public override object SimulateData(object inputs, int simSeed)
{
var times = inputs as List <DateTime>;
if (times == null)
{
return(null); // inputs should be a list of DateTimes
}
int n = times.Count;
var simulated = new TimeSeries();
var randomSource = new Palf(simSeed);
var stdnormal = new Normal();
stdnormal.RandomSource = randomSource;
double mVar = GetVariance(Parameters);
Vector <double> ss = Vector <double> .Build.Dense(n);
for (int i = 0; i < n; ++i)
{
double variance = GetConditionalSig2(i, simulated, ss, Parameters, mVar);
double simLR = stdnormal.RandomSource.NextDouble() * Math.Sqrt(variance);
simulated.Add(times[i], simLR, false);
}
simulated.Title = "Simulation";
simulated.Description = "Simulation from " + Description;
return(simulated);
}
/// <summary>
/// Run example
/// </summary>
/// <seealso cref="http://en.wikipedia.org/wiki/Random_number_generation">Random number generation</seealso>
/// <seealso cref="http://en.wikipedia.org/wiki/Linear_congruential_generator">Linear congruential generator</seealso>
/// <seealso cref="http://en.wikipedia.org/wiki/Mersenne_twister">Mersenne twister</seealso>
/// <seealso cref="http://en.wikipedia.org/wiki/Lagged_Fibonacci_generator">Lagged Fibonacci generator</seealso>
/// <seealso cref="http://en.wikipedia.org/wiki/Xorshift">Xorshift</seealso>
public void Run()
{
// All RNG classes in MathNet have next counstructors:
// - RNG(int seed, bool threadSafe): initializes a new instance with specific seed value and thread safe property
// - RNG(int seed): iуууnitializes a new instance with specific seed value. Thread safe property is set to Control.ThreadSafeRandomNumberGenerators
// - RNG(bool threadSafe) : initializes a new instance with the seed value set to DateTime.Now.Ticks and specific thread safe property
// - RNG(bool threadSafe) : initializes a new instance with the seed value set to DateTime.Now.Ticks and thread safe property set to Control.ThreadSafeRandomNumberGenerators
// All RNG classes in MathNet have next methods to produce random values:
// - double[] NextDouble(int n): returns an "n"-size array of uniformly distributed random doubles in the interval [0.0,1.0];
// - int Next(): returns a nonnegative random number;
// - int Next(int maxValue): returns a random number less then a specified maximum;
// - int Next(int minValue, int maxValue): returns a random number within a specified range;
// - void NextBytes(byte[] buffer): fills the elements of a specified array of bytes with random numbers;
// All RNG classes in MathNet have next extension methods to produce random values:
// - long NextInt64(): returns a nonnegative random number less than "Int64.MaxValue";
// - int NextFullRangeInt32(): returns a random number of the full Int32 range;
// - long NextFullRangeInt64(): returns a random number of the full Int64 range;
// - decimal NextDecimal(): returns a nonnegative decimal floating point random number less than 1.0;
// 1. Multiplicative congruential generator using a modulus of 2^31-1 and a multiplier of 1132489760
var mcg31M1 = new Mcg31m1(1);
Console.WriteLine(@"1. Generate 10 random double values using Multiplicative congruential generator with a modulus of 2^31-1 and a multiplier of 1132489760");
var randomValues = mcg31M1.NextDouble(10);
for (var i = 0; i < randomValues.Length; i++)
{
Console.Write(randomValues[i].ToString("N") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 2. Multiplicative congruential generator using a modulus of 2^59 and a multiplier of 13^13
var mcg59 = new Mcg59(1);
Console.WriteLine(@"2. Generate 10 random integer values using Multiplicative congruential generator with a modulus of 2^59 and a multiplier of 13^13");
for (var i = 0; i < 10; i++)
{
Console.Write(mcg59.Next() + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 3. Random number generator using Mersenne Twister 19937 algorithm
var mersenneTwister = new MersenneTwister(1);
Console.WriteLine(@"3. Generate 10 random integer values less then 100 using Mersenne Twister 19937 algorithm");
for (var i = 0; i < 10; i++)
{
Console.Write(mersenneTwister.Next(100) + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Multiple recursive generator with 2 components of order 3
var mrg32K3A = new Mrg32k3a(1);
Console.WriteLine(@"4. Generate 10 random integer values in range [50;100] using multiple recursive generator with 2 components of order 3");
for (var i = 0; i < 10; i++)
{
Console.Write(mrg32K3A.Next(50, 100) + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 5. Parallel Additive Lagged Fibonacci pseudo-random number generator
var palf = new Palf(1);
Console.WriteLine(@"5. Generate 10 random bytes using Parallel Additive Lagged Fibonacci pseudo-random number generator");
var bytes = new byte[10];
palf.NextBytes(bytes);
for (var i = 0; i < bytes.Length; i++)
{
Console.Write(bytes[i] + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 6. A random number generator based on the "System.Security.Cryptography.RandomNumberGenerator" class in the .NET library
var systemCryptoRandomNumberGenerator = new SystemCryptoRandomNumberGenerator();
Console.WriteLine(@"6. Generate 10 random decimal values using RNG based on the 'System.Security.Cryptography.RandomNumberGenerator'");
for (var i = 0; i < 10; i++)
{
Console.Write(systemCryptoRandomNumberGenerator.NextDecimal().ToString("N") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 7. Wichmann-Hill’s 1982 combined multiplicative congruential generator
var rngWh1982 = new WH1982();
Console.WriteLine(@"7. Generate 10 random full Int32 range values using Wichmann-Hill’s 1982 combined multiplicative congruential generator");
for (var i = 0; i < 10; i++)
{
Console.Write(rngWh1982.NextFullRangeInt32() + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 8. Wichmann-Hill’s 2006 combined multiplicative congruential generator.
