public void TestGaussianReset()
{
var strategy = ResetWeightMutationStrategy <double> .CreateGaussianResetStrategy(
new SelectAllStrategy(),
1.0, RandomDefaults.CreateRandomSource(0));
int iters = 100000;
double[] weightArr = new double[iters];
for (int i = 0; i < iters; i++)
{
weightArr[i] = 123.0;
}
strategy.Invoke(weightArr);
// Construct a histogram on the array of weights.
HistogramData hist = NumericsUtils.BuildHistogramData(weightArr, 8);
// We expect min and max to be close to be about -4.5 and +4.5 respectively
// (but they could be higher in magnitude, with no bound).
Assert.IsTrue(hist.Max >= 3.8);
Assert.IsTrue(hist.Min <= -3.8);
TestMean(weightArr);
TestStandardDeviation(weightArr);
}
public void UniformReset()
{
double weightScale = 5.0;
var strategy = ResetWeightMutationStrategy <double> .CreateUniformResetStrategy(
new SelectAllStrategy(),
weightScale);
IRandomSource rng = RandomDefaults.CreateRandomSource(0);
int iters = 10_000;
double[] weightArr = new double[iters];
for (int i = 0; i < iters; i++)
{
weightArr[i] = 123.0;
}
strategy.Invoke(weightArr, rng);
// Construct a histogram on the array of weights.
HistogramData hist = NumericsUtils.BuildHistogramData(weightArr, 8);
// We expect samples to be approximately evenly distributed over the histogram buckets.
for (int i = 0; i < hist.FrequencyArray.Length; i++)
{
Assert.True(hist.FrequencyArray[i] > (iters / 8) * 0.8);
}
// We expect min and max to be close to -weightScale and +weightScale respectively.
double delta = weightScale - hist.Max;
Assert.True(delta >= 0.0 && delta < 0.1);
delta = weightScale + hist.Min;
Assert.True(delta >= 0.0 && delta < 0.1);
// Mean should be near to zero.
TestMean(weightArr);
}
