🐦swift auxiliary module SkillHubAlgDispencer

[fukurou]

auxiliary module SkillHubAlgDispencer · yotamarker/public-livinGrimoire Wiki

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class SkillHubAlgDispenser {
    /// super class to output an algorithm out of a selection of skills
    ///  engage the hub with dispenseAlg and return the value to the containing the outAlg attribute
    ///  of the containing skill (which houses the skill hub)
    ///  this module enables using a selection of 1 skill for triggers instead of having the triggers engage on multible skill
    ///   the methode is ideal for learnability and behavioral modifications
    ///   use a learnability auxiliary module as a condition to run an active skill shuffle or change methode
    ///   (rndAlg , cycleAlg)
    ///   moods can be used for specific cases to change behavior of the AGI, for example low energy state
    ///   for that use (moodAlg)
    var skills:Array<DiSkillV2> = [DiSkillV2]()
    var activeSkill:Int = 0
    init(_skills:DiSkillV2...){
        for skill in _skills {
            self.skills.append(skill)
        }
    }
    func addSkill(skill:DiSkillV2) -> SkillHubAlgDispenser {
        self.skills.append(skill)
        return self
    }
    func dispenseAlgorithm(ear:String, skin:String, eye:String) -> Algorithm? {
        skills[activeSkill].input(ear: ear, skin: skin, eye: eye)
        return skills[activeSkill].outAlg
    }
    func rndAlg(){
        activeSkill = Int.random(in: 0..<skills.count)
    }
    func moodAlg (mood:Int){
        let c1:Int = skills.count
        if -1<mood && mood<c1 {
            activeSkill = mood
        }
    }
    func cycleAlg(){
        activeSkill += 1
        if activeSkill == skills.count {activeSkill = 0}
    }
}

super class to output an algorithm out of a selection of skills engage the hub with dispenseAlg(method) and, return the value to the containing the outAlg attribute of the containing skill (which houses the skill hub)

this module enables using a selection of 1 skill for triggers instead of, having the triggers engage on multiple skills.

the method is ideal for learnability and behavioral modifications :
use a learnability auxiliary module as a condition to run an active skill shuffle or change method (rndAlg , cycleAlg)

moods can be used for specific cases to change behavior of the AGI, for example low energy state for that use the moodAlg method