public void TestMeanAndVariacneConsistency()
{
const int numSamples = 100000;
double mean, stdev;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
Uniform uniform = new Uniform();
rs.Clear();
var a = Convert.ToDouble(uniform.UpperBound);
var b = Convert.ToDouble(uniform.LowerBound);
mean = (a + b) / 2; stdev = Math.Sqrt(0.25);
for (int i = 0; i < numSamples; ++i)
{
rs.Push(uniform.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("uniform", mean, stdev * stdev, rs.Mean(), rs.Variance());
Assert.IsTrue(Math.Abs(mean - rs.Mean()) < 0.1);
Assert.IsTrue(Math.Abs(stdev * stdev - rs.Variance()) < 0.1);
}
public void TestMeanAndVariacneConsistency_Mean()
{
const int numSamples = 100000;
double mean, variance;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
Beta beta = new Beta();
rs.Clear();
//double a = 2, b = 70;
//mean = a / (a + b);
//variance = mean * (1 - mean) / (a + b + 1);
mean = 0.1; variance = 0.1 * 0.1;
for (int i = 0; i < numSamples; ++i)
{
beta.Mean = mean;
beta.StandardDeviation = Math.Sqrt(variance);
rs.Push(beta.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("Beta", mean, variance, rs.Mean(), rs.Variance());
}
public void TestMeanAndVariacneConsistency()
{
const int numSamples = 100000;
double mean, stdev;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
Uniform uniform = new Uniform();
rs.Clear();
var a = uniform.UpperBound;
var b = uniform.LowerBound;
mean = (a + b) / 2; stdev = Math.Sqrt((b - a) * (b - a) / 12);
for (int i = 0; i < numSamples; ++i)
{
rs.Push(uniform.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("uniform", mean, stdev * stdev, rs.Mean(), rs.Variance());
}
public void TestMeanAndVariacneConsistency()
{
List <int> numList = new List <int>()
{
1, 2, 3, 4, 5, 6, 7, 8, 9
};
const int numSamples = 100000;
double mean, stdev;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
Uniform <int> uniform = new Uniform <int>();
uniform.Candidates = numList;
rs.Clear();
mean = 50; stdev = 0;
for (int i = 0; i < numSamples; ++i)
{
rs.Push(uniform.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("uniform categorical", mean, stdev * stdev, rs.Mean(), rs.Variance());
}
public void TestMeanAndVariacneConsistency()
{
const int numSamples = 100000;
double mean, stdev;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
Triangular tri = new Triangular();
rs.Clear();
var a = tri.LowerBound; var b = tri.UpperBound; var c = tri.Mode;
mean = (a + b + c) / 3; stdev = Math.Sqrt((a * a + b * b + c * c - a * b - a * c - b * c) / 18);
for (int i = 0; i < numSamples; ++i)
{
rs.Push(tri.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("Triangular", mean, stdev * stdev, rs.Mean(), rs.Variance());
}
public void TestMeanAndVariacneConsistency_MuSigma()
{
const int numSamples = 100000;
double mean, stdev;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
LogNormal logNormal = new LogNormal();
rs.Clear();
mean = 2; stdev = 5;
var muTemp = Math.Log(mean) - 0.5 * Math.Log(1 + stdev * stdev / mean / mean);
var sigmaTemp = Math.Sqrt(Math.Log(1 + stdev * stdev / mean / mean));
for (int i = 0; i < numSamples; ++i)
{
logNormal.Mu = muTemp;
logNormal.Sigma = sigmaTemp;
rs.Push(logNormal.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("logNormal", mean, stdev * stdev, rs.Mean(), rs.Variance());
}
public void TestMeanAndVariacneConsistency()
{
const int numSamples = 100000;
double mean, stdev;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
LogNormal logNormal = new LogNormal();
rs.Clear();
mean = 2; stdev = 5;
for (int i = 0; i < numSamples; ++i)
{
logNormal.Mean = mean;
logNormal.StandardDeviation = stdev;
rs.Push(logNormal.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("logNormal", mean, stdev * stdev, rs.Mean(), rs.Variance());
}
public void TestMeanAndVariacneConsistency_Shape()
{
const int numSamples = 100000;
double mean, stdev;
double alpha, beta;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
Gamma gamma = new Gamma();
rs.Clear();
alpha = 4; beta = 4;
mean = alpha / beta; stdev = Math.Sqrt(alpha / beta / beta);
for (int i = 0; i < numSamples; ++i)
{
gamma.Mean = mean;
gamma.StandardDeviation = stdev;
rs.Push(gamma.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("gamma", mean, stdev * stdev, rs.Mean(), rs.Variance()); // TODO: result not consistent need to fix the bug
}
public void TestMeanAndVariacneConsistency_CV()
{
const int numSamples = 100000;
double mean, stdev, cv;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
Normal normal = new Normal();
rs.Clear();
mean = 2; stdev = 5;
cv = stdev / Math.Abs(mean);
for (int i = 0; i < numSamples; ++i)
{
normal.Mean = mean;
normal.CV = cv;
rs.Push(normal.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("normal", mean, stdev * stdev, rs.Mean(), rs.Variance());
}
public double Slope()
{
double S_xx = x_stats.Variance() * (n - 1.0);
return(S_xy / S_xx);
}
public void TestMeanAndVariacneConsistency()
{
const int numSamples = 100000;
double mean, stdev;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
Poisson poisson = new Poisson();
rs.Clear();
mean = 2000; stdev = Math.Sqrt(2000);
poisson.Lambda = 2000;
for (int i = 0; i < numSamples; ++i)
{
rs.Push(poisson.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("Poisson Discrete", mean, stdev * stdev, rs.Mean(), rs.Variance());
}