can construct a 2^(#predicates)-by-#predicates truth table out of predicates. For example, given three predicates: ofWorkingAge, ofGoodCharacter, isQualified we can construct the following 8-by-3 truth table:
T T T
T T F
T F T
T F F
F T T
F T F
F F T
F F F
We now want to employ people with desirable qualities. Let + indicate that we wish to consider somebody employable (i.e. set their employability flag to true) and - the opposite.
T T T | +
T T F | +
T F T | +
T F F | -
F T T | +
F T F | -
F F T | -
F F F | -
Now imagine having a collection of Person objects. For each person we adjust their employability flag according to the three predicates. We also update a count (this forces us to use the entire truth table instead of just the positives), so that given 1,000 people we want to end up with something like:
T T T | + 100
T T F | + 200
T F T | + 50
T F F | - 450
F T T | + 50
F T F | - 50
F F T | - 50
F F F | - 50
Presumably this can be thought of as filtering with truth tables. Setting employability flags and updating counts is a rather contrived example but you can easily see how we might instead want to set and update much more complicated things.
QUESTION
Is there a way of elegantly doing this? I can think of two solutions:
Clunky solution
Have a giant hand coded if, else if, else chain.
if(ofWorkingAge && ofGoodCharacter && isQualified){
c1++;
p.setEmployable(true)
}
else if(ofWorkingAge && ofGoodCharacter && !isQualified){
c2++;
p.setEmployable(true)
}
...
else if(!ofWorkingAge && !ofGoodCharacter && isQualified){
c7++;
}
else{
c8++;
}
This is just bad.
Slightly smarter solution
Pass predicates (perhaps in an array) and a collection of sentences to a method. Let the method generate the corresponding truth table. Loop over the people, set their employability, and return an array of counts.
I can see how things could be done with functional interfaces. This SO answer is potentially relevant. You could change PrintCommand to IsQualified and pass callCommand a Person instead of a string. But this also seems kindah clunky because we'd then have to have a new interface file for every predicate we come up with.
Is there any other Java 8-ish way of doing this?
Let's start with the list of predicates you have:
List<Predicate<Person>> predicates = Arrays.<Predicate<Person>> asList(
PersonUtil::ofWorkingAge, PersonUtil::ofGoodCharacter,
PersonUtil::isQualified);
To track which predicate is true or false, let's attach names to them creating NamedPredicate
class:
public static class NamedPredicate<T> implements Predicate<T> {
final Predicate<T> predicate;
final String name;
public NamedPredicate(Predicate<T> predicate, String name) {
this.predicate = predicate;
this.name = name;
}
@Override
public String toString() {
return name;
}
@Override
public boolean test(T t) {
return predicate.test(t);
}
}
(one may attach BitSet
or something like this for efficiency, but String
names are also fine).
Now we need to generate a truth table which is a new list of predicates having names like "T T F"
and able to apply the given combination of source predicates, negated or not. This can be generated easily with a bit of functional programming magic:
Supplier<Stream<NamedPredicate<Person>>> truthTable
= predicates.stream() // start with plain predicates
.<Supplier<Stream<NamedPredicate<Person>>>>map(
// generate a supplier which creates a stream of
// true-predicate and false-predicate
p -> () -> Stream.of(
new NamedPredicate<>(p, "T"),
new NamedPredicate<>(p.negate(), "F")))
.reduce(
// reduce each pair of suppliers to the single supplier
// which produces a Cartesian product stream
(s1, s2) -> () -> s1.get().flatMap(np1 -> s2.get()
.map(np2 -> new NamedPredicate<>(np1.and(np2), np1+" "+np2))))
// no input predicates? Fine, produce empty stream then
.orElse(Stream::empty);
as truthTable
is a Supplier<Stream>
, you can reuse it as many times as you want. Also note that all the NamedPredicate
objects are generated on the fly by demand, we don't store them anywhere. Let's try to use this supplier:
truthTable.get().forEach(System.out::println);
The output is:
T T T
T T F
T F T
T F F
F T T
F T F
F F T
F F F
Now you can classify the persons
collection by the truth table, for example, in the following way:
Map<String,List<Person>> map = truthTable.get().collect(
Collectors.toMap(np -> np.toString(), // Key is string like "T T F"
// Value is the list of persons for which given combination is true
np -> persons.stream().filter(np).collect(Collectors.toList()),
// Merge function: actually should never happen;
// you may throw assertion error here instead
(a, b) -> a,
// Use LinkedHashMap to preserve an order
LinkedHashMap::new));
Now you can easily get the counts:
map.forEach((k, v) -> System.out.println(k+" | "+v.size()));
To update the employable
field we need to know how the desired truth table is specified. Let it be the collection of truth strings like this:
Collection<String> desired = Arrays.asList("T T T", "T T F", "T F T", "F T T");
In this case you may use the previously generated map:
desired.stream()
.flatMap(k -> map.get(k).stream())
.forEach(person -> person.setEmployable(true));
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