public override void Describe(
IEvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log)
{
if (_ca == null)
{
_ca = new CA(CA_WIDTH, NEIGHBORHOOD);
}
int[] trial = new int[CA_WIDTH];
MajorityData input = (MajorityData)Input;
// extract the rule
((GPIndividual)ind).Trees[0].Child.Eval(
state, threadnum, input, Stack, (GPIndividual)ind, this);
int[] rule = _ca.GetRule();
for (int i = 0; i < 64; i++)
{
rule[i] = (int)((input.Data0 >> i) & 0x1);
}
for (int i = 64; i < 128; i++)
{
rule[i] = (int)((input.Data1 >> (i - 64)) & 0x1);
}
_ca.SetRule(rule); // for good measure though it doesn't matter
// print rule
var s = "Rule: ";
foreach (int r in rule)
{
s += r;
}
state.Output.PrintLn(s, log);
double sum = 0;
for (var i = 0; i < NUM_TESTS; i++)
{
// set up and run the CA
int result = MakeTrial(state, threadnum, trial, RANDOM) ? 1 : 0;
_ca.SetVals(trial);
_ca.Step(STEPS, true);
// extract the fitness
if (All(_ca.GetVals(), result))
{
sum++;
}
}
_density = (sum / NUM_TESTS);
state.Output.PrintLn("Generalization Accuracy: " + _density, 1); // stderr
state.Output.PrintLn("Generalization Accuracy: " + _density, log);
}
public override void Describe(
IEvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log)
{
if (_ca == null)
{
_ca = new CA(CA_WIDTH, NEIGHBORHOOD);
}
int[] trial = new int[CA_WIDTH];
bool[] genome = ((BitVectorIndividual)ind).genome;
// extract the rule
int[] rule = _ca.GetRule();
for (int i = 0; i < 128; i++)
{
rule[i] = (genome[i] ? 1 : 0);
}
_ca.SetRule(rule); // for good measure though it doesn't matter
double sum = 0;
for (int i = 0; i < NUM_TESTS; i++)
{
// set up and run the CA
int result = MakeTrial(state, threadnum, trial, RANDOM) ? 1 : 0;
_ca.SetVals(trial);
_ca.Step(STEPS, true);
// extract the fitness
if (All(_ca.GetVals(), result))
{
sum++;
}
}
_density = sum / NUM_TESTS;
if (state.Output == null) // can happen if we call from main() below
{
Console.Error.WriteLine("Generalization Accuracy: " + _density);
}
else
{
state.Output.PrintLn("Generalization Accuracy: " + _density, 1); // stderr
state.Output.PrintLn("Generalization Accuracy: " + _density, log);
}
}
public void Evaluate(IEvolutionState state,
Individual ind,
int subpopulation,
int threadnum)
{
if (_ca == null)
{
_ca = new CA(CA_WIDTH, NEIGHBORHOOD);
}
// we always reevaluate
//if (!ind.evaluated) // don't bother reevaluating
{
MajorityData input = (MajorityData)(this.Input);
int sum = 0;
// extract the rule
((GPIndividual)ind).Trees[0].Child.Eval(
state, threadnum, input, Stack, (GPIndividual)ind, this);
int[] rule = _ca.GetRule();
for (int i = 0; i < 64; i++)
{
rule[i] = (int)(((input.Data0) >> i) & 0x1);
}
for (int i = 64; i < 128; i++)
{
rule[i] = (int)(((input.Data1) >> (i - 64)) & 0x1);
}
_ca.SetRule(rule); // for good measure though it doesn't matter
for (int i = 0; i < NUM_TRIALS; i++)
{
// set up and run the CA
_ca.SetVals(_trials[i]);
_ca.Step(STEPS, true);
// extract the fitness
if (All(_ca.GetVals(), _majorities[i]))
{
sum++;
}
}
SimpleFitness f = (SimpleFitness)ind.Fitness;
f.SetFitness(state, sum / (double)NUM_TRIALS, sum == NUM_TRIALS);
ind.Evaluated = true;
}
}
public void Evaluate(IEvolutionState state,
Individual ind,
int subpopulation,
int threadnum)
{
if (_ca == null)
{
_ca = new CA(CA_WIDTH, NEIGHBORHOOD);
}
// we always reevaluate
//if (!ind.evaluated) // don't bother reevaluating
{
int sum = 0;
bool[] genome = ((BitVectorIndividual)ind).genome;
// extract the rule
int[] rule = _ca.GetRule();
for (int i = 0; i < 128; i++)
{
rule[i] = (genome[i] ? 1 : 0);
}
_ca.SetRule(rule); // for good measure though it doesn't matter
for (int i = 0; i < NUM_TRIALS; i++)
{
// set up and run the CA
_ca.SetVals(_trials[i]);
_ca.Step(STEPS, true);
// extract the fitness
if (All(_ca.GetVals(), _majorities[i]))
{
sum++;
}
}
SimpleFitness f = (SimpleFitness)ind.Fitness;
f.SetFitness(state, sum / (double)NUM_TRIALS, false);
ind.Evaluated = true;
}
}
