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package org.singinst.uf.model;
import java.util.ArrayList;
import java.util.List;
import java.util.TreeSet;
import org.singinst.uf.math.MathUtil;
public class RescheduledEventsRelation extends CalculationRelation {
/*@Override
protected Calculation getCalculationForYear(double year) {
// TODO Auto-generated method stub
return null;
}*/
public RescheduledEventsRelation(Node node) {
super(node);
}
protected Calculation probabilityAiIsPossible() {
return new Calculation("Probability that AGI is possible") {
@Override
protected double rawEvaluate(StringBuilder htmlConsole) {
MathUtil.ScienceSpeedModelParameters scienceSpeedModelParameters = scienceSpeedModel();
org.singinst.uf.common.LogUtil.info(String.format(
"institutional_base_year: %g\n"
+ "institutional_mean_slope_log_factor: %g\n"
+ "institutional_stddev_slope_log_factor: %g\n"
+ "population_base_year: %g\n"
+ "population_mean_log_year: %g\n"
+ "population_stddev_log_year: %g\n"
+ "population_mean_init_log_rate: %g\n"
+ "population_stddev_init_log_rate: %g\n"
+ "population_mean_slope_log_rate: %g\n"
+ "population_stddev_slope_log_rate: %g\n"
+ "sequencedata_base_year: %g\n"
+ "sequencedata_mean_log_year: %g\n"
+ "sequencedata_stddev_log_year: %g\n"
+ "sequencedata_mean_init_log_factor: %g\n"
+ "sequencedata_stddev_init_log_factor: %g\n"
+ "sequencedata_mean_slope_log_factor: %g\n"
+ "sequencedata_stddev_slope_log_factor: %g\n"
, scienceSpeedModelParameters.institutional_base_year
, scienceSpeedModelParameters.institutional_mean_slope_log_factor
, scienceSpeedModelParameters.institutional_stddev_slope_log_factor
, scienceSpeedModelParameters.population_base_year
, scienceSpeedModelParameters.population_mean_log_year
, scienceSpeedModelParameters.population_stddev_log_year
, scienceSpeedModelParameters.population_mean_init_log_rate
, scienceSpeedModelParameters.population_stddev_init_log_rate
, scienceSpeedModelParameters.population_mean_slope_log_rate
, scienceSpeedModelParameters.population_stddev_slope_log_rate
, scienceSpeedModelParameters.sequencedata_base_year
, scienceSpeedModelParameters.sequencedata_mean_log_year
, scienceSpeedModelParameters.sequencedata_stddev_log_year
, scienceSpeedModelParameters.sequencedata_mean_init_log_factor
, scienceSpeedModelParameters.sequencedata_stddev_init_log_factor
, scienceSpeedModelParameters.sequencedata_mean_slope_log_factor
, scienceSpeedModelParameters.sequencedata_stddev_slope_log_factor
));
return getDependency().value(NodeIDString.Q1_1, ScalarSubIDString.PROBABILITY) / 100;
}
};
}
MathUtil.EventDiscreteDistributionSchedule rescheduledProbabilities;
@Override
protected List<Calculation> getCalculations(final List<Double> years) {
List<Calculation> retVal = new ArrayList<Calculation>();
for (final double year : years) {
retVal.add(new Calculation("Probability of AI by " + (int) year) {
@Override
protected double rawEvaluate(StringBuilder htmlConsole) {
if (year == ModelUtil.BASIC_MODEL_YEARS[0]) {
calculate(MathUtil.wrapDoubleArray(ModelUtil.BASIC_MODEL_YEARS));
}
return MathUtil.expc(rescheduledProbabilities.logitnerp(year).clogProbEvent);
}
});
}
return retVal;
}
private void calculate(List<Double> years) {
double[] yearsInOtherCalculations = ModelUtil.BASIC_MODEL_YEARS;
double[] yearsHere = MathUtil.unwrapDoubleList(years);
//new double[years.size()];
//for (int i=0; i<yearsHere.length; ++i)
// yearsHere[i] = years.get(i);