var rngWh2006 = new WH2006();
Console.WriteLine(@"8. Generate 10 random full Int64 range values using Wichmann-Hill’s 2006 combined multiplicative congruential generator");
for (var i = 0; i < 10; i++)
{
Console.Write(rngWh2006.NextFullRangeInt32() + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 9. Multiply-with-carry Xorshift pseudo random number generator
var xorshift = new Xorshift();
Console.WriteLine(@"9. Generate 10 random nonnegative values less than Int64.MaxValue using Multiply-with-carry Xorshift pseudo random number generator");
for (var i = 0; i < 10; i++)
{
Console.Write(xorshift.NextInt64() + @" ");
}
Console.WriteLine();
}
public void StaticSamplesConsistent()
{
Assert.That(Palf.Doubles(1000, 1), Is.EqualTo(new Palf(1).NextDoubles(1000)).Within(1e-12).AsCollection);
}
public override object SimulateData(object inputs, int randomSeed)
{
var times = inputs as List <DateTime>;
if (times == null)
{
return(null); // inputs should be a list of DateTimes
}
// Simulation here uses the Durbin-Levinson recursions to simulate from
// successive one-step predictive d-ns.
// (This works for long-memory processes as well, unlike the obvious constructive approach for ARMA models.)
int nn = times.Count;
Vector <double> acf = ComputeACF(nn + 1, false);
var nu = Vector <double> .Build.Dense(nn);
var olda = Vector <double> .Build.Dense(nn);
var a = Vector <double> .Build.Dense(nn);
var simd = Vector <double> .Build.Dense(nn);
var rs = new Palf(randomSeed);
var sd = new Normal();//new StandardDistribution(rs);
sd.RandomSource = rs;
nu[0] = acf[0]; // nu(0) = 1-step pred. variance of X(0)
simd[0] = sd.RandomSource.NextDouble() * Math.Sqrt(nu[0]);
for (int t = 1; t < nn; ++t)
{
for (int j = 0; j < nn; ++j)
{
olda[j] = a[j];
}
// compute the new a vector
double sum = 0.0;
for (int j = 1; j < t; ++j)
{
sum += olda[j - 1] * acf[t - j];
}
a[t - 1] = 1 / nu[t - 1] * (acf[t] - sum);
for (int j = 0; j < t - 1; ++j)
{
a[j] = olda[j] - a[t - 1] * olda[t - 2 - j];
}
// update nu
nu[t] = nu[t - 1] * (1 - a[t - 1] * a[t - 1]);
// compute xhat
sum = 0.0;
for (int j = 0; j < t; ++j)
{
sum += a[j] * simd[t - 1 - j];
}
simd[t] = sd.RandomSource.NextDouble() * Math.Sqrt(nu[t]) + sum;
}
var simulated = new TimeSeries
{
Title = "Simul.",
Description = "Simulation from " + Description
};
for (int i = 0; i < nn; ++i)
{
simulated.Add(times[i], simd[i] + Mu, false);
}
return(simulated);
}
public void ThrowsArgumentExceptionWhenLongLagIsNotGreaterThanShortLag()
{
var random = new Palf(1, true, 10, 10);
}
public void ThrowsArgumentExceptionWhenShortLagIsNonPositive()
{
var random = new Palf(1, true, 0, 10);
}
