public void TestGaussianDelta()
{
var strategy = DeltaWeightMutationStrategy.CreateGaussianDeltaStrategy(
new SelectAllStrategy(),
1.0);
IRandomSource rng = RandomDefaults.CreateRandomSource(0);
int iters = 100_000;
double[] weightArr = new double[iters];
for (int i = 0; i < iters; i++)
{
weightArr[i] = 1000.0;
}
strategy.Invoke(weightArr, rng);
// 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 -995.5 and +1004.5 respectively
// (but they could be further from the mean of 1000, with no bound).
Assert.IsTrue(hist.Max >= 1002.0);
Assert.IsTrue(hist.Min <= 998.0);
TestMean(weightArr, 1000.0);
}
public void TestUniformDelta()
{
double weightScale = 5.0;
var strategy = DeltaWeightMutationStrategy.CreateUniformDeltaStrategy(
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] = 1000.0;
}
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.IsTrue(hist.FrequencyArray[i] > (iters / 8) * 0.8);
}
// We expect min and max to be close to 1000-weightScale and 1000+weightScale respectively.
Assert.IsTrue(hist.Max <= (1000 + weightScale) && hist.Max > (1000 + weightScale) - 0.1);
Assert.IsTrue(hist.Min >= (1000 - weightScale) && hist.Min < (1000 - weightScale) + 0.1);
}
public void GaussianReset()
{
var strategy = ResetWeightMutationStrategy <double> .CreateGaussianResetStrategy(
new SelectAllStrategy(),
1.0);
IRandomSource rng = RandomDefaults.CreateRandomSource(0);
int iters = 100_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 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.True(hist.Max >= 3.8);
Assert.True(hist.Min <= -3.8);
TestMean(weightArr);
TestStandardDeviation(weightArr);
}
public void BuildHistogramData()
{
IRandomSource rng = RandomDefaults.CreateRandomSource(0);
int iters = 10_000;
double[] vals = new double[iters];
for (int i = 0; i < iters; i++)
{
vals[i] = 1000.0 + (rng.NextDouble() * 2.0) - 1.0;
}
// Construct a histogram on the array of values.
HistogramData hist = NumericsUtils.BuildHistogramData(vals, 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 999 and 1001 respectively.
Assert.True(hist.Max <= (1001) && hist.Max > (1001) - 0.1);
Assert.True(hist.Min >= (999) && hist.Min < (999) + 0.1);
}
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);
}