TreeSet<Double> beforeYearsSortedSet = new TreeSet<Double>(MathUtil.wrapDoubleArray(yearsInOtherCalculations));
{
double i = ModelUtil.LATEST_YEAR - ModelUtil.EARLIEST_IA_YEAR;
double factor =
(yearsInOtherCalculations[yearsInOtherCalculations.length-1] - ModelUtil.EARLIEST_IA_YEAR)
/ (yearsInOtherCalculations[yearsInOtherCalculations.length-2] - ModelUtil.EARLIEST_IA_YEAR)
;
i *= factor;
while (i <= ModelUtil.LATEST_UNACCELERATED_YEAR - ModelUtil.EARLIEST_IA_YEAR) {
beforeYearsSortedSet.add(Math.round(i/ModelUtil.YEAR_STEPSIZE)*ModelUtil.YEAR_STEPSIZE + ModelUtil.EARLIEST_IA_YEAR);
i *= factor;
};
i = ModelUtil.LATEST_YEAR - ModelUtil.EARLIEST_IA_YEAR;
i /= factor;
while (i <= ModelUtil.EARLIEST_YEAR - ModelUtil.EARLIEST_IA_YEAR) {
beforeYearsSortedSet.add(Math.round(i/ModelUtil.YEAR_STEPSIZE)*ModelUtil.YEAR_STEPSIZE + ModelUtil.EARLIEST_IA_YEAR);
i /= factor;
};
}
double[] beforeYears = MathUtil.unwrapDoubleList(new ArrayList<Double>(beforeYearsSortedSet));
//List<Double> clogPAiWrapped;
double[] clogPAi;
//clogPAi = MathUtil.unwrapDoubleList(clogPAiWrapped);
clogPAi = new double[beforeYears.length];
for (int i=0; i<clogPAi.length; ++i) {
// clogPAI[i] = 100;
clogPAi[i] = MathUtil.clog(ScalarValueHolder.findById(NodeIDString.C1_6, ScalarSubIDString.yearProbabilityID(beforeYears[i])).getValue());
if (clogPAi[i]>300d) clogPAi[i]=300d;
}
MathUtil.EventDiscreteDistributionSchedule s_before = new MathUtil.EventDiscreteDistributionSchedule();
MathUtil.EventDiscreteDistributionSchedule s_after;
s_before.time = beforeYears; //ModelUtil.BASIC_MODEL_YEARS;
//s_before.time = java.util.Arrays.copyOf(ModelUtil.BASIC_MODEL_YEARS, yearsInOtherCalculations.length+2);
//s_before.time[yearsInOtherCalculations.length] = ModelUtil.LATEST_YEAR*(1+2*MathUtil.DOUBLE_EPS);
//s_before.time[yearsInOtherCalculations.length+1] = ModelUtil.LATEST_YEAR*(1+4*MathUtil.DOUBLE_EPS);
s_before.clogProbEvent = clogPAi;
//s_before.clogProbEvent = java.util.Arrays.copyOf(clogPAI, yearsInOtherCalculations.length+2);
s_before.logitSubevents = new double[][]{};
MathUtil.ScienceSpeedModelParameters scienceSpeedModelParameters = scienceSpeedModel();
s_after = MathUtil.clogMarginalScienceSpeedEventRescheduling(
scienceSpeedModelParameters,
s_before, yearsHere, 1000);
rescheduledProbabilities = s_after;
//List<Double> rescheduledProbabilities = new java.util.ArrayList<Double>(years.size());
//for (int i=0; i<rescheduledProbabilities.size(); ++i) {
// rescheduledProbabilities.set(i, MathUtil.expc(s_after.clogProbEvent[i]));
//}
/*
yearsInOtherCalculations = new double[ModelUtil.]*/
//return rescheduledProbabilities;
}
// glue equations
private MathUtil.ScienceSpeedModelParameters scienceSpeedModel() {
MathUtil.ScienceSpeedModelParameters o = new MathUtil.ScienceSpeedModelParameters();
o.sequencedata_base_year = ModelUtil.ANCHOR_FAR_YEAR + ModelUtil.RESEARCH_CAREER_DELAY;
o.sequencedata_mean_log_year = getDependency().value(NodeIDString.Q1_9_1, ScalarSubIDString.MEAN)*Math.log(10);
o.sequencedata_stddev_log_year = getDependency().value(NodeIDString.Q1_9_1, ScalarSubIDString.STD_DEV)*Math.log(10);
o.population_base_year = ModelUtil.ANCHOR_FAR_YEAR + ModelUtil.RESEARCH_CAREER_DELAY;
o.population_mean_log_year = getDependency().value(NodeIDString.Q1_9_2, ScalarSubIDString.MEAN)*Math.log(10);
o.population_stddev_log_year = getDependency().value(NodeIDString.Q1_9_2, ScalarSubIDString.STD_DEV)*Math.log(10);
double t0, t1;
