public RandomSearch(TrainingSamplesGenerator gen, int maxDepth, NodeTypeFrequencyProfile profile, int batchSize = 128)
: base(gen)
{
this.profile = profile;
this.maxDepth = maxDepth;
this.batchSize = batchSize;
}
public RandomMutationSearch(TrainingSamplesGenerator generator, int mutationsInEachStep, NodeTypeFrequencyProfile profile)
: base(generator)
{
this.mutationsInStep = mutationsInEachStep;
this.profile = profile;
this.mutator = new PointMutation(profile, 3);
this.mutatedPrograms = new List <EvaluatedEntity <SolutionProgram> >();
}
/// <summary>
/// Evaluates given solution program using given samplesGenerator. Prints a comparison of desiredOutputs to realOutputs and computes success rate.
/// </summary>
/// <param name="generator"></param>
/// <param name="p"></param>
/// <param name="samplesCount"></param>
public static void runEvaluation(TrainingSamplesGenerator generator, SolutionProgram p, int samplesCount, bool quiet = false)
{
Interpret i = new Interpret();
int currectCount = 0;
foreach (var sample in generator.generateSamples(samplesCount))
{
p.evaluate(sample, i);
Printing.PrintMsg(sample.ToString() + "\treal output: " + sample.realOutputs2String + " " + (sample.isCorrectlyAnswered ? "OK" : "WRONG"), quiet);
if (sample.isCorrectlyAnswered)
{
currectCount++;
}
}
Console.WriteLine($"Success rate: {currectCount} out of {samplesCount} ({((double)(currectCount*100) / samplesCount).ToString("0.##")}%)");
}
protected SearchAlgorithm(TrainingSamplesGenerator generator)
{
this.watch = new Stopwatch();
this.generator = generator;
this.i = new Interpret();
}
public FullEnumerationSearch(TrainingSamplesGenerator generator, int batchSize = 128)
: base(generator)
{
this.batchSize = batchSize;
}
