public void RandomNormalTestWithStddev()
{
RandomNormal rn = new RandomNormal(2018, 0.0f, 4.2f);
Assert.AreEqual(firstValue * 4.2, rn.NextDouble(), epsilon);
Assert.AreEqual(secondValue * 4.2, rn.NextDouble(), epsilon);
}
public void RandomNormalTestTwoDouble()
{
RandomNormal rn = new RandomNormal(2018);
Assert.AreEqual(firstValue, rn.NextDouble(), epsilon);
Assert.AreEqual(secondValue, rn.NextDouble(), epsilon);
}
public void RandomNormalTestWithMean()
{
RandomNormal rn = new RandomNormal(2018, 5.0f);
Assert.AreEqual(firstValue + 5.0, rn.NextDouble(), epsilon);
Assert.AreEqual(secondValue + 5.0, rn.NextDouble(), epsilon);
}
public void RandomNormalTestWithStddev()
{
var rn = new RandomNormal(2018, 0.0f, 4.2f);
Assert.AreEqual(k_FirstValue * 4.2, rn.NextDouble(), k_Epsilon);
Assert.AreEqual(k_SecondValue * 4.2, rn.NextDouble(), k_Epsilon);
}
public void RandomNormalTestTwoDouble()
{
var rn = new RandomNormal(2018);
Assert.AreEqual(k_FirstValue, rn.NextDouble(), k_Epsilon);
Assert.AreEqual(k_SecondValue, rn.NextDouble(), k_Epsilon);
}
public void RandomNormalTestWithMean()
{
var rn = new RandomNormal(2018, 5.0f);
Assert.AreEqual(k_FirstValue + 5.0, rn.NextDouble(), k_Epsilon);
Assert.AreEqual(k_SecondValue + 5.0, rn.NextDouble(), k_Epsilon);
}
public void RandomNormalTestWithMeanStddev()
{
RandomNormal rn = new RandomNormal(2018, -3.2f, 2.2f);
Assert.AreEqual(-4.2266, rn.NextDouble(), 0.0001);
Assert.AreEqual(-4.0357, rn.NextDouble(), 0.0001);
}
public void RandomNormalTestWithStddev()
{
RandomNormal rn = new RandomNormal(2018, 1.0f, 4.2f);
Assert.AreEqual(-0.9599, rn.NextDouble(), 0.0001);
Assert.AreEqual(-0.5955, rn.NextDouble(), 0.0001);
}
public void RandomNormalTestWithMean()
{
RandomNormal rn = new RandomNormal(2018, 5.0f);
Assert.AreEqual(4.53333, rn.NextDouble(), 0.0001);
Assert.AreEqual(4.6201, rn.NextDouble(), 0.0001);
}
public void RandomNormalTestTwoDouble()
{
RandomNormal rn = new RandomNormal(2018);
Assert.AreEqual(-0.46666, rn.NextDouble(), 0.0001);
Assert.AreEqual(-0.37989, rn.NextDouble(), 0.0001);
}
public void RandomNormalTestWithMeanStddev()
{
float mean = -3.2f;
float stddev = 2.2f;
RandomNormal rn = new RandomNormal(2018, mean, stddev);
Assert.AreEqual(firstValue * stddev + mean, rn.NextDouble(), epsilon);
Assert.AreEqual(secondValue * stddev + mean, rn.NextDouble(), epsilon);
}
public void RandomNormalTestWithMeanStddev()
{
const float mean = -3.2f;
const float stddev = 2.2f;
var rn = new RandomNormal(2018, mean, stddev);
Assert.AreEqual(k_FirstValue * stddev + mean, rn.NextDouble(), k_Epsilon);
Assert.AreEqual(k_SecondValue * stddev + mean, rn.NextDouble(), k_Epsilon);
}
/// <summary>
/// Fill a pre-allocated Tensor with random numbers
/// </summary>
/// <param name="tensorProxy">The pre-allocated Tensor to fill</param>
/// <param name="randomNormal">RandomNormal object used to populate tensor</param>
/// <exception cref="NotImplementedException">
/// Throws when trying to fill a Tensor of type other than float
/// </exception>
/// <exception cref="ArgumentNullException">
/// Throws when the Tensor is not allocated
/// </exception>
public static void FillTensorWithRandomNormal(
TensorProxy tensorProxy, RandomNormal randomNormal)
{
if (tensorProxy.DataType != typeof(float))
{
throw new NotImplementedException("Only float data types are currently supported");
}
if (tensorProxy.data == null)
{
throw new ArgumentNullException();
}
for (var i = 0; i < tensorProxy.data.length; i++)
{
tensorProxy.data[i] = (float)randomNormal.NextDouble();
}
}
public void RandomNormalTestDistribution()
{
float mean = -3.2f;
float stddev = 2.2f;
RandomNormal rn = new RandomNormal(2018, mean, stddev);
int numSamples = 100000;
// Adapted from https://www.johndcook.com/blog/standard_deviation/
// Computes stddev and mean without losing precision
double oldM = 0.0, newM = 0.0, oldS = 0.0, newS = 0.0;
for (int i = 0; i < numSamples; i++)
{
double x = rn.NextDouble();
if (i == 0)
{
oldM = newM = x;
oldS = 0.0;
}
else
{
newM = oldM + (x - oldM) / i;
newS = oldS + (x - oldM) * (x - newM);
// set up for next iteration
oldM = newM;
oldS = newS;
}
}
double sampleMean = newM;
double sampleVariance = newS / (numSamples - 1);
double sampleStddev = Math.Sqrt(sampleVariance);
// Note a larger epsilon here. We could get closer to the true values with more samples.
Assert.AreEqual(mean, sampleMean, 0.01);
Assert.AreEqual(stddev, sampleStddev, 0.01);
}
public static Func <float> CreateGaussianSampler(float mean, float stddev, int seed)
{
RandomNormal distr = new RandomNormal(seed, mean, stddev);
return(() => (float)distr.NextDouble());
}
