/// <summary>
/// Genetic mutation for auxiliary argument data.
/// </summary>
public void MutateAuxArgs(double[] auxArgs, IRandomSource rng, ZigguratGaussianDistribution gaussianSampler, double connectionWeightRange)
{
// Mutate centre.
// Add Gaussian distribution sample and clamp result to +-connectionWeightRange.
double tmp = auxArgs[0] + gaussianSampler.Sample(0, _auxArgsMutationSigmaCenter);
if (tmp < -connectionWeightRange)
{
auxArgs[0] = -connectionWeightRange;
}
else if (tmp > connectionWeightRange)
{
auxArgs[0] = connectionWeightRange;
}
else
{
auxArgs[0] = tmp;
}
// Mutate radius.
// Add Gaussian distribution sample and clamp result to [0,1]
tmp = auxArgs[1] + gaussianSampler.Sample(0, _auxArgsMutationSigmaRadius);
if (tmp < 0.0)
{
auxArgs[1] = 0.0;
}
else if (tmp > 1.0)
{
auxArgs[1] = 1.0;
}
else
{
auxArgs[1] = tmp;
}
}
public void TestCumulativeDistribution()
{
// Standard normal.
var dist = new ZigguratGaussianDistribution(0.0, 1.0);
GaussianDistributionTestUtils.TestDistribution(dist, 0.0, 1.0);
// Non-zero mean tests.
dist = new ZigguratGaussianDistribution(10.0, 1.0);
GaussianDistributionTestUtils.TestDistribution(dist, 10.0, 1.0);
dist = new ZigguratGaussianDistribution(-100.0, 1.0);
GaussianDistributionTestUtils.TestDistribution(dist, -100.0, 1.0);
// Non-1.0 standard deviations
dist = new ZigguratGaussianDistribution(0.0, 0.2);
GaussianDistributionTestUtils.TestDistribution(dist, 0.0, 0.2);
dist = new ZigguratGaussianDistribution(0.0, 5.0);
GaussianDistributionTestUtils.TestDistribution(dist, 0.0, 5.0);
// Non-zero mean and non-1.0 standard deviation.
dist = new ZigguratGaussianDistribution(10.0, 2.0);
GaussianDistributionTestUtils.TestDistribution(dist, 10.0, 2.0);
dist = new ZigguratGaussianDistribution(-10.0, 3.0);
GaussianDistributionTestUtils.TestDistribution(dist, -10.0, 3.0);
}
public FunctionFloatBenchmarks()
{
// Create some random Gaussian values as the inputs to the activation functions.
ZigguratGaussianDistribution gaussian = new ZigguratGaussianDistribution(0);
for(int i=0; i<_x.Length; i++) {
_x[i] = (float)gaussian.Sample(0, 2.0);
}
}
static NNFunctions()
{
using (RNGCryptoServiceProvider provider = new RNGCryptoServiceProvider())
{
DateTime now = DateTime.Now;
ZIGGURAT = new ZigguratGaussianDistribution(provider.GetHashCode() ^ now.TimeOfDay.Milliseconds ^ (int)now.TimeOfDay.TotalMilliseconds, 0, 0.01);
}
}
public BenchmarksVectorizedDouble()
{
// Create some random Gaussian values as the inputs to the activation functions.
var gaussian = new ZigguratGaussianDistribution(0);
for (int i = 0; i < _x.Length; i++)
{
_x[i] = gaussian.Sample(0, 2.0);
}
}
public void MutateAuxArgs(double[] auxArgs, IRandomSource rng, ZigguratGaussianDistribution gaussianSampler, double connectionWeightRange)
{
throw new SharpNeatException("MutateAuxArgs() called on activation function that does not use auxiliary arguments.");
}
public void TestSimpleStats()
{
var dist = new ZigguratGaussianDistribution();
GaussianDistributionTestUtils.TestSimpleStats(dist);
}
public void TestStandardDeviation()
{
var dist = new ZigguratGaussianDistribution();
GaussianDistributionTestUtils.TestStandardDeviation(dist);
}
public void TestMean()
{
var dist = new ZigguratGaussianDistribution();
GaussianDistributionTestUtils.TestMean(dist);
}