t0 = 0; // EARLIEST_IA_DELAY
t1 = ModelUtil.LATEST_IA_DELAY;
double klow, khigh;
klow = NotablePercentile.PERCENTILE5.getValue();
khigh = NotablePercentile.PERCENTILE95.getValue();
double zlow, zhigh;
zlow = NotablePercentile.PERCENTILE5.getOffset();
zhigh = NotablePercentile.PERCENTILE95.getOffset();
double y0low, y0high, y1low, y1high;
double mean_init, stddev_init, mean_slope, stddev_slope, mean_final, stddev_final;
//y0low = getDependency().value(NodeIDString.Q1_9_3, ScalarSubIDString.yearPercentileID(t0, klow ))*Math.log(10);
//y0high = getDependency().value(NodeIDString.Q1_9_3, ScalarSubIDString.yearPercentileID(t0, khigh))*Math.log(10);
y0low = 0;
y0high = 0;
y1low = getDependency().value(NodeIDString.Q1_9_3, ScalarSubIDString.yearPercentileID(t1, klow ))*Math.log(10);
y1high = getDependency().value(NodeIDString.Q1_9_3, ScalarSubIDString.yearPercentileID(t1, khigh))*Math.log(10);
mean_init = MathUtil.linterp(zlow, zhigh, y0low, y0high, 0);
stddev_init = (y0high-y0low)/(zhigh-zlow);
mean_final = MathUtil.linterp(zlow, zhigh, y1low, y1high, 0);
stddev_final = (y1high-y1low)/(zhigh-zlow);
mean_slope = (mean_final-mean_init)/(t1-t0);
stddev_slope = (stddev_final-stddev_init)/(t1-t0);
o.sequencedata_mean_init_log_factor = mean_init;
o.sequencedata_mean_slope_log_factor = mean_slope;
o.sequencedata_stddev_init_log_factor = stddev_init;
o.sequencedata_stddev_slope_log_factor = stddev_slope;
t0 = 0; // EARLIEST_IA_DELAY
t1 = ModelUtil.LATEST_IA_DELAY;
y0low = getDependency().value(NodeIDString.Q1_9_4, ScalarSubIDString.yearPercentileID(t0, klow ))*Math.log(10);
y0high = getDependency().value(NodeIDString.Q1_9_4, ScalarSubIDString.yearPercentileID(t0, khigh))*Math.log(10);
y1low = getDependency().value(NodeIDString.Q1_9_4, ScalarSubIDString.yearPercentileID(t1, klow ))*Math.log(10);
y1high = getDependency().value(NodeIDString.Q1_9_4, ScalarSubIDString.yearPercentileID(t1, khigh))*Math.log(10);
mean_init = MathUtil.linterp(zlow, zhigh, y0low, y0high, 0);
stddev_init = (y0high-y0low)/(zhigh-zlow);
mean_final = MathUtil.linterp(zlow, zhigh, y1low, y1high, 0);
stddev_final = (y1high-y1low)/(zhigh-zlow);
mean_slope = (mean_final-mean_init)/(t1-t0);
stddev_slope = (stddev_final-stddev_init)/(t1-t0);
o.population_mean_init_log_rate = mean_init - Math.log(ModelUtil.UNENHANCED_RESEARCHERS);
o.population_mean_slope_log_rate = mean_slope - Math.log(ModelUtil.UNENHANCED_RESEARCHERS);
o.population_stddev_init_log_rate = stddev_init - Math.log(ModelUtil.UNENHANCED_RESEARCHERS);
o.population_stddev_slope_log_rate = stddev_slope - Math.log(ModelUtil.UNENHANCED_RESEARCHERS);
t0 = ModelUtil.EARLIEST_YEAR;
t1 = ModelUtil.LATEST_YEAR;
y0low = 0;
y0high = 0;
y1low = getDependency().value(NodeIDString.Q1_9_5, ScalarSubIDString.yearPercentileID(t1, klow ))*Math.log(10);
y1high = getDependency().value(NodeIDString.Q1_9_5, ScalarSubIDString.yearPercentileID(t1, khigh))*Math.log(10);
mean_init = MathUtil.linterp(zlow, zhigh, y0low, y0high, 0);
stddev_init = (y0high-y0low)/(zhigh-zlow);
mean_final = MathUtil.linterp(zlow, zhigh, y1low, y1high, 0);
stddev_final = (y1high-y1low)/(zhigh-zlow);
mean_slope = (mean_final-mean_init)/(t1-t0);
stddev_slope = (stddev_final-stddev_init)/(t1-t0);
o.institutional_base_year = ModelUtil.EARLIEST_YEAR;
o.institutional_mean_slope_log_factor = mean_slope;
o.institutional_stddev_slope_log_factor = stddev_slope;
return o;
}
}
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