Examples with FilteredScalar nars.concept.scalar.FilteredScalar used on opensource projects

Example 1 with FilteredScalar

use of nars.concept.scalar.FilteredScalar in project narchy by automenta.

the class NAgent method starting.

/**
 * registers sensor, action, and reward concepts with the NAR
 * TODO call this in the constructor
 */
@Override
protected void starting(NAR nar) {
    synchronized (this) {
        Term id = (this.id == null) ? nar.self() : this.id;
        // Term happyTerm = id == null ?
        // $.the("happy") : //generally happy
        // $.p(id, $.the("happy"));
        // //$.inh(id, $.the("happy")); //happy in this environment
        // //$.prop(id, $.the("happy")); //happiness of this environment
        // FloatSupplier happyValue = new FloatCached(
        // new FloatNormalized(
        // //new FloatPolarNormalized(
        // //new FloatHighPass(
        // () -> reward
        // //)
        // ) {
        // @Override
        // public float asFloat() {
        // float f = super.asFloat();
        // if (f!=f) return Float.NaN;
        // else {
        // f = Util.unitize(f);
        // 
        // //assert(f >= 0 && f <= 1f);
        // 
        // //depression curve and offset
        // return Util.max(0,f - depress.floatValue());
        // }
        // }
        // //                        @Override
        // //                        public float min() {
        // //                            return Util.lerp(depress.floatValue(), super.max(), super.min());
        // //                        }
        // //
        // //                        @Override
        // //                        public float max() {
        // //                            //decrease the max toward min in proportion to the depression setting
        // //                            return Util.lerp(depress.floatValue(), super.max(), super.min());
        // //                        }
        // }.relax(Param.HAPPINESS_RE_SENSITIZATION_RATE),
        // nar::time);
        FloatSupplier happyValue = new FloatCached(() -> reward - depress.floatValue(), nar::time);
        this.happy = // new ActionInfluencingScalar(happyTerm, happyValue);
        new FilteredScalar(happyValue, nar, // happiness (raw value)
        pair($.inh($.p("happy", "raw"), id), new FloatNormalizer().relax(Param.HAPPINESS_RE_SENSITIZATION_RATE)), // long-term happiness: chronic / satisfaction
        pair($.inh($.p("happy", "chronic"), id), compose(new FloatNormalizer().relax(Param.HAPPINESS_RE_SENSITIZATION_RATE), new FloatExpMovingAverage(0.02f))), // short-term happiness: acute / joy
        pair($.inh($.p("happy", "acute"), id), compose(new FloatExpMovingAverage(0.1f, false), new FloatPolarNormalizer().relax(Param.HAPPINESS_RE_SENSITIZATION_RATE_FAST))));
        happy.pri(() -> motivation.floatValue() * nar.priDefault(BELIEF));
        alwaysWant((Iterable) happy, nar.confDefault(GOAL));
        actions.keySet().forEach(a -> alwaysQuestion(Op.IMPL.the(happy.term, 1, /*XTERNAL*/
        a.term)));
        this.in = nar.newCauseChannel(this);
        this.now = nar.time();
        // head-start
        this.last = now - nar.dur();
        // finally:
        enabled.set(true);
    }
